student perceptions of their biology teacher's - repositories

STUDENT PERCEPTIONS OF THEIR BIOLOGY TEACHER'S INTERPERSONAL

TEACHING BEHAVIORS AND STUDENT ACHIEVEMENT

AND AFFECTIVE LEARNING OUTCOMES

by

WADE CLAY SMITH, JR., B.S.Ed., M.Ed.

A DISSERTATION

IN

CURRICULUM AND INSTRUCTION

Submitted to the Graduate Faculty of Texas Tech University in

Partial Fulfillment of the Requirements for

the Degree of

DOCTOR OF EDUCATION

Approved

Accepted

August, 1998

ACKNOWLEDGMENTS

The assistance Drs. Skoog and Lan have been instrumental in the

research, development and completion of this dissertation. Dr. Skoog was

always there to help me develop a clearer understanding of the concepts central

to my dissertation. In the best Socratic tradition he helped me sharpen my

thought processes. Dr. Lan's assistance was critical to me. Without his help, I

would not have developed a good theory based understanding of quantitative

methodologies.

I also want to acknowledge the critical discussions I had with Ms. Chih-

Ling Hseih, and Dr. Chi-Chau Lin, fellow doctoral students. Each of these fine

friends acted as sounding boards to help me develop a dearer understanding of

the central ideas of my dissertation and how I should present them at public

meetings. I also want to thank Dr. Jung who as helped me broaden my

understanding of what it means to be a professional educator. Lastly, but by no

means last, I am deeply indebted to my wife, Esther Smith. Without her support

and love I would never of been able to complete my doctoral program.

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ii

ABSTRACT vii

LIST OF TABLES ix

CHAPTER

I. THE PROBLEM 1

Introduction and Background of the Study 1

Theoretical Framework: Interpersonal Teaching Behaviors

Theory 5

Statement of the Problem 7

Delimitations of the Study 7

Limitations of the Study 8

Purpose of the Study 8

Hypotheses 9

Importance of the Study 9

Organization of the Dissertation 10

II. LITERATURE REVIEW 11 Analytical Observations of Teacher Overt Behaviors and Competencies: Styles, Skills and Techniques and Effective Teaching 11

Direct versus Indirect Teaching Styles 15

Deployment of Key Teaching Competencies and Teacher Effectiveness 27

Holistic Communication Analysis and Teacher Effectiveness: Interpersonal Teaching Behaviors and Pupil achievement 30

Theoretical Frameworks 30

Leary's Communication Theory 30

Watzlawick, Beavin and Jackson's human communication theory 31

Interpersonal Teacher Behaviors 31

Summary: Interpersonal Teaching Behaviors Theory 42

Bloom's Taxonomy of Educational Objectives 43

RESEARCH DESIGN AND METHODOLOGY 51

Introduction 51

Purpose 52

Hypotheses 53

Methodology 53

Participants 53

Instrumentation 54

Questionnaire on Teacher Interaction (QTI) 54

Biology End-of-Course Examination (Spring 1996 ver.) 57

Biology Student Affective Instrument (BSAI) 60 Procedure 62

Data Analysis 62

iv

IV. RESULTS 65

Hypotheses 66

Descriptive Statistics 66

Assumptions Concerning the use of Multiple Regression 68

Statistical Analysis 70

Summary 86

V. DISCUSSIONS, CONCLUSIONS, AND RECOMMENDATIONS 89

Summary of Study 89

Limitations 95

Implications 95

Influence of Student Perceptions of their Biology Teacher on Student Achievement Levels 95

Influence of Student Perceptions of their Biology Teacher on Student Affective Outcomes 100

Theoretical Contributions 104

Educational Practice 107

Suggestions for Future Research 108

REFERENCES 111

APPENDIX

A: QUESTIONNAIRE ON TEACHER INTERACTION 117

B: BIOLOGY STUDENT AFFECTIVE INSTRUMENT, FACTOR ANALYSIS, AND RELIABILITIES 120

C: BIOLOGY END OF COURSE EXAMINATION 125

D: CORRELATION OF ALL VARIABLES 147

VI

ABSTRACT

The primary goals of this dissertation were to determine the relationships

between interpersonal teaching behaviors and student achievement and affective

learning outcomes. The instrument used to collect student perceptions of teacher

interpersonal teaching behaviors was the Questionnaire on Teacher Interactions

(QTI). The instrument used to assess student affective learning outcomes was

the Biology Student Affective Instrument (BSAI) The interpersonal teaching

behavior data were collected using students as the observers. 111 students in an

urban influenced, rural high school answered the QTI and BSAI in September of

1997 and again in April 1998. At the same time students were pre and post

tested using the Biology End of Course Examination (BECE).

The QTI has been used primarily in European and Oceanic areas. The

instrument was also primarily used in educational stratified environments. This

was the first time the BSAI was used to assess student affective leaming

outcomes. The BECE is a Texas normed cognitive assessment test and it is

used by Texas schools districts as the end of course examination in biology. The

interpersonal teaching behaviors model was tested to ascertain if it was

predictive of student achievement and affective learning outcomes in a Texan

non-stratified educational environment. Findings indicate that the QTI is not an

adequate predictor of student achievement in biology. Nor is an adequate

predictor of student affective learning outcomes In biology. This study's results

were not congruent with the non-USA results, this indicates that the QTI is also a

VII

society/culturally sensitive instrument and the instrument needs to be normed to

a particular society/culture and its predictive power established before It is used

to affect teachers' and students' educational environments.

VIII

LIST OF TABLES

1. Descriptive Statistics of all independent and dependent variables 67

2. Descriptive Statistics of all retained criteria variables for sub-hypothesis 1.1 71

3. ANOVA Test for model/data fit for sub-hypothesis 1.1 71

4. Standardized Beta Coefficient of retained independent variables for Dependent Post-Test, High Level (Sub-hypothesis 1.1) 73



7. Standardized Beta Coefficient of retained independent variables for Dependent Post-Test, Low Level (Sub-hypothesis 1.2) 76



10. Standardized Beta Coefficient of retained independent variables for Dependent Post-Test, Overall Level (Sub-hypothesis 1.3) 79

11. Descriptive Statistics of all retained criteria variables for Sub-hypothesis 2.1 (Factor 1) 80

12. ANOVA Test for model/data fit for sub-hypothesis 2.1 (Factor 1) 81

13. Standardized Beta Coefficient of retained independent variables for BSAI, Factor 1 (Sub-hypothesis 2.1) 82

14. Descriptive Statistics of all retained criteria variables for Sub-hypothesis 2.2 (Factor 2) 83

15. ANOVA Test for model/data fit for sub-hypothesis 2.2 (Factor 2) 84

IX

16. Standardized Beta Coefficient of retained independent variables for BSAI, Factor 2 (Sub-hypothesis 2.2) 86

17. Resultant Factors firom Principal Component Analysis Factor Analysis 121

18. Reliability analysis, Cronbach Alpha scale, Factor 1, BSAI (Opinions) 122

19. Reliability analysis, Cronbach Alpha scale. Factor 2, BSAI (Feelings) 123

20. Correlation of all variables 148

CHAPTER I

THE PROBLEM

Introduction and Background of the Study

Since the 1950's, researchers have been attempting to quantify the impact

of teacher characteristics and styles on their pupils' learning outcomes. There

has been a recognized need to understand the basis for effective teaching in the

9-12 grade setting. Effective teaching style is defined by this researcher as those

actions, interactions and communications of the teacher with her/his students

which are associated with positive achievement and/or affective student

outcomes. While the common definition of an effective teacher is moderately

stable, the methods used to operationally define the theoretical construct of

effective teacher styles have not been stable in the literature (see Bennett, 1976;

Brophy, 1973; Flanders, 1960, 1964, 1965, 1970 1970a; Good, Biddle & Brophy

1975; Haige & Schmidt, 1956; Medley, 1977, 1979; Ostlund, 1956; Rosenshine,

1970; Stalling 1976; Soar, 1968; Tuckman, 1970; Veldman & Brophy, 1974;

Wispe, 1951). The varying scales used in the aforementioned studies to assess

the effective teacher construct were bipolar and linear in nature. Since 1985 the

primary method used to operationally define the theoretical construct of effective

teaching styles has focused on the interpersonal teaching behavior model (see

Brekelmans, 1989; Brekelmans, Wubbels, & Creton, 1990; Creton, Wubbels, &

Hooymayers, 1993; Fisher, 1995; Fraser, 1986; Henderson, Fisher, & Fraser,

1995; Wubbels, Brekelmans, Creton, & Hooymayers, 1989; Wubbels,

1

Brekelmans, & Hooymayers, 1991; Wubbels, Creton, & Holvast, 1988; Wubbels,

Creton, & Hooymayers, 1985; Wubbels, Creton & Hooymayers, 1987; Wubbels,

Creton, & Hooymayers, 1992; Wubbels, Creton, Levy, & Hooymayers, 1993;

Wubbels, & Levy, 1989). Two confounding variables that impact on the stability

of interpersonal teaching behaviors are experience and age of the teachers

under study (Brekelmans, Holvast, & van Tartwijk, 1992). This study found that

teacher interepersonal teaching behaviors underwent three major changes

during a teacher's career. These changes occurred during the 5^ year and 15^

year of experience. Teachers generally exhibited more leadership traits and less

uncertainty and strictness traits at the 5* year and they exhibited more strictness

traits at the 15* year of experience. Age was also related to differing

interpersonal teaching behaviors with the more elderiy teacher exhibiting more

leadership and less uncertainty traits (Brekelmans et al., 1992). Brekelmans et

al.'s study is supportive of Veldman and Brophy's (1974) study which found that

Rosenshine's (1970) study was flawed because he did not account for teacher

experience in his study. Teachers change their interpersonal teaching behaviors

as they gain experience and age, and there is evidence that the Questionnaire

on Teacher Interaction is reliable enough to detect changes in teacher's

interpersonal teaching behaviors.

The scales that have been used to attempt to assess teacher

styles/behaviors were labeled Authoritarian-Democratic -Lassez Faire (Anderson,

1959), Progressive-Traditional (Bennett, 1976), Direct versus Open Questions

(Rosenshine, 1976), Positive reinforcement model versus Open structure model

(Stalling, 1976), Directive-Permissive (Haige & Schmidt, 1956), and Direct-

Indirect (Flanders, 1960, 1964, 1965, 1970a, 1970b; Soar, 1968; Tuckman, 1970,

1976). These prior studies used differing operational definitions of teacher

style/behavior and their results were mixed and contradictory (see Bennett, 1976;

Soar, 1968; Stallings, 1976). The interpersonal teaching behavior theory will be

used to bring clarity to the theoretical construct of teacher effectiveness/styles

and the associated research field. To assist in the development of clarity, this

study will use the interpersonal teaching behaviors model perspective to evaluate

the seminal teacher effectiveness literature and bring partial coherence to this

literature.

The current interpersonal teaching behaviors research is focused on

correlation studies between teachers' interpersonal teaching behaviors and

students cognitive and affective outcomes (Brekelmans, 1990; Fisher, 1995;

Henderson, Fisher & Fraser, 1995; Tuckman, 1980; Wubbels, Brekelmans,

Creton & Hooymayers, 1989; Wubbels, Brekelmans & Hooymayers, 1991;

Wubbels, Creton & Holvast, 1988; Wubbels, Creton & Hooymayers, 1985, 1987;

Wubbels, Creton, Levy & Hooymayers, 1993; Wubbels & Levy 1989). Using the

interpersonal teaching behaviors model, the teacher is viewed as an integrated

whole educator interacting with the students in his/her classes. Student

perceptions of their teacher's interpersonal teaching behaviors are instrumental

in effecting student cognitive and affective learning outcomes.

The eariier investigations were identifiable by a number of key correlation

studies with the central question of, "is there a correlation or association between

certain teacher behaviors and students' cognitive or affective learning outcomes

or both?" The Flanders (1960, 1964, 1965, 1970, 1970a), Soar (1968), Stalling

(1976), and Tuckman (1970) models proposed a positive correlation between

indirect teaching styles and cognitive achievement. The Bennett (1976) model

proposed a positive correlation between traditional teaching styles and cognitive

achievement at the lower levels of complexity (see also Brophy, 1973; Good,

Biddle & Brophy 1975; Haige & Schmidt, 1956; Medley. 1977, 1979; Ostlund,

1956; Rosenshine, 1970; Veldman & Brophy, 1974; Wispe, 1951).

Anderson (1959) in his review of the literature, proposed that the teaching

styles were arranged on a continuum from authoritarian on one end through

democratic to laissaz faire on the other end, and reported positive correlation

between democratic teaching methods and cognitive and affective achievements.

The Soar model (1968) used a continuum with direct and indirect teaching styles

on the extremes and a mixed style in the middle. The Soar model proposed that

the relationship between teaching styles and student outcomes would be

curvilinear for the lower cognitive levels with both direct and indirect styles

correlated with lower cognitive learning outcomes and the mixed teaching style

correlated with higher cognitive learning outcomes. The model also proposed

that the relationship would be linear for the higher cognitive levels. The Bennett

(1976) model was a non-continuous model with various teaching typologies

ranging fi-om completely progressive to completely traditional with other discrete

categories between the extremes. In the Flanders (1960) model, the teaching

styles were determined by a numerical score that originates from a series of

observations by trained observers. The Flanders Interaction Analysis Categories

(FIAC) system describes the teacher as indirect if the indirect score to direct

score ratio was more than one. If it was less than one the teacher was identified

as a direct style teacher.

In the Tuckman (1970) model, the teaching styles were based on student

observations. The scale used in the Tuckman model was continuous from a

score of 1 to 9. With a score of 1 the teacher was identified as completely direct

and a score of 9 as completely indirect.

Research by Flanders (1965), Tuckman (1970) and others which depicted

teacher behaviors on a single continuum led to the study of teacher

competencies, the proper utilization of those competencies and how these two

factors impacted on teacher effectiveness. There was an attempt to develop an

understanding of teacher styles by identifying critical skills/techniques. Teachers

were then assessed by their ability to display the appropriate skills/techniques in

the proper context. Teachers' competence was viewed as collections of skills

and techniques and teaching was perceived as a mechanistic procedure.

Theoretical Framework: Interpersonal Teaching Behaviors Theory

The interpersonal teaching behavior model (Brekelmans, Wubbels &

Creton, 1990; Tuckman, 1976, 1995; Wubbels, Creton, & Hooymayers, 1985;

Wubbels, Creton, Levy, & Hooymayers, 1993) is predicated on the

communication theories of Leary (1957) and Watzlawick, Beavin and Jackson

(1967). Leary's book Interpersonal Diagnosis of Personality was written in the

1950's and became the basis for treatment of various psychological infirmities.

Watzlawick et al. (1967) adapted Leary's theory in their book, Pragmatics of

Human Communication: A Study of Interpersonal Patterns. Pathologies, and

Paradoxes, and this adaptation became and continues to be the basis for family

and group counseling and therapies. In the 1980's Wubbels, Creton and

Hooymayers used Leary's theory as modified by Watzlawick and his colleagues'

human communication theory as the basis for their model on interpersonal

teaching behavior from which they constructed the Questionnaire on Teacher

Interaction (QTI).

In Brekelmans, Wubbels, and Creton (1990), Wubbels, Creton, and

Hooymayers (1985) and Wubbels, Creton, Levy, and Hooymayers (1993) the

researchers depicted the interactional teacher behavior by arranging the

behaviors on a grid with dominance or submission on the 'Y' axis and opposition

or cooperation on the X axis. The 'Y' axis is identified as the "influence axis"

and it is read from the origin to top and bottom. The 'X' axis is labeled as the

"proximity axis" and it is read from the origin to the left and to the right. This

resulted in four quadrants that are further divided into two sections each, for a

total of eight sections. These eight sections which represent distinctive teacher

behaviors, as perceived by students, are then correlated to achievement, in the

cognitive domain, and affective outcomes of the students.

statement of the Problem

The problems investigated in this study were:

1. What are the degrees and directions of the relationship between

student perceptions of their biology teacher's interpersonal teaching behaviors

and student achievement outcomes?

2. What are the degrees and directions of the relationship between


and student affective outcomes?

Delimitations of the Study

The data of this study were collected in an urban-influenced rural school

district in western Texas. The study was specifically limited to the interpersonal

teaching behaviors occurring between students and teachers in biology classes.

The study was further limited in that the data were recorded from the perspective

of the students only. Previous research has shown that students produce data

that are more reliable and valid than teacher self-report data (Wubbels, Creton,

Levy, & Hooymayers, 1993). Other research (Tuckman, 1976) has shown that

the perceptions of students and external observers are highly correlated with a

value near .80.

Limitations of the Study

Several limitations of this study are acknowledged:

1. This study included only those achievement and affective outcomes that

were measurable in group setting with paper and pencil test forms.

2. Only high school freshmen in biology classes in the school year 1997-

1998 from one moderate sized, urban influenced rural school district were

involved in the testing.

3. The generalization of this study to other populations is restricted by the

type of sample (convenience) and it is further restricted to populations which are

demographically congruent to the sample used in this study.

Purpose of the Study

The purpose of the study was to answer the following research questions:

1. What are the directions and degrees of the relationship between


and student overall, higher and lower level cognitive outcomes?

2. What are the directions and degrees of the relationship between


and student affective outcomes?

8

Hypotheses

The following hypotheses were tested In this study:

HOI: None of the eight scales of the QTI are significantly related to

student achievement outcomes.

Sub-hypothesis 1.1: None of the eight scales of the QTI are

significantly related to student higher achievement

outcomes.


significantly related to student lower achievement outcomes.


significantly related to student overall achievement

outcomes.

H02 : None of the eight scales of the QTI are significantly related to

student affective outcomes.


significantly related to student feelings about biology.

Sub-hypothesis 2.2 : None of the eight scales of the QTI are

significantly related to student opinions about biology.

Importance of the Study

This study was designed to determine the directions and degrees of the

relationships between student perceptions of their biology teacher's interpersonal

teaching behaviors and student learning outcomes. Student learning outcomes

were measured in the cognitive and affective domains. The clarification of the

relationships studied In this research endeavor will be helpful in considering how

to improve student outcomes in both domains. As a result of this study, the

relationship between student perceptions of their biology teacher's interpersonal

teaching behaviors as it related to student cognitive and affective outcomes was

determined and this will provide an useful guide to the teacher in the selection of

appropriate interpersonal teaching behaviors to maximize student outcomes.

Organization of the Dissertation

This study is comprised of five chapters. Chapter I provided an

introduction of the problems existing in the teacher effectiveness research area

and the need for developing a clearer understanding of the relationship between

student perceptions of their teacher's interpersonal teaching behaviors and

student achievement and affective learning Chapter II provided a review of the

related literature on the assessment and description of teacher effectiveness and

its relationship to the teacher's interpersonal teaching behaviors. The

relationships between interpersonal teaching behaviors and cognitive and

affective outcomes were also reviewed. Chapter III detailed the research design

methodology selected to address the research problems described in the first two

chapters. Chapter IV presented the findings of this study's data collection and

analysis, and Chapter V presented the conclusions of this study based only on

the data collection and their implications, and suggestions for further research in

the study's area.

10

CHAPTER II

REVIEW OF THE LITERATURE

Analytical Observations of Teacher Overt Behaviors Competencies: Teaching Styles. Skills and

Technigues and Effective Teaching

The literature prior to 1985 was primarily concerned with investigating

teacher styles and its relationship to student outcomes and the identification of

teaching skills and techniques. This information was then used to develop an

understanding of what teaching behaviors were present and the behaviors/styles

impact on student learning outcomes. After 1985 the literature is focused on

interpersonal teaching behaviors and student perceptions of their learning

environments.

The skills and techniques were predicated on the linear teaching models

developed by many researchers (see Bennett, 1976; Burkman, Tate, Snyder, &

Beditz, 1981; Flanders, 1960, 1964, 1965, 1970 1970a; Haige & Schmidt, 1956;

Ostlund, 1956; Stalling 1976; Soar, 1968; Tuckman, 1970; Wispe, 1951). A

plethora of continuum labels were used by these researchers such as;

Progressive-Traditional (Bennett, 1976), Positive reinforcement model versus

Open structure model (Stalling, 1976), Directive-Permissive (Haige & Schmidt,

1956; Ostlund, 1956; Wispe, 1951), Direct-Indirect (Flanders, 1960, 1964, 1965,

1970a, 1970b; Soar, 1968), and Direct-Indirect {from the students' perceptions}

(Tuckman, 1970, 1976, 1995). All of these contlnua were based on a linear bi

polar model of teaching with one attribute on an extreme and its opposite on the

11

other extreme. All of these linear models used the pupils' achievement as one of

the criteria for determining teacher effectiveness, but the models, except

Tuckman, used expert observers. They did not recognize nor use pupil

perceptions of teacher behaviors/styles. Since there is limited agreement

concerning the concept of teacher style/behaviors, this research will discuss the

seminal researchers in the literature through the perspective of interpersonal

teaching behaviors.

Bennett (1976) identified teaching styles on a continuum with traditional on

one pole and progressive on the other pole, with intermediate styles located in

between these poles. Traditional/Progressive was operationally defined by

Bennett by using a continuum with eleven descriptors which were bipolar in

nature.

The eleven descriptors for the traditional pole are; 1) separate subject matter, 2) teacher as distributor of knowledge, 3) passive pupil role, 4) pupils have no say in curriculum planning, 5) accent on memory, practice, and rote, 6) external rewards used, 7) concerned with academic standards, 8) regular testing, 9) accent on competition, 10) teaching confined to classroom base, and 11) little emphasis on creative expression. The eleven descriptors for the progressive pole are: 1) integrated subject matter, 2) teacher as guide to educational experience, 3) active pupil role, 4) pupil participate in curriculum planning, 5) leaming primarily by discovery techniques, 6) intrinsic motivation, 7) not too concerned with conventional academic standards, 8) little testing, 9) accent on cooperative group work, 10) teaching not confined to classroom base, and 11) accent on creative expression. (Bennett, 1976, p. 38)

The traditional descriptors: (1) separate subject matter, (2) teacher as

distributor of knowledge, (3) passive pupil role, and (4) pupils not being involved

in curriculum planning are congruent with the descriptors for the leadership scale

12

on the interpersonal teaching behavior model. The descriptors: (1) accent on

memory, practice, and rote, (2) using external rewards, (3) being concerned with

academic standards, (4) regular testing, (5) accent on competition, (6) teaching

confined to the classroom, and (7) little emphasis on creative expression fall

within the parameters of the strict scale on the interpersonal teaching behavior

model. The progressive descriptors: (1) integrated subject matter. (2) teacher as

guide to educational experience, (3) active pupil role, and (4) pupils are involved

in curriculum planning are similar to the descriptors for the student

responsibility/fi"eedom scale on the interpersonal teaching behavior model. The

descriptors: (1) learning primarily by discovery techniques, (2) Intrinsic

motivation, (3) not too concerned with conventional academic standards, (4) little

testing, (5) accent on cooperative group work, (6) teaching not confined to

classroom base, and (7) accent on creative expression fall within the parameters

of the uncertain and student responsibility/freedom scales on the interpersonal

teaching behavior model. This linear bi-polar continuum is restricted and does

not enable the researcher to assess all the interpersonal teaching behaviors

which are correlated to student achievement, such as admonishing, dissatisfied,

helpful-friendly and understanding behaviors.

In the Bennett (1976), study the students were pretested and post-tested

In the areas of mathematics, reading and English. The tests focused on

assessing students' mastery of materials on the lower levels of Bloom's

taxonomy in the areas of knowledge, comprehension, and application. In the

reading area, Bennett found that the students taught under a traditional teaching

13

style achieved .5 standard deviations above their predicted scores, which were

determined by their pre-test. The mixed or intermediate teaching style was

associated with student outcomes of a 1.0 standard deviation above their

predicted scores. The students taught under the progressive teaching style

under performed by -1.5 standard deviations when compared to their predicted

score.

In the area of mathematics, the students taught under the traditional

teaching style resulted in an achievement of 2.0 standard deviation above their

predicted scores. The students that were taught under the progressive and

mixed achieved at -1.5 and -1.0 standard deviations respectively below their

predicted scores.

In the area of English the results were similar, the traditionally taught

students exceeded their predicted scores by 1.5 standard deviation. Students

taught under progressive and mixed teaching styles under achieved by -1.2 and

.-3 standard deviations below their predicted scores.

Bennett's study also assessed student achievement at the creative

(synthesis) or higher cognitive realm and found no significant differences

between the pupils' achievement scores in the three differing teaching groups.

Bennett (1976) concluded that the traditional teaching style was more effective at

the lower cognitive levels, as the pupils taught under the traditional style teacher

out performed their progressively taught colleagues.

14

Direct versus Indirect Teaching Style

In Rosenshine's (1976) review of the research, he identified the

characteristics of the direct teaching model.

In the direct instruction, the lessons and workbook activities are supervised by the teacher, and there is little free time or unsupervised desk work. The teacher is the dominant leader of the activities, decides what activities will take place, and directs without giving reasons. Teacher questions tend to be narrow, pupils are expected to know rather than to guess the answer, and the teacher immediately reinforces and answers as right or wrong. The learning is organized around questions posed by the teacher or materials provided by the teacher, and it is approached in a direct and business manner. (Rosenshine, 1976, p. 365)

Because direct and indirect teaching are bi-polar, indirect teaching can be

operationally defined as occurring when the lessons and workbook activities are

controlled or directed by the students, and there is a great amount of free time or

unsupervised desk work. The teacher is not the dominant leader of the activities,

s/he does not decide what activities will take place, and directs only after giving

reasons for his/her decision. The teacher's questions tend to be broad, pupils

are expected to try to find the answer, and the teacher does not immediately

reinforce nor does the teacher identify an answer as right or wrong. The learning

is organized around questions posed by the students, and is approached in an

indirect and discovery oriented manner (Rosenshine, 1976).

Wispe (1951) found that there was not an overall difference in student

achievements in the directive and permissive teaching styles classrooms. Wispe

described the directive teacher section as,"... material-centered and highly

structured. The instructor defined the problem areas frequently, he asked many

15

drill-type specific questions, and lectured at long length on course-related

materials"(p. 168). These descriptors fall within the interpersonal teaching

behavior model scales identified as leadership and strict. Wispe also identified

the permissive section as, "...student-centered and activity-centered. The

representative permissive instructor asked many wide-open and reflective-type

questions" (p. 168). These behaviors identified by Wispe fall within the student

responsibility/fi'eedom scale of the interpersonal teacher behavior model. These

types of teaching styles were ftjrther classified as a type of teacher-centered or

student-centered teaching style continuum (Anderson, 1959). The type of

classes utilized in the Wispe study were introductory college courses on social

relations. Wispe analyzed two independent variables, the first administration of

pre-test and the SAT scores were compared to the dependent variable of the

second administration of the pre-test, now the post-test. The finding of the Wispe

study was," When analyzed in this way none of the F ratios were significanf (p.

170). Wispe then divided the students into high and low ability groups and he

reanalyzed the post-test. From this analysis Wispe was able to determine, "that

although teaching methods make no significant difference in the final

examination scores of the brighter students, the scores on the post-test of the

poorer students were significantly raised by directive-type instruction" (p. 170).

Similar findings were found in operational replicates of the Wispe study. The type

of teaching style was not significantly correlated to the students' outcome as it

was measured by achievement (Haige & Schmidt, 1956). Ostlund (1956) found

16

that the lower ability or the lessor prepared student seemed to benefit ft"om direct

teaching.

The instrument Flanders (1960) developed to assess the Direct or Indirect

styles of the observed teacher was the Flanders Interaction Analysis Category

(FIAC). This was one of the first attempts to quantify the operational definition of

the terms direct and indirect teaching styles using expert outside observers.

Flanders asserted that teachers' teaching styles could be arranged on a

continuum with indirect on one end and direct on the other end. He further

contended that teaching style could be operationally defined by a set of

characteristic verbal behaviors and that these behaviors could be manipulated

into a ratio of direct scores to indirect scores. The indirect score is obtained by

counting the frequency of occurrences in categories 1, 2, 3 and 4, the direct

scores are obtained by counting the fi'equency of occurrences in categories 5, 6

and 7 in the FIAC (Flanders, 1960). Flanders (1960) "...assumed that different

types of statement will either increase or decrease the number of alternative

actions available to the student" (1960, p.11). This led to his identification of

indirect teaching styles as the indirect value divided by the direct value and if that

ratio was greater than one, then the teacher was described as using an indirect

teaching style, conversely if the number was under one the teacher was

identified as using a direct teaching style. This led to Flanders identifying direct

and indirect teaching styles as direct and indirect influence. Flanders defined

direct influence as "...a teacher restricts the fi-eedom of action of a student by

setting restraints or focusing his attention on an idea" (1960, p. 12). This

17

definition is similar to the descriptors in the leadership scale of the interpersonal

teacher behavior model. Flanders (1960) defined indirect influence," a teacher

increases the freedom of action of a student by reducing restraints or

encouraging participation" (p. 12). This definition comparable to the descriptors in

the student responsibility/freedom scale of the interpersonal teacher behavior

model. Flanders (1964) used the FIAC in New Zealand and found that the

indirect teaching style was associated with higher student scores in the two types

of classes in the study, 7th grade social studies and 8th grade science classes.

Contrary to Wispe (1951), Flanders' findings supported the conclusion that the

indirect teaching style is more effective than the direct teaching style and that

there is a linear relationship between teaching style and pupil outcomes.

This conclusion was contradicted partially by the findings in the Soar

(1968) study and completely by Bennett's (1976) findings. Soar (1968) found

evidence of a curvilinear relationship between teaching styles and lower cognitive

pupil outcomes, with both extreme indirect and direct teaching styles being

related with lower cognitive outcomes, but the mixed teaching style was related

with higher cognitive outcomes. There was also evidence of a linear relationship

between indirect teaching style and higher level cognition in the students. There

was a modest relationship (exact statistic was not reported by Soar) between

indirect teachers and students with low anxiety levels, for any other type of

student there was not a significant relationship between teaching style and

student achievement (Soar, 1968). Soar utilized Flanders Interaction Analysis

18

instrument to operationally define the terms direct and indirect teaching styles.

According to Soar,

What appears to be clear is that when the objective is the learning of concrete material such as spelling, the multiplication table, or foreign language vocabulary, the teacher should be quite direct and highly structured in his presentation; but when the objective is an abstract one, such as the concept of conservation in children, or new math, or creative writing on older pupils the teacher should be highly indirect. The effective teacher must be able to shift style as he shifts objectives. (1968, p. 279)

Both of these studies used the FIAC as the basis for assessing teacher

verbal behaviors in the classroom. Because the FIAC uses only two scales

(leadership and helpful/fiiendly) of the model of interpersonal teacher (QTI)

behaviors to assess teacher verbal behaviors as ascertained by outside expert

observers, it is inefficient in ascertaining the depth and breath of the

interpersonal behaviors occurring in the classroom as perceived by students.

This could account for the contradictory results between Flanders' (1964) and

Soar's (1968) studies. The lack of agreement between the Bennett (1976) and

Flanders and Soar studies is an expected result since there were differing

operational definitions of teacher behaviors/styles in these studies. This is

another indicator of the need for using the interpersonal teching behaviors model

which is bi-directional to establish a framework for the study of teacher

interpersonal teaching behaviors/styles.

Tuckman (1970) developed an instrument designed to assess the

directness or indirectness of teaching styles. This instrument was a radical

departure fi'om the aforementioned approaches to assessing the teacher's style

19

in that the observers were the teacher's students and not outside professional

observer(s) or outside professional educator(s).

The Tuckman (1970) study consisted of twenty-two eleventh and twelfth

grade teachers from a vocational high school. One-half of the teachers taught

vocational subjects and the other half taught traditional academic courses. All of

the teachers had at least five years teaching experience. This factor eliminated

one of the identified confounding variables of previous research, inexperienced

versus experienced teachers. The reliability of the Students Perception of

Teacher Style (SPOTS) was established by comparing the mean SPOTS score

of each item with the grand mean SPOTS score of each teacher. Each item was

a statement that was concerned with student perceptions of the directness of the

teachers actions. Student perceptions of each item statement was assessed on

a 1-9 Likert scale. Students responded by assigning a score of 1 to 9 for each

item statement that was reflective of their perceptions. All 22 student responses

were added together and that sum was divided by 22. The lowest score possible

was 1 and the highest score possible was 9. Then all student responses were

added together and divided by that classes 'n' number to obtain a class SPOTS

means. A score of one on the SPOTS was reflective of totally indirect teaching

behaviors in the opinion of that teacher's student raters. A score of nine on the

SPOTS was reflective of totally direct teaching behaviors in the opinion of that

teacher's student raters. Twenty-five of the thirty-two items were found to be

highly significantly related and they were retained to form the final version of the

SPOTS instrument. Deviations are rated by their distance from the class mean

20

SPOTS score. The first deviation is the closest individual SPOTS score to the

class SPOTS mean, the second deviation is the second closest individual

SPOTS score to the class SPOTS mean. The inter-rater reliability ranged ft-om

an r of .98 for the first deviation to an r of .69 for the tenth deviation. This

established that the instrument would produce approximately the same result

without regard to the individual student conducting the assessment. This

instrument provided the basis for a new operational definition of the variable

direct and indirect teaching style. The nearer the teachers' scores are to the

value of one, the more indirect the teachers are in their teaching styles.

Conversely, the higher the score, the nearer to the maximum score of 9 a

teacher is the more direct the teaching style.

The major deficiency of the SPOTS scale is that it is a linear model for one

teacher trait, directness or indirectness of teaching style. Tuckman's (1970)

study introduced the instrument, but Tuckman does not relate the teacher's

teaching style to students' academic outcomes. The major accomplishment of

this study was that it validated the usage of student observers instead of expert

outside observers. In doing so this study was foundational in introducing student

perceptions into the educational research arena.

The Rothman (1969) study was an investigation into the preparation of the

teacher in his/her field and its correlation to the students' learning outcomes.

Here the author identified student outcomes by cognitive and affective attributes.

The findings in the cognitive realm of investigation indicated a significant

correlation between the teacher's preparation and the pupils' cognitive outcomes.

21

This relationship was found to be significant without regard to the type of

teaching style utilized by the teacher. The primary importance of the Rothman

study was the finding of the significant overall relationship between teacher

background and students' learning variables. Students' Physics Aptitude Test

(PAT) was significantly correlated to teachers' number of semester hours of

college physics. The students' scores on the Academic Interest Measure,

Physical Science (AIM PS) were significantly correlated with teachers' semester

hours of college level physics classes. Teachers' number of college math

semester hours were significantly correlated with students' scores on the Test On

Understanding Science (TOUS) and the PAT. The teachers' number of math

courses was also positively correlated with the students' AIM PS scores.

Teachers' scores on the Test of Selected Topics in Physics (TSTP) were

negatively correlated with the students' feeling of "Physics: Interesting." The

TSTP was positively correlated with the students' scores on the TOUS. Lastly

teachers' physics teaching experience was correlated to the students' PAT. This

study indicated that teachers' semesters of preparation and years experience

could be confounding variables in teacher interpersonal teaching behavior

studies and their relationship to student outcomes.

In the affective realm, Rothman (1969) found that teachers' attitudes were

a significant predictor of changes in students' attitudes. The relationship is

positive and reflective. If the teacher projects the attitude that physics is

important and easy to learn, students will project the same attitude toward

physics. The teacher's projection that physics is understandable is significantly

22

correlated to students finding that physics is easy. Rothman (1969) concluded,

"In general the results indicate that students acquire more knowledge about

physics when taught by teachers with more extensive preparation in physics,

physics education, and mathematics with greater knowledge of physics and

longer physics teaching experience" (p. 347). This led to the conclusion that two

other variables which impact on teacher effectiveness would be the teachers'

academic preparation in their field and their experience level in that particular

subject. These factors have to be controlled in order to clarify the relationship

between teaching behaviors that are occurring in the classroom and cognitive

and affective student leaming outcomes.

While Soar (1968), Bennett (1976), and Tuckman (1970) conducted

research on the linear model of teaching methodology, Flanders and Brophy

were concurrently working on the stability of the teachers' behavior. If the

teacher's teaching behavior Is not stable over a sufficient time frame then the

linear model would not be able to explain the learning outcomes of the students

(Brophy, 1973; Flanders, 1970). In the Flanders (1970) study data, from New

Zealand and Minnesota provided evidence to support the hypothesis that, "once

the teacher has established a pattern of direct or indirect teaching this pattern will

be stable the following year with completely different students" (p. 223).

Brophy (1973) investigated the question, 'Are there any stable teacher

behaviors?' The study used ordinary teachers in their classrooms without an

experimental intervention. The teachers' behaviors were identified and then

used to sort teachers into categories. Brophy used the Metropolitan

23

Achievement Test (MAT) to identify the students' achievement with the 1st grade

as the baseline, and the scores fi^om the 2" , 3"*, and 4^ grades for comparative

purposes. The students' achievement scores were used as an indicator of the

teacher's effectiveness. The baseline score was then used as a covariant and

from that point the other grade scores were converted to Grade Equivalent

Levels (GEL) and the residual scores were calculated. Brophy found that the

teacher's behaviors and effectiveness were stable across the three years this

study was being conducted. Both Brophy (1973) and Flanders (1970) studies

established that there were specific, identifiable teacher behaviors, which were

stable In the time frame of multiple school years.

Veldman and Brophy (1974) investigated the predictive value of a series

of variables on the criterion variable, pupil achievement. The selected predictor

variables were: (1) gender, (2) pre-tests, (3) teacher behaviors and (4) SES. The

pupils' genders were found to be an extremely weak predictor although Veldman

and Brophy observed that giris significantly outperformed the boys in both grade

levels. The pretest was the most powerful indicator of success with the teacher's

behavior the second most powerful indicator of pupil success in the classroom.

The pupil's SES status was not significantly con elated with student success.

Veldman and Brophy also contradicted the Rosenshine (1970) study,

which found that there were not any stable teacher behaviors. Rosenshine's

teacher sample included teachers who were in their first year of teaching and

teachers who were in the first year of teaching a new grade level. Veldman and

Brophy argued that the inclusion of these types of teachers into the Rosenshine

24

study resulted in the skewing of the data and caused Rosenshine to erroneously

conclude that teacher behaviors are inherently unstable. Veldman and Brophy

found, "...that reasonable stable estimates of teacher influence can be obtained

from standardized achievement measures when the sample selection procedures

eliminate new teachers and teachers who have recently switched grades (1974,

p. 323). Brekelmans, Holvast, and van Tartwijk, (1992) also found that teacher

experience and age were possible confounding variables, when researchers

were attempting to obtain stable estimates of teacher interpersonal teaching

behaviors and their effects on student cognitive and affective learning outcomes.

In the Good, Biddle, and Brophy (1975) work, Teacher's Make a

Difference, the authors argued that the use of transformed pretest scores as a

covariant was preferable to using the students' raw pretest scores. The raw

pretest scores were transformed into z-scores and then used as a covariant.

These raw scores would be subject to the moderating variable of the amount of

potential gain (A. Oliveraz, personal communication, June 1996). This

moderating variable, which represents the amount of potential gain, occurs

because the students who scored near the top end of the scale on the pre-test,

do not have the same opportunity of achievement as a student who scored at the

mean or less on the pre-test (A. Olivarez, personal communication, June 1996).

This is supportive of the recommendation of Good and his colleagues to utilize

pretest/posttest or repeated measures design. According to Good et al., "The

usage of the repeated measures model will eliminate the problems of raw scores

25

and gain scores, because the students' pre-tests will be used as a covariant" (p.

41).

Stallings (1976) study was another attempt to determine which types of

teaching methodologies were more efficient, direct or indirect teaching. The

Stalling's paper was a study of the 22 Follow Through educational programs;

seven were identified for fijrther study. In this study, the term follow through

group is analogous with the term longitudinal treatment group. Two of the seven

programs were identified as following the direct teaching or positive

reinforcement models and the other five programs were identified as indirect or

open structure models. A total of 136 first-grade and 137 third-grade classrooms

were observed in 36 different cities and towns. The comparison group, non-

follow through (non-treatment) classes, was identified and one class in each of

24 different locations was included in the study. Stallings found, "classroom

instructional processes predicted as much or more of the outcome score

variances than did entering school test scores of children" (p. 47). From these

findings Stallings developed the conclusion that, "...what occurs within the

classroom does contribute to achievement in basic skills, good attendance and

desired child behavior" (p. 47).

Stallings' (1976) findings in the area of comparative efficiency were a

mixed set of results. In the areas of reading and math achievement, the students

that were taught by teachers using the direct or positive reinforcement models

scored significantly higher than all of the indirect or open structure models. The

positive reinforcement model's student scores were also statistically significantly

26

higher than the comparison group's student scores. In the area of nonverbal

problem solving, the students that were taught under the open structure models

scored significantly higher than the students that were instructed in the positive

reinforcement models. These findings reinforced Soar's (1968) conclusion in

which he proposed that there was a curvilinear relationship between the lower

levels of cognition and the indirectness or directness of the teacher's teaching

style. Soar also proposed that the seemingly linear relationship between higher

order learning and Indirect teaching was because the teacher was not yet at the

optimal indirect teaching level for the higher cognitive level. Stallings (1976)

provided evidence that there is a linear relationship between the higher cognitive

levels and the teacher's indirect or open structure teaching style. Whether the

relationship is actuality curvilinear is still an unanswered hypothesis.

Deployment of Key Teaching Competencies and Teacher Effectiveness

The identification of certain teacher behaviors as stable and as

significantly correlated to student outcome led to the identification of key teacher

competencies. This was the area Medley (1977) investigated.

Medley (1977), investigated the central question, "How does the behavior

of effective teacher differ from that of Ineffective teacher?" (p. 5). Medley

reviewed 289 studies to ascertain the "relationship between how a teacher

behaves and how much the pupils learn fi^om him or her, commonly called

process-product relationship" (p. 5). Medley's basis for understanding the

27

concept of teacher competencies was to utilize the measurement of teacher

effectiveness as an indicator of teacher competence. According to Medley, "...we

shall use the measure of effectiveness as an indicator of teacher competence,

inferring that teachers who are effective are more competent on the average than

teachers who are ineffective" (p. 6). Medley further distinguished between

teacher competency and teacher effectiveness by identifying, "competence has

to do with how a teacher teaches and is measured in terms of the teacher's

behavior; how effective a teacher is is measured in terms of pupil learning" (pp.

6-7). This led Medley to "...view the behavior of the teacher as an effect rattier

tfian a cause" (p. 7). This led to the conclusion that a competency is a behavior

which is strongly associated with teacher effectiveness (Medley. 1977).

The Texas Education Agency used general teacher competencies as a

basis for the evaluation rubric assessing teaching professionals. These

competencies are divided into five categories: (1) learner-centered knowledge,

(2) learner-centered instruction, (3) equity in excellence for all learners, (4)

learner-centered communication and (5) learner-centered professional

development (Texas Education Agency, 1995). First, learner-centered

knowledge is predicated on teachers being well grounded in the subjects taught

and on the ability of the teachers to facilitate the learners' development of

patterns of studying. Second, leamer-centered instruction is based on the

teachers being able to manage their classrooms from the perspectives of the

individual leamers, groups of learners and physical material necessary for

learning to occur. These first two competencies reflect the works of Kratz (1894),

28

Charters and Waples (1929), Barr and Emans, (1930), Soar, (1968), and

Flanders (1970).

The next competency, equity in excellence for all learners, is a relatively

new factor grounded in the premise that all students can learn. Predicated on

this premise is the assertion that all students will be given the opportunity to learn

and excel in the school system. The fourth competency is related to eariier

research (Banr & Emans, 1930; Charters & Waples, 1929) in the area of effective

communication between the teachers, and the families, fellow professionals and

the public. The reflective portion of this competency, "because the teacher is a

compelling communicator, students begin to appreciate the important of

expressing their views cleariy" (TEA, 1995, p. 7), is itself reflective of Kratz's

(1894) finding, that "... children are highly susceptible to such impressions of

taste and neatness..." (p. 416) Kratz concluded that, "...pupils are generally more

appreciative of the earnest and intelligent efforts of their teacher to training and

develop them..." (p. 415). Both of these conclusions are supportive of the Texas

Education Agency's utilization of learner-centered communication. The last

competency, leamer-centered professional development, places teachers in the

status of learners as they further develop the knowledge of their subject matters

and various teaching methodologies.

29

Holistic Communication Analysis and Teacher Effectiveness: Interpersonal Teaching

Behaviors and Pupil Achievement

In the late 1970's the emphasis on research into teacher effectiveness

changed toward the quest to develop an understanding of the impact of the

teacher's interpersonal teaching behaviors on pupil achievement. At the same

time, the understanding that the student was an active participant in his/her

learning was incorporated into the interpersonal teaching behaviors model. The

interpersonal teaching behavior model rests on two theoretical frameworks,

Leary's communication theory (1957) and Watzlawick, Beavin and Jackson's

human communication theory (1967).

Theoretical Frameworks

Leary's Communication Theory

The Leary (1957) model was developed to describe and measure specific

interpersonal behaviors, primarily In a therapeutic setting. The Leary model was

developed to measure both normal and abnormal behavior on the same scale,

and it therefore can be applied both inside and outside the clinic (Wubbels,

Creton, Levy. & Hooymayers, 1993). The Leary model identifies personality as

the controlling factor in interpersonal behavior. In addition to the informative

functions of communication, the Leary model recognizes that people use

language or other forms of communication to accomplish two affective goals.

The first goal is to avoid anxiety and the second goal is to feel good about

themselves. The model further recognized that different persons will use

30

different methods to achieve the two goals. The methods available are as

numerous as the human personality; a person could use dominance or

submission and/or cooperation or opposition behaviors to obtain his/her goals.

Watzlawick. Beavin and Jackson's human communication theorv

Watzlawick and his colleagues (1967) adapted Leary's (1957)

communication theory to the field of family and marriage therapy. The premise

remained the same, that persons communicate in the manner that lets them feel

good about themselves and lowers their anxiety level. The primary difference

between Leary's model and Watzlawick et al.'s model is the level of focus. In

Leary's model, the focus is individual to individual communications in an isolate

setting, whereas in Watzlawick et al.'s model the focus is on individual or multiple

communications in a group/family setting.

Interpersonal Teacher Behaviors

Using the Leary model as a template Wubbels, Creton, and Hooymayers

(1985) developed a model for interpersonal teacher behavior. The term

interactional aspect of teacher behavior, which is synonymous with the term

interpersonal teacher behavior, was operationally defined as,"... behavior that

refer to the relationship between the teacher and his students and which is

expressed in the interaction between the personal communication in the

classroom" (Wubbels et al., 1985, p. 3).

31

Wubbels et al.'s model for interpersonal teacher behavior directly adopted

the two-dimensional plane with 'Influence' on the vertical axis and 'Proximity' on

the horizontal axis.

The model maps interpersonal behavior with help of an influence-dimension (Dominance-Submission) and a proximity-dimension (Cooperation-Opposition). These dimensions are equally divided into eight sectors. Every instance of interactional teacher-behavior can be placed within the system of axes. The closer the instances of behavior are placed in the chart, the closer they resemble each other (and the more similar are their effects on the students). (Wubbels et al., 1985, p. 3)

OD: Strict DC:Leadership

OD: Admonishing CD:Helpful/Friendly

OS: Dissatisfied CS: Understanding

SO:Uncertain SC:Student responsibility/freedom

Figure 1.

Model For Interpersonal Teaching Behavior

This adaptation of the Leary (1957) model resulted In the eight sections,

two in each quadrant in upper right; dominance-cooperation and cooperation-

dominance, lower right; cooperation-submission and submission-cooperation

32

and, lower left; submission-opposition and opposition-submission and the upper

left; opposition-dominance and dominance-opposition. Each of these sections in

the model are identified by a specific teacher characteristic, these names are as

follows: leadership, helping/friendly, understanding, student

responsibility/fireedom, uncertain, dissatisfied, admonishing and strict.

The Wubbels's (1985) model on interpersonal teaching behavior was not

predicated on the assertion that one teacher behavior was the cause of student

achievement. This was a break from the previous literature which attempted to

place all teacher behaviors on a bi-polar, linear continuum (see Flanders, 1960,

1964. 1965, 1970 1970a; Haige & Schmidt, 1956; Stalling 1976; Soar, 1968;

Tuckman, 1970). Instead, the Wubbels's model recognized that the teacher

behavior will consist of all of the aforementioned characteristics identified by the

learners. The linear models were inefficient in assessing all the behaviors

present in a student-teacher interpersonal interaction. In addition, Wubbels's

model recognized as important the teacher trait of stability as identified by

Brekelmans (1989). Prior research (Brophy, 1973; Veldman & Brophy, 1974)

also had established that quantifiable teacher traits were stable across years and

grades. Except for the first few weeks of the school year, a teacher's

interpersonal teaching style is stable across years and classes. Wubbels (1992)

challenged the assertion of teacher stability but because this study is a

correlation study teacher stability is not of paramount Importance.

These findings led Wubbels and his colleagues to develop the

Questionnaire on Teacher Interaction (QTI) to assess student perceptions of their

33

teacher's interpersonal teaching behaviors. Each of these model sections have a

number of items statements on the Questionnaire on Teacher Interaction (QTI)

associated with it. Students respond to the item statements of the instrument on

an A-E Likert scale, an A is scored as a 4 and it signifies strong agreement with

the Item and an E is scored as a 0 and it signifies strong disagreement with the

item. Scale scores are then constructed by adding all the item scores for a scale

and dividing by the number of item statement, this results in a ratio score

between 0.00 to 1.00. "The higher the score in a sector the more significantiy or

frequently the behavior of the sector Is displayed" (Wubbels et al., 1985, p.5).

Several studies were conducted to ascertain the validity and reliability of the QTI.

These studies were conducted in The Netheriands, in Singapore and in Australia.

As a result of these studies, Brekelmans (1989) calculated an item internal

consistency of greater than .70 on the individual level and an item internal

consistency of greater than .80 on the class level. A determination of its

reliability was calculated using Cronbach's alpha using student answers as

repeated measures with a result of .92 (Brekelmans, 1989). In her study

Brekelman used a test-retest procedure to ascertain if the QTI was reliable. The

result of a repeated measures coefficient of .92 Is also evidence that student

perceptions of teacher interpersonal teaching behaviors are stable across time.

A value of .80 or higher is considered adequate for internal validity. Brekelmans'

(1989) research also ascertained that the two factors, influence and proximity,

accounted for approximately 80% of the variance on all the scales. The QTI can

be completed by the students as an evaluation of teacher behavior, or the QTI

34

can be used by the teachers to self-report their behavior or identify their 'ideal'

teaching behaviors. "Using this instrument, interactional teacher-behavior can be

examined empirically. It is also suitable for giving feedback to teachers regarding

their behavior" (Wubbels et al., 1985, p. 5).

Wubbels and Levy (1989) conducted a comparative study of the Dutch

version and the derived American version of the QTI. Both the Dutch and

American version utilized students to measure aspects of the learning

environment. The QTI was translated from the Dutch to English with an added

precaution that, "the translation of the items were checked with a back-

translation by an independent second translator" (Wubbels & Levy, 1989, p. 4).

The original American version contained one hundred items from the original

seventy-seven items in the Dutch original version. This increase in items was

caused by more than one possible translation fi^om several Dutch items. The

American version was then inspected by Wubbels and Levy to ascertain if it was

still in accordance with the original Leary (1957) model. According to the Leary

model, "an item should correlate highest with the scale to which it belongs and

lowest with the opposite sector"(Wubbels & Levy, 1989, p. 4).

Thirty-three items were removed from the original one hundred American

Items because they did not correspond to the parameters of the assumptions of

the Leary model. The second version was field-tested and two more items were

eliminated due to the same psychometric concerns. The final American

instrument consisted of sixty-five items. Of these items, fifty-nine were direct

translations of their respective Dutch items. A series of item analyses were

35

conducted to ascertain the American instrument's reliability. Seven of the eight

section's reliabilities were above .90 and the other section's reliability was

calculated to be .86. These values far exceed the minimal value of .60, which

has historically been identified as the value at which the researcher does not

attempt further improvement in the research instrument (Wubbels & Levy, 1989).

These values also exceed the threshold for utilization in tests that will influence

decisions about individuals (Wubbels & Levy, 1989). In a factor analysis the

variation accounted for by the two factors, influence and proximity, was

calculated to be 88.3% (Wubbels & Levy, 1989). From this data and analysis, "it

can be concluded that the reliability of the American QTI is good and that there is

some confirming evidence about the validity of the new instrument" (Wubbels &

Levy, 1989, p. 8).

Brekelmans, Wubbels, and Creton (1990) used the Questionnaire of

Teacher Interaction (QTI) to investigate the question, "is there a correlation

between student perception of teacher behavior and cognitive and affective

outcomes In the context of a physics curriculum?" There were two types of

physics curriculum in The Netheriands, the traditional curriculum and the PLON

curriculum. PLON is a Dutch acronym for, Dutch Physics Curriculum

Development. The traditional curriculum was designed for students who were

going to complete physics in their college. The content was reflective of a

simplified and dated university physics course. The course did not emphasize

the practical aspects of physics and the students were not required to conduct

any laboratory exercises (Brekelmans, Wubbels and Creton, 1990). The newer

36

curriculum PLON was developed to, "create curriculum materials that stimulate

activity learning, reality learning and participation learning" (Brekelmans et al.,

1990, p. 338).

Cognitive outcomes were measured with a standardized and

internationally developed test for physics subject matter. The researchers did not

delineate the standardized test used in this research by cognitive nor affective

levels of complexity. The test's validity was established by a high correlation

between the teachers' in-class students' grades and the students' scores on the

standardized physics test. The Dutch secondary educational school system is

stratified into three academic levels. The MAVO school type is the general

secondary educational situation at the intermediate level. The HAVO school type

is the general secondary education situation at the higher level and the WVO

school type is secondary level education in preparation for university studies

(Brekelmans et al., 1990). "Further corroboration of the validity is obtained fi'om

the fact that the levels of the students abilities of the three school types are

represented in the test scores (on a scale 0-100): MAVO 70, HAVO, 76, VWO

81" (Brekelmans et al., 1990, p. 339).

The student's affective outcomes were ascertained by utilization of a

questionnaire that targeted five areas of interest. These areas were represented

by five scales: "appreciation of lessons, instructiveness, easiness, structuredness

of lessons and subject matter and motivation for physics" (Brekelmans et al.,

1990, pp. 340-341). The affective instrument is still in the Dutch language and

has not been translated using the procedures discussed eariier. The researchers

37

discovered that there was no significant difference between the two groups

(PLON and revised PLON) in regard to the students' cognitive and affective

learning outcomes. There was a significant difference found between student

perceptions of their teachers as assessed by the QTI interpersonal teaching

behavior scales and student cognitive and affective learning outcomes.

In the cognitive domain, the teacher's interpersonal teaching behavior, for

the section dominant-opposition was significantiy correlated to cognitive

achievement at +.39 and the submission-opposition section was significantiy

correlated to cognitive achievement at -.38. The other six teacher interpersonal

teaching characteristics were not significantiy correlated with student cognitive

outcomes. The cognitive domain was not divided by cognition levels and the

study did not address possible differences in significance and correlation as it

pertains to the various cognition levels.

When the data in the Brekelmans et al. (1990) study were examined to

ascertain which of the stimuli, interpersonal teaching behavior or type of

curriculum utilized in the classrooms were more closely correlated with students'

cognitive outcomes, the conclusion supported by the data was that Interpersonal

teaching behaviors were significantly correlated with cognitive outcome. The

same study found the differing types of curricula were not significantiy associated

with the students' cognitive outcomes.

In the affective domain, the teacher's interpersonal teaching behavior was

significantiy correlated with the multiple affective outcomes, appreciation of

lessons, instructiveness, structuredness, and motivation for physics. In the

38

section dominant-cooperation, this type of interpersonal teaching behavior was

positively correlated with appreciation of lessons (AP), instructiveness (IN),

structuredness of lessons and subject matter (ST) and motivation for physics

(MO). These con-elations were found to be significant. In the section

cooperation-dominant, this type of interpersonal teaching behavior was again

significantly positively correlated with appreciation of lessons (AP),

Instructiveness (IN), structuredness of lessons and subject matter (ST) and

motivation for physics (MO). In the section cooperation-submission, this type of

interpersonal teaching behavior once again was significantiy positively correlated

with appreciation of lessons (AP), instructiveness (IN), structuredness of lessons

and subject matter (ST) and motivation for physics (MO). The next section

submission-cooperation, exhibited a type of interpersonal teaching behavior that

was significantiy positively correlated with only appreciation of lessons (AP), and

easiness. The other three types of affective outcomes, instructiveness (IN),

structuredness of lessons and subject matter (ST) and motivation for physics

(MO) were not significantly correlated toward a submissive-cooperative

interpersonal teaching style. The section of interpersonal teaching style

identified as submission-opposition was not significantiy correlated to any of the

students' affective outcomes. The sixth section under consideration, opposition-

submission interpersonal teaching behavior was significantly negatively

correlated with all the affective student outcomes. The section identified as the

opposition-dominant interpersonal teaching style was significantiy negatively

correlated with appreciation of lessons (AP), instructiveness (IN), and

39

structuredness of lessons and subject matter (ST). The last section dominant-

opposition was negatively significantiy correlated with easiness, but all other

student affective outcomes were not significantiy correlated. These findings led

to a general observation that a teacher's interpersonal behavior, which falls to the

right of the Influence factor, will be correlated with positive affective student

outcomes and those interpersonal teaching behaviors which fall to the left of the

influence factor will be negatively with positive affective student outcomes. While

the line of effectiveness for the cognitive realm is rotated 45 degrees counter

clockwise of the "y" axis that has been identified as the dominant-submission

axis. This leads to a dilemma for the teacher because the most effective areas of

interpersonal teaching behaviors for the cognitive and affective domain are mildly

contradictory. The way out of this dilemma might be found through the

comparison of the teachers' ideal teacher and the students' best teacher. The

interpersonal patterns on the two-way matrix are very similar. Perhaps, the

students value the cognitive outcomes more than their affective outcomes.

These data from the Brekelmans, Wubbels, and Creton, (1990) study led

to the conclusion that the teacher leadership is positively correlated to positive

cognitive outcomes. The dominant-cooperation (DC) section was also positively

correlated to increases in four affective sets which are Appreciation of Lessons,

Instructiveness, Structuredness of Lessons and Subject Matter and Motivation.

The other affective set was not significantiy correlated. The other section related

to DC the cooperation-dominant (CD) section was significant in all the same

affective sets as was the DC section but, CD was found not to be significant with

40

cognitive outcomes. The next quadrant, cooperation-submission (CS) and

submission-cooperation (SC) were not significantly correlated with cognitive

outcomes. The CS section was significantiy correlated with the affective sets of

AP, IN, ST and MO. The SC section was significantiy conrelated with the affective

sets of AP and EA, all other pairs were insignificant. The next quadrant

submission-opposition (SO) was correlated with a negative cognitive outcome

while the opposition-submission (OS) set was not significantiy correlated to

cognitive outcome. The SO set was not significantiy correlated with any of the

affective sets, while the OS significantiy negatively correlated with all the

affective sets. In the last quadrant, opposition-dominant (OD) was not

significantiy correlated with cognitive outcomes while the dominant-opposition

(DO) set was positively correlated with cognitive outcomes. The OD section was

significantiy correlated with the affective sets of AP, IN and ST in a negative

manner. The DO section was significantly correlated with only EA and that is a

negative manner.

After closer examination of the results of the correlations of the teachers'

interpersonal teaching behaviors and the cognitive and affective learning

outcomes, it is apparent that

if the teacher's aim is to promote both student achievement [Cognitive] and attitudes [Affective], they are pulled in opposite directions by the conflicting demands of the sectors DO and SC. In order to promote higher achievement, teachers have to be stricter but, to promote better attitudes, they have to be less strict. (Wubbels et al., 1993, p.7)

This problem of contradicting needs of the students has been identified in

the literature but, a solution has not been offered.

41

Summarv: Interpersonal Teaching BehaviorsTheorv

The interpersonal teaching behavior model (Brekelmans, Wubbels &

Creton, 1990; Tuckman, 1976, 1995; Wubbels, Creton, & Hooymayers, 1985;

Wubbels, Creton, Levy, & Hooymayers, 1993) is predicated on the idea that

students perceive their teachers to exhibit certain behaviors through their

communication patterns of physical actions. Perceived Interpersonal teaching

behaviors are associated with student achievement. These perceptions are

individual in nature and each student acts on his/her perceptions. Student

perceptions of teacher leadership, strictness, helpful/friend and understanding

behaviors are related to positive student achievement. Student perceptions of

teacher increasing student responsibility/freedom, uncertainty, dissatisfaction,

and admonishing behaviors are related to negative student achievement.

Perceived Interpersonal teaching behaviors are also associated with

student affective learning outcomes. These perceptions are also individual in

nature and each student acts on their perceptions. Student perceptions of

teacher leadership, helpful/friend, understanding, and increased student

responsibility/fi'eedom behaviors are related to positive student affective learning

outcomes. Student perceptions of uncertainty, dissatisfaction, admonishing, and

strictness teachers behaviors are related to negative student affective learning

outcomes. Because the interpersonal teaching behaviors model is the basis for

42

the QTI, the QTI should be predictive of student achievement and affective

learning outcomes.

Bloom's Taxonomv of Educational Obiectives

Arguably, one of the most influential educational monographs of the past

half century is Bloom's (1956) Taxonomv of Educational Obiectives. The

Classification of Educational Goals. Handbook I: Cognitive Domain. Neariy forty

years after its publication in 1956 the volume remains a standard reference for

discussions of testing and evaluation, curriculum development, and teaching and

teacher evaluation (Anderson & Sosniak, 1994, vii). Bloom's taxonomy can best

be visualized as a pyramid. At the first level is knowledge, the second level is

comprehension, the third level is application, the fourth level is analysis, the fifth

level is synthesis and the sixth level is evaluation. The levels are also divided

into lower and higher levels. The low level consists of the first three levels,

knowledge, comprehension, and application. The high level consists of the level

four through six, analysis, synthesis, and evaluation.

Bloom's Taxonomy has been increasingly used by educators to classify

educational objectives since being introduced to the educational worid in 1956.

According to Bloom (1956),"... any objective which describes an intended

behavior should be classifiable in this system" (p. 14). Because the Taxonomy

and the objectives are congruent in their natures, they are both based on,"... the

classification of intended behaviors" (Bloom, 1956, p.13), the central question

becomes, 'Is the Taxonomy an internally consistent hierarchical stnjcture?' The

43

answer to that question Is a qualified yes (see Hill & McGrew , 1981; Hill, 1984;

and Kropp and Stoker, 1966).

First, the Taxonomy was examined philosophically to ascertain if it is

reflective of a particular theory of education or learning. As Bloom (1994)

acknowledged, he and the other authors were unable to identify a unifying theory

which would account for all the behaviors represented in the classifiable

educational objectives under study. This does not eliminate the Taxonomy from

consideration, as currently there is not a unifying theory that explains how

learning occurs in an educational setting. The disjointed nature of learning

theory Is reflected in the differences In the basic tenets that support the theories

of information processing, constructivism, and direct instruction. The first major

tenet of the information processing theory is that each student will process input

(information) from the teacher and build pathways to elaboratively place the

input in long term memory. The second tenet is that there is an objective reality

and student learning can be assessed using that reality. The first major tenet of

constructivism is that each student will process input (information) from the

teacher and build schemata to accomadate the information. The second tenet is

that there is not an objective reality and student learning can not be assessed

using that reality. The first major tenet of direct instruction is that each student

will absorb the information presented to him/her and the information will be

placed in memory without any elaboration or modification. The second tenet is

that there is an objective reality and student learning can be assessed using that

reality. Another consideration is the philosophical analysis of the Taxonomy. As

44

Furst (1994) noted, the taxonomy's claim to being value fi-ee is not considered

possible from a philosophical perspective. "Classifications tend to throw

emphasis on certain qualities and, in turn, to diminish the apparent significance

of other qualities" (Furst, 1994, p. 28). While this assertion has merit, it is not

critical to the use of Bloom's Taxonomy, because the authors of the taxonomy

expected each teacher to use the taxonomy to assess their students' abilities via

a series of intended learning outcomes. The use of a rigid, all-inclusive test

would be detrimental to education since the educator would be unable to

emphasize one objective over another objective. The educator would then either

have to construct enormous tests, which covered every type of objective or

develop a series of smaller more specific tests. The use of the latter option

would operationally recreate Bloom's taxonomy. It is the teacher's prerogative to

emphasize those objectives that he/she believes are the most valuable. Just as

It is the local/state educational establishment's duty to emphasize certain

objectives in their criterion tests. Another perceived philosophical deficiency is

the assertion of linearity of the various levels in Bloom's Taxonomy. The linear

assumption was attacked by the assertion that certain lower level objectives

could be more difficult to achieve than higher level objectives. This situation

could occur, but it is acknowledged that the difficulty of an item is not directiy

related to an item's cognitive level (see Bloom, 1956, 1994; Bloom, Hastings, &

Madaus, 1971; Furst, 1994; Kroop & Stoker, 1966; Madaus, Woods, & Nuttall,

1973).

45

The last area of examination is a empirical examination of the correlations

between the various levels of Bloom's Taxonomy. The most extensive analysis

of the hierarchical structure of Bloom's Taxonomy was reported by Kropp and

Stoker In 1966. All of the other statistical analyses of Bloom's Taxonomy have

consisted of the reanalysis of Kropp and Stoker's data.

Inherent in Bloom's (1956) Taxonomy hierarchy is that the levels proceed

from simple to complex, from knowledge to evaluation. As Bloom (1994) stated,

"... the hierarchical relations amoung the categories would enable users of the

taxomony to understand more cleariy the place of a particular objective in

relationship to other objectives" (p. 4). From this perspective each higher level

contains the lower cognitive levels in its functioning. Kropp and Stoker (1966)

found that the first four levels of Bloom's taxonomy were cumulative and

hierarchical in nature but that the constructs of synthesis and evaluation did not

perform as predicted. In two of the four tests used by Kropp and Stoker, the

synthesis and evaluation constructs were found in the area predicted by Bloom's

taxonomy. In the other two tests, the synthesis and evaluation questions were

found to be located between knowledge and comprehension in one test and in

the other test the synthesis questions were found in between knowledge and

comprehension (Kropp & Stoker, 1966).

Kropp and Stoker (1966) were concemed with the items that were present

in the tests to measure the constructs of synthesis and evaluation. They

believed that lack of clarity in the items were responsible for tiie mixed results.

The incorrect identification of items' hierarchical levels is a confounding variable,

46

which is addressed in this study. Since Kroop and Stoker (1966) confirmed the

relative placement of the first four levels of Bloom's taxonomy, knowledge

through analysis they concluded, "The evidence is more conforming than

disconfirming of the hypothesis" ( p. 84). Kropp and Stoker also noted that there

was not a statistical test, which could be used to support a clear decision as to

the hierarchical nature of Bloom's Taxonomy.

A series of researchers reanalyzed Kropp and Stoker's data using more

advanced and powerful statistical formula. Madaus, Woods, and Nuttral (1973)

reanalyzed the data using pathway analysis and they found that if general ability

is held constant, statistically Bloom's Taxonomy was hierarchical in nature. But,

the structijre was "Y" shaped with Knowledge and Comprehension forming the

base of the "Y". The structure then branches with Application and Synthesis on

one branch and Analysis on the other branch. The Evaluation level was not

supported in this model.

Hill and McGrew (1981) reanalyzed the data using the LISREL statistical

package. They found that the six level hierarchy proposed by Bloom was not

supported by their analysis, but if the knowledge level was removed the resultant

five level hierarchy was supported by the data. This is supportive of Bloom's

Taxonomy, which separated the knowledge level from the other five levels. Hill

(1984) later re-analyzed the data and inserted general ability into the model, but

using the LISREL analysis he found that general ability did not fit the data and he

rejected it as part of the hierarchy.

47

The last two categories synthesis and evaluation were found in differing

relationships with each other in Bloom's taxonomy but they were found to be at a

higher hierarchical level than the other four levels (Hill & McGrew, 1981; Hill,

1984). The synthesis and evaluation categories were found to be distinctive from

the lower levels of cognition, knowledge, comprehension and application. These

findings are supportive of this researcher's Intent to divide Bloom's taxonomy into

two categories, low and high. The theoretical reason for dividing the objectives

into higher and lower domain objectives was grounded in prior research (see

Bennett, 1976; Soar, 1968; Stalling, 1976; Wispe. 1951). Bennett (1976) found

differing achievement levels in his study that were related to teacher

behaviors/styles and the level of the objectives in the assessment instrument.

Soar's (1968) study found that the relationship between teaching styles and

student outcomes would be curvilinear for the lower cognitive levels with both

direct and indirect styles con-elated with lower cognitive learning outcomes and

the mixed teaching style correlated with higher cognitive learning outcomes.

Stalling's (1976) study found that in the areas of reading and math achievement,

the students that were taught by teachers using the direct or positive

reinforcement models scored significantiy higher than all of the indirect or open

structure models. In the area of nonveriDal problem solving, the students that

were taught under the open structure models scored significantiy higher than the

students that were instructed in the positive reinforcement models. These results

are further supportive of the conclusion that teaching styles are differentially

impacting student achievement outcomes at different levels. Because student

48

learning outcomes are used to ascertain the effects of interpersonal teaching

behaviors, the confounding variable higher versus lower cognitive domains must

be controlled in order to clarify the relationship between interpersonal teaching

behaviors and student learning outcomes. Wispe's (1951) study found that there

was a difference in student scores in the direct teaching groups when the

students were divided into high and low ability groups in that the low level ability

students benefited from direct teaching to a greater degree than high level ability

students. This type of effect can also be ascertained by using an assessment

instrument which is divided into lower and higher cognitive level objectives.

Bloom's taxonomy was used to classify the objectives of the Biology End

of Course (BECE), Spring 96 version into the general levels of lower and higher

cognitive levels. These objectives are based on observable behaviors, thus if

students exhibit these behaviors it will be taken as evidence of student mastery

of that objective. Since the BECE objectives are based on observable behaviors,

these objectives should be classifiable using the Bloomian Taxonomy of

Cognitive Objectives.

Lastiy, the relationship between achievement and affect must be

explored. Affect is "the feelings and emotions that an individual brings to bear on

a task" (Ormrod, 1995, p. 420). The student's affective attitude toward a subject

will impact on the values attached to the subject under study. This led Ormrod

(1995) to conclude, "affect Is cleariy a factor in learning and cognition" (p.423).

This led to the conclusion that if student affect can be positively increased there

should be a corresponding increase in achievement.

49

The literature of teaching styles research has traveled through three

distinctive phases. The first phase was a series of investigations into various

teaching style continua, which were predicated in a linear, bi-polar teacher style

model. Once teacher styles were identified various methodologies were

identified and the researchers then used those data sets and frameworks to

develop teacher skills, techniques and competencies. This research area was

commonly identified by a series of investigations of process-product processes.

The utilization of process-product processes is evident in the development of the

teacher assessment instruments, such as the Texas Teacher Assessment

System (TTAS). Currently, researchers (Brekelmans, Wubbels, & Creton, 1990,

Brekelmans, Holvast, & van Tartwijk, 1992; Fraser, 1986; Fisher, 1995,

Henderson, Fisher, & Fraser, 1995; Wubbels, Brekelmans, Creton, &

Hooymayers, 1989; Wubbels, Brekelmans, & Hooymayers, 1991; Wubbels,

Creton, & Holvast, 1988; Wubbels, Creton, & Hooymayers, 1985, 1987, 1992;

Wubbels, Creton, Levy, & Hooymayers, 1993; Wubbels, & Levy, 1989) are trying

to identify interpersonal teaching styles of teachers and ascertain the styles'

effects on student cognitive and affective outcomes. This area has been

pioneered by the research of Professor Dr. Wubbels in The Netheriands, Dr.

Levy In the USA and Dr. Fraser in Australia as well as their colleagues. This

literature review has established that the correlations between interpersonal

teaching behaviors and student cognitive and affective outcomes at the higher

and lower levels of Bloom's taxonomy in American biology classes have not been

investigated. This researcher will attempt to fill the identified gap in the literature.

50

CHAPTER III

RESEARCH DESIGN AND METHODOLOGY

Introduction

This study is grounded in the educational and interpersonal

communication literature. The review of this literature revealed a rich educational

literature as it related to the effectiveness of teachers' instruction (see Bennett,

1976; Brophy, 1973; Flanders, 1960, 1964, 1965, 1970 1970a; Good, Biddle &

Brophy 1975; Haige & Schmidt, 1956; Medley. 1977, 1979; Ostiund, 1956;

Rosenshine, 1970; Stalling 1976; Soar, 1968; Tuckman, 1970; Veldman &

Brophy, 1974; Wispe, 1951). This review also revealed a rich literature

concerned with the usage of interpersonal communication theory to quantify a

segment of the phenomenon known as teacher effectiveness (see Brekelmans,

Wubbels, & Creton, 1990, Brekelmans, Holvast, & van Tartwijk, 1992; Fraser,

1986; Fisher, 1995, Henderson, Fisher, & Fraser, 1995; Wubbels, Brekelmans,

Creton, & Hooymayers, 1989; Wubbels, Brekelmans, & Hooymayers, 1991;

Wubbels, Creton, & Holvast, 1988; Wubbels, Creton, & Hooymayers, 1985,

1987, 1992; Wubbels, Creton, Levy, & Hooymayers, 1993; Wubbels, &

Levy, 1989). The term teacher effectiveness is used to reflect the reality that the

classroom environment is more complicated than teacher-student instructional

couplet. It Is a more global term than the term effectiveness of teachers'

instruction. While there are studies concerning the relationship between

interpersonal teacher behaviors and student cognitive and affective outcomes,

51

these studies were primarily conducted outside of the USA and involved

educationally stratified student populations. In the Brekelmans, Wubbels and

Creton (1990) study student perceptions of their teacher's interpersonal teaching

behaviors were correlated with student cognitive and affective outcomes. Their

study used ability stratified student samples that eliminated the communication

intercourse between students of differing abilities. This stratification resulted in

an artificial homogenized sample and thereby reduced the found external validity.

Furthermore, the studies in this area, (Brekelmans et al., 1990; Fisher,

Henderson, Fraser, 1995; Henderson, 1995) did not account for the various

levels of cognitive outcomes of students. This study will explore those two areas

and add to the growing understanding of teacher effectiveness as it relates to

communication theory and the interpersonal teacher behavior model.

Purpose

The purpose of the study was to answer the following research questions:

1. What are the directions and degrees of the relationships of student

perceptions of their biology teachers' interpersonal teaching behaviors and

student overall, higher and lower level achievement outcomes?

2. What are the direction and degree of the relationships of student

perceptions of their biology teachers' interpersonal teaching behaviors and

student affective outcomes?

52

Hypotheses

The following hypotheses were tested:

HOI: None of the eight scales of the QTI are significantly related to



significantiy related to student higher achievement

outcomes.

Sub-hypothesis 1.2: None of tiie eight scales of the QTI are

significantiy related to student lower achievement outcomes.


significantiy related to student overall achievement

outcomes.

H02: None of the eight scales of the QTI are significantiy related to






Methodology

Participants

A total of 111 participants in this study were members of the freshman

(9th) grade biology classes at a medium high school in a medium sized western

53

Texas school district. The students' age range was 14-17 years of age. The

participant group's gender composition was 52.2% male (n=58) and 47.8%

female (n=53). The ethnic composition of the participants was 75.1% majority

(n=83) and 24.9% minority (n=28). The participants' at-risk statuses were 72.1%

not at-risk (n=80) and 29.9% at-risk (n=31). All participants who entered this

study were responding to the author's presentation soliciting participants and all

participants were assured of the confidentiality of their responses and

assessment scores.

Instrumentation

The data collected to ascertain student perceptions of biology teachers

were collected using the Questionnaire on Teacher Interaction (QTI). The data

on student achievement and affective outcomes were collected using two

instruments, the Biology End of Course Examination (BESE) and the Biology

Student Affective Instrument (BSAI).

Questionnaire on Teacher Interaction (QTI)

The QTI is a 64-item instrument that assesses student perceptions of their

teachers' interpersonal behaviors on eight scales. Each scale assesses a

particular facet of the teachers' interpersonal behaviors (see Figure 1, p 32). The

scales are identified as leadership (DC), Helpful/fi iendly (CD), Understanding

(CS), Student responsibility/freedom (SC), Uncertain (SO), Dissatisfied (OS),

Admonishing (OD), and Strict (DO) (Wubbels, Creton, & Hooymayers, 1985).

54

The letters in parenthesis are the code assigned to each section by its

relationship to the underiying dominant-submissive /opposition-cooperation two

way matrix. The instrument's eight scales have been found to have reliability

coefficients at the student level of; leadership, .80; helpful/fi-iendly, .88;

understanding, .88; student responsibility/freedom, .76; uncertain, .79;

dissatisfied, .83; admonishing, .84, and strict, .80 (Wubbels, Creton, Levy, &

Hooymayers, 1993). A scale with a found alpha equal to or above .80 is

considered very reliable. Because the alpha values of two scales (uncertain and

dissatisfied) are less that .80, data collected using the two scales should be

Interpreted with caution.

Each scale was operationally defined by a series of words, which are

descriptions of the variable's that scale assesses. The DC (Leadership) scale

was composed of the descriptions of: notice what's happening, lead, organize,

give orders, set tasks, determine procedure, structure the classroom situation,

explain, and hold attention. The CD (Helpful/Friendly) scale was composed of

the descriptions: assist, show interest, behaves in a friendly or considerate

manner, be able to take a joke, inspire confidence and trust. The CS

(Understanding) scale was composed of the descriptions of: listen with interest,

empathize, show confidence and understanding, accept apologies, look for ways

to settle differences, be patient and open. The SC (Student

Responsibility/Freedom) scale was composed of the descriptions of: give

opportunity for independent work, wait for class to let off stream, give freedom

and responsibility, and approve of something. The SO (Uncertain) scale was

55

composed of the descriptions of: keep a low profile, apologize, wait and see how

the wind blows, and admit one is in the wrong. The OS (Dissatisfied) scale was

composed of the descriptions of: wait for silence, consider pros and cons, keep

quite, show dissatisfaction, look glum, question, and criticize. The OD

(Admonishing) scale was composed of the descriptions of: get angry, take pupils

to task, express irritation and anger, forbid, correct and punish. The DO (Strict)

scale was composed of the descriptions of: keep reins tight, check, judge, get

class silent, maintain silence, be strict, exact norms, and set rules.

Each scale of the QTI produced a ratio score which was indicative of the

degree that interpersonal teaching behavior is perceived to be present by

students. All the scales were measured using a 5-point ( 0 - 4 pt) Likert scale.

The items in a scale were counted and that number was multiplied by 4. This

number became the denominator In a ratio. A student's responses on those

same items were added together and that number became the numerator in the

ratio. The ratio was then converted to a number, which was between 0.0 and

1.0. 0.0 means that teacher behavior was not perceived by that student to be

present and 1.0 means that behaviors was perceived by the student to always be

present. For example, in the DC (Leadership) scale there are seven items. The

denominator would be 7X4, which equals 28. A student's responses to the

seven items were added and for this example that number was 22. The found

ratio is 22/28 and it is converted into .786. These calculations were repeated for

each scale of each student's QTI response sheet. These calculations resulted in

eight scale values for each student in the study.

56

Biology End-of-Course Examination, Spring 1996 version (BECE)

The reliability coefficients for this examination (BECE) was found to range

from .75 to .94, with most of the values being in the high .80 to low .90 range

(Technical Work Group, Texas Education Agency, National Computer Systems,

The Psychological Corp. & Measurement Incorporated, 1995). The procedure

used to determine the instrument's alpha was the KR-20 formula for dichotomous

choices.

The BECE is a 42-item multiple choice instrument developed for the

Texas Educational Agency which assessed students' mastery of nine objectives

in the area of biology. These objective are: (1) the student will demonstrate an

understanding of concepts In heredity and biological change over time, (2) the

student will demonstrate an understanding of concepts in patterns of living

systems, (3) the student will demonstrate an understanding of concepts in

ecology, (4) the student will demonstrate the ability to apply laboratory

techniques and use equipment in a biology context, (5) the student will

demonstrate the use of skills in acquiring and organizing data, (6) the student will

demonstrate the ability to interpret and communicate scientific data and/or

information, (7) the student will demonstrate skills in drawing logical inferences,

predicting outcomes, and forming generalized statements, (8) the student will

design and conduct biological experiments and activities, and (9) the student will

demonstrate an understanding of the application of science in daily life.

57

The theoretical reason for dividing the objectives into higher and lower

domain objectives is grounded in prior research (see Bennett, 1976; Soar, 1968;

Stalling, 1976; Wispe, 1951). Bennett (1976) found differing achievement levels

in his study that was related to teacher behaviors/styles and the level of the

objectives in the assessment instrument. Soar's (1968) study found that the

relationship between teaching styles and student outcomes would be curvilinear

for the lower cognitive levels with both direct and indirect styles correlated with

lower cognitive learning outcomes and the mixed teaching style correlated with

higher cognitive learning outcomes. Stalling's (1976) study found that in the

areas of reading and math achievement, the students that were taught by

teachers using the direct or positive reinforcement models scored significantly

higher than all of the indirect or open structure models. In the area of nonverbal

problem solving, the students that were taught under the open structure models

scored significantiy higher than the students that were instructed in the positive

reinforcement models. These findings are supportive of this researcher's intent to

divide Bloom's taxonomy into two categories, low and high.

Bennett's (1976), Soar's (1968), Stallings's (1976), and Wispe's (1951)

results are further supportive of the conclusion that teaching styles are

differentially impacting student achievement outcomes at different levels. Since

student learning outcomes are used to ascertain the effects of Interpersonal

teaching behaviors, the confounding variable higher versus lower cognitive

domains must be controlled in order to clarify the relationship between

interpersonal teaching behaviors and student learning outcomes.

58

Each of the BECE objectives were classified by a Professor of Curriculum

and Instruction (Elementary Mathematics) Dr. Cooper, and two Curriculum and

Instruction doctoral students, Ms. Peters, and the researcher using key word

analysis into either low or high levels of learning. Keyword analysis is based on

the association of certain words/phases with certain levels of Bloom's taxonomy

(Bloom, 1956). In this research the three lower levels of Bloom's taxonomy,

knowledge, comprehension, and application were identified as the low level of

cognition. Conversely, the three higher levels of Bloom's taxonomy, analysis,

synthesis, and evaluation were identified as the high level of cognition.

Objectives 1-3 in the BECE all begin with the root, 'The student will demonstrate

an understanding of concepts...'. This is indicative of a lower cognitive level

usually identified as comprehension. Objectives 4 and 5 in the BECE are also

identified as members of the lower cognitive domain due to their root descriptors.

In objectives 4 and 5 the students are required to apply their conceptualization of

laboratory techniques (objective 4) and data collection skills (objective 5). This is

clearly at the application level, which is included in the lower cognitive domain.

Objectives 6-8 in the BECE are all in the higher cognitive domain. This is

evidenced by the key words in their sentence roots, such as interpret and

communicate (objective 6), drawing logical inferences, predicting outcomes, and

forming generalized statement (objective 7), design and conduct biological

experiments (objective 8). In objective 9 students are required to demonstrate

their understanding of the applications of science in daily life, this activity is

identified as an application activity and it is assigned to that level. This

59

identification process by Dr. Cooper, Mr. Smith and Ms. Peters resulted in

Objectives 1-5 being assigned to the lower cognitive level in Bloom's Taxonomy

and Objectives 6-8 were assigned to the higher cognitive level. The agreement

in rating between Dr. Cooper, Mr. Smith and Ms. Peters was 89%, with

agreement on objectives 1-8 and disagreement on objective 9. On objective 9,

Ms. Peters identified it as an application objective, Mr. Smith identified it as an

analysis objective, and Dr. Cooper thought the objective was too broad to be

categorized. After consultation the objective was placed In the lower cognitive

area. This resulted in 31 items of BECE measuring the lower level of learning

and 11 items of BECE measuring the higher level of learning.

The variables produced by this instrument were pre-test and post-test

overall scores, pre-test and post-test lower cognitive level scores, and pre-test

and post-test higher cognitive level scores. Pretest scores were used to

ascertain the extent of student knowledge of biology prior to the year's instruction

of biology. The posttest scores were used to ascertain the extent of student

learning outcomes after a year of instruction of biology. The overall, low, and

high scores were determined by percentage correctiy answered items measuring

low level, high level, and total knowledge of biology, ranging between 0 and 100.

Biology Student Affective Instrument (BSAI)

The BSAI Is a 9-item Likert scale instrument that recorded students'

feelings and opinions about biology. This instrument was a modification of

Talese and Ollvarez's (1993) instrument that assessed students' feelings and

60

opinions about mathematics. Construct validity of BSAI was not well established

by the insignificant relationship between attitudes toward biology and student

pre-test scores. The researcher examining the item statements established face

validity. Item statements 1. 4, 5, 6, and 7 are examples of opinion statements.

The items In this instrument were clustered into two factors, an opinion

factor and a feeling factor. Item 1,4,5, 6, and 7 belonged to the opinion factor.

Sample items were 'There were always rules..." and "Biology is mostiy

memorizing." The factor loading coefficients were .376, .467, .685, .836, and .495

for items 1, 4, 5, 6, and 7, respectively. The opinion factor had a Cronbach alpha

of .52. Item 2, 8, 9, and 10 belonged to the feeling factor. Sample items were

"Biology is interesting" and "Biology is fun." Factor loading coefficients were

.849, .700, .698, and .812 for the four items, respectively. The Cronbach alpha

was .77 for the feeling factor. Students responded to the items of the instrument

on a 5-point Likert scale, with 0 signifying a strong disagreement with an item

and 4 signifying a strong agreement with an item. Because the items in the

opinion factor were negatively stated and items in the feeling factor were

positively stated, low scores meant positive opinions but negative feeling about

biology whereas high scores meant negative opinions but positive feeling about

biology. The two factors were negatively related with a weak correlation

coefficient, p = -.26, p < .01.

The feelings about biology were positive statements and all the opinions

about biology were negative statements. This is reflected in the negative

correlation between BSAI factor 1, opinions about biology, and BSAI factor 2,

61

feelings about biology. The variables produced by this instrument were pre-test

and post-test affective scores for the opinions factor and for the feelings factor.

Pretest scores were used to ascertain initial student opinions and feelings about

biology at the beginning of the academic year. The posttest scores were used to

ascertain flnal student opinions and feelings concerning biology at the end of the

academic year. The meaning attached to the Factor 1, opinions about biology,

scores was the lower the score the more positive the student's opinions about

biology. The meaning attached to the Factor 2, feelings about biology, scores

were the higher the score the more positive the student's feelings about biology.

Procedure

Data were collected in the students' regular classroom setting during their

normal 80-minute block schedule. Data were collected on September 22 and 23,

1997 and again on April 2 and 3,1998. All data collections were conducted by

either this researcher or his associate. Teachers were not involved in the data

collection processes. Prior to each data collection event, all students were

informed that their responses were confidential and would not affect their biology

grades. They were further assured that their teachers would never see any of

their responses or individual scores.

Data Analysis

The research questions involved variables at the individual student level

set In a classroom context. To measure these variables data were collected

62

using the QTI, BSAI, and BECE. The individual student was the appropriate

level of analysis because it was the individual student's perceptions of his/her

teacher's interpersonal teaching behaviors, which were assessed by the QTI.

The basis for the QTI Is Leary's communication theory concerning the interaction

between individuals. The communication patterns, which develop are contingent

on the personalities of everyone who are interacting (Wubbels, Creton, Levy &

Hooymayers, 1993). While student perceptions are formed in the context of a

classroom, each student have their own individual perception of the teacher's

interpersonal teaching behaviors. As Creton, Wubbels and Hooymayers (1993)

stated, "Teacher-student relationships are not deduced from psychosocial

backgrounds, but are seen as outcomes of a classroom system in which both

teacher and students take part" (p. 2). Reliability coefficients for student level

analysis were found to range from .76 to .88, while class level analysis was

associated with reliability coefficients ranging from .86 to .95 (Wubbels et al.,

1993). These scores indicate scale internal consistency. While class level

analysis was used by Wubbels et al. (1985) to insure credibility the student level

analysis was not considered inappropriate. Wubbels et al. preference for class

level analysis was in the context of providing teachers feedback from their

classes, which was not one of the concerns of this study.

Because the data collected were interval in nature, the proposed

hypotheses were tested using step-wise multiple regression. For hypothesis one

all retained variables were used as predictors and student posttest scores on the

BECE were used as criteria variables. All retained variables were used as

63

predictors and student posttest scores on the BSAI were used as criteria

variables. In a step-wise multiple regression equation, all variables were

assessed to ascertain if they met the parameters of p value for inclusion in the

step-wise multiple regression equation. All variables had to have a p of .05 or

less to initially enter the equation and a p of .10 or more to leave the equation.

The demographic data (i.e., gender, ethnicity, and student at-risk status) were

coded using dummy variables (0 and 1) to convert the data and allow its

inclusion into the step-wise multiple regression analysis. The dummy values were

coded in the following manner. Female was coded 0 and male 1, majority was

coded 0 and minority 1, and not at risk was coded 0 and at risk 1. The

participants provided all demographic data when they signed their consent forms.

All demographic data were then examined using step-wise multiple regression

modeling. The correlations between scales of the QTI were also examined to

determine if multi-linearity was a concern in this research study. For hypothesis

one all retained variables were used to generate predicted BECE post-test

scores. The pretest BECE scores were used as indicators of student prior

knowledge concerning biology. The same procedure was used to generate

predicted BSAI post-test scores for hypothesis 2. The pretest BSAI scores were

used as indicators of student initial feelings and opinions about biology.

64

CHAPTER IV

RESULTS

The purpose of this was to investigate the effects of student perceptions of

their biology teacher's interpersonal teaching behaviors on student achievement

and affective learning outcomes. This study also investigated whether student

perceptions of their biology teacher's interpersonal teaching behaviors as

measured by the QTI scales were significant predictors of student achievement

and affective learning outcomes.

Analyses were conducted in order to investigate the effects of differing

student perceptions of their biology teacher's interpersonal teaching behaviors as

measured by the QTI scales on post-test student achievements and post-test

affective learning outcomes. Four main dependent variables (post-test, overall,

post-test, high level; post-test, low level; and post-test affective learning

outcome) were used to measure the effects of student perceptions of their

biology teacher's Interpersonal teaching behaviors as measured by the eight QTI

scales.

This chapter presents the descriptive statistics for this sample and

discusses the hypothesis testing for each of the research hypothesis. The

SPSS-PC system was used to analyze the data

65

Hypotheses

The following hypotheses were tested:

HOI: None of the eight scales of the QTI are significantiy related to



significantly related to higher student achievement outcomes.


significantiy related to lower student achievement outcomes.


significantiy related to overall student achievement outcomes.

H02: None of the eight scales of the QTI are significantiy related to






Descriptive statistics

Descriptive statistics for all independent and dependent variables used in

this study were calculated and presented in Table 1. The following abbreviations

were used in this dissertation: (1) QTI scales; Admon=Admonishing,

Dissat=Dissatisfied, Helpful=Helpful/friendly, Leader=Leadership, Strict=Strict,

Uncer=Uncertain, and Stu_resp=Student responsibility/freedom:

66

(2) Demographics; At_risk= At risk to fail, Ethnlcity=Ethnicty, Gender=Gender:

(3) BSAI scales; BSAI_F1=Spring data Factor 1, BSAIF1F=Fall data Factor 1,

BSAI_F2=Spring data Factor 2, BSAIF2F=Fall data Factor 2: (4) Pre and Post

test scores: POSTTH=Post test high level, POSTTTL=post test low level,

POSTTT=Post test overall level, PRETTH= Pre-test high level, PRETTL=Pre-test

low level, PRETTT=Pre-test overall level.

Table 1: Descriptive Statistics for independent and dependent variable

Variables N

Admon 1' Dissat 1' Helpful 1' Leader 1' Strict 1' Uncer 1' Stu-resp 1' At_risk 1' Ethnicity 1' Gender 1' BSAI F1 1' BSAIF1F 1' BSAI F2 1' BSAIF2F 1' POSTTH 1' POSTTL 1' POSTTT 1' PRETTH 1' PRETTL 1' PRETTT 1' Valid N 1' (listwise)

Mean

11 .22325 11 .19094 11 .81982 11 .77413 11 .41066 11 .18211 11 .46425 11 .28 11 .27 11 .50 11 .41126 11 .6288 11 .57207 11 .5507 11 .76383 11 .75051 11 .75581 11 .69813 11 .63888 11 .65359

Std. Dev.

.14264

.12642

.13204

.13707

.13149

.15167

.13509

.45

.45

.50

.17885

.1429

.23328

.2445

.20512

.15291

.15519

.22738

.15225

.16083

Var.

.0203

.0160

.0174

.0188

.0173

.0230

.0183

.203

.199

.252

.0312

.0204

.0544

.0598 .0421 .0234 .0241 .0517 .0232 .0259

Skewness

1.107 1.145 -.822 -.611 .280

1.499 .159 .997

1.049 .018 .854

-.364 -.289 -.333 -.985 -.793 -.862 -.432 -.116 -.247

Kurtosis

1.976 2.348

.459 .014 -.162 3.152 -.449

-1.024 -.917

- 2.037 1.216 -.326 -.640 -.228 .313 .068 .162

-.960 -.964 -.765

67

Univariate analyses for all variables were conducted only after preliminary

analyses were preformed to examine the assumptions of univariate analysis to

insure the appropriateness of this procedure. First, near normal distributions of

most dependent and independent variables were shown. While variables QTI

scales admonish, dissatisfied, and uncertain were positively skewed ranging from

1.049 to 1.499, they were not extreme enough to violate the univariate

assumption of normal distribution. Even if it was extreme enough to cause a

concern that the assumption was violated. Glass, Peckham, and Sanders (1972)

found that skewness has only a slight effect (generally only a few hundredths) on

the level of significance or power. Second, the assumption of homogeneity of

variance was assessed by univariate methods of Cochran's-C test and Bariett

Box F test. The null hypothesis of homogeneity of variance was retained.

Assumptions Concerning the Use of Multiple Regression Modeling

The basic assumptions of the multiple regression model are: (1) all

variables must be measured at the interval level and without error (measurement

error), (2) each independent variable's mean error value is zero (intercept error),

(3) each set of independent variables' error variances are constant

(heteroscedasticity), (4) for any two sets of independent variables there is no

auto-correlation (heteroscedasticity), (5) each independent variable is

uncorrelated with the error term (specification error), (6) perfect colllnarity is not

present (perfect linear relationship), and (7) each independent variable is

68

normally distributed (statistical significance). These assumption are related to

the population under study and not the sample taken from that population. But,

because the sample is representative of the population it was drawn from, the

assumptions of the multiple regression model can be determined from the

samples descriptive statistics with moderate certainty. All variables except three

demographic variables were measured at the Interval level and with minimal

error. The demographic variables were converted using dummy variables and

they were inserted into the multiple regression models. Using residual versus

predicted values from the post-test high level resulted in an error plot was oval in

nature and with an approximate error value of zero. This was also true for the

other four multiple regression models used for variables, post-test, low level;

post-test, overall level, opinions about biology, and feelings about biology. The

equality of variances were plotted and found to be congruent in each of the

multiple regression models used in this study. The models' Durbin-Watson

statistics ranged from 1.535 to 2.040 which is indicative of the presence of minor

positive auto-correlation to extremely minor negative auto-correlation. Because

error values were found to be approximately zero and error variances were found

to be congruent, the effect of auto-correlation is minimal. It was found that each

independent variable was found to be uncorrelated with the error term in each

multiple regression model. While the independent variables were correlated to

each other none of the variables were perfectiy correlated. Each independent

variable was found to be normally distributed with the presence of minor

skewness in the data.

69

statistical Analysis

First, a step-wise multiple regression equation was used to ascertain

which of the independent variables were significant predictors of the dependent

variable, post-test, high level scores. The independent variables that were

entered into the equation were: (1) BECE pre-test overall scores, (2) QTI scales

(except understanding), (3) BSAI factor 1 and 2 (pre-test and posttest data), and

(4) student demographic values; at-risk, ethnicity, and gender. The BECE

pretest high and low scores were eliminated due to their high correlations to each

other (r=.723, p=.01). Both pretest high and low levels were significantiy

correlated to the pretest overall scores (respectively: r=.884 and r=.960, p=.01).

The variable pretest, overall scores was retained because it was more closely

correlated to post-test high level than were pretest high and low level scores.

QTI scale understanding was eliminated due to its high correlation to QTI scale

helpful/friendly (r=.795, p=.01). Helpful/friendly was retained because it was

more closely correlated to all five dependent variables than was understanding.

The elimination of these three variables removed the threat of multi-linearity to

each multiple regression model.

Sub-hypothesis 1.1 stated that none of the eight QTI scales are

significantiy related to student high level achievement outcomes. The multiple

regression model used to test hypothesis 1.1 consisted of a dependent variable,

post-test at high level and Independent variables pretest overall score, QTI scale

uncertain, ethnicity, and QTI scale leadership. The descriptive statistics of the

predictor variables are reported In Table 2.

70

Table 2: Descriptive Statistics of retained predictor variables for sub-hypothesis 1.1

Variables

PRETTT Uncer Ethnicity Leader Valid N (listwise)

N

111 111 111 111

Mean

.65359

.18211

.27

.77413

Std. Dev.

.16083

.15167

.45

.13707

Var.

.0287

.0230 .199 .0188

Skewness

-.247 1.499 1.049 -.611

Kurtosis

-.765 3.152 -.917 .014

An ANOVA was conducted (see Table 3) to ascertain how well the data fit

the model. The results of the ANOVA revealed that the model fit the data with a

L(4, 106) = 24.852, E<.001.

Table 3: ANOVA test for model/data fit of sub-hypothesis 1.1

Model

A. Regression Residual

Total B. Regression

Residual Total

C. Regression Residual

Total D. Regression

Residual Total

SS

1.793 2.835 4.628 2.017 2.611 4.628 2.150 2.478 4.628 2.240 2.388 4.628

Df

1 109 110

2 108 110

3 107 110

4 106 110

Mean Sq.

1.793 .026012

1.008 .024182

.717

.02316

.560

.02253

F

68.936

41.709

30.952

24.852

Sig.

.001

.001

.001

.001

A: Predictors: PRETTT B: Predictors: PRETTT, Uncertain C: Predictors: PRETTT, Uncertain, Ethnicity D: Predictors: PRETTT, Uncertain, Ethnicity, Leadership

Students pretest overall score was selected to enter the regression model.

A significant portion of the post-test, high level learning variance was explained

71

by the pretest, R-square = .387, F_(1, 109) = 68.936, p < .001. The QTI scale

uncertain entered the equation and explained 4.8% of additional variance of high

level post-test scores, R-square = .435, F (2, 108) = 41.709, p < .001. The

relationship between QTI scale uncertain and achievement were negatively

correlated with a B-value of -.302, that is, with an increase of uncertainty in

teacher behavior, students' academic achievement decreases. Ethnicity

explained an additional 2.9% of the variance, R-square = .464, F (3, 107), p <

.001. The relationship was also negative with a B-value of -.083, which indicated

that majority students achieved higher in the post-test high level of biology than

minority students. The last significant predictor for the high level post-test score

was QTI scale leadership which explained an additional 1.9% of the variance of

the dependent variable, R-square = .483, F (4, 106), p < .001. The relationship

between leadership in teaching behaviors and student achievement was negative

with a B-value of-.236, that is, the stronger the leadership a teacher manifested

in his/her teaching behaviors, the lower his/her students achievement in biology

class. Other independent variables were excluded from the regression equation

because their insignificant relationships with the high level post-test scores B-

values, beta values and tests for significance are presented in Table 4.

72

Table 4: Standardized beta coefficients of the retained independent variables for sub-hypothesis 1.1 model

Model

A PRETTT B PREI11

Uncer C PRETTT

Uncer Ethnicity

D PRETTT Uncer Ethnicity Leader

Unstandard Coefficient B

.794

.744 -.302 .664 -.298 -.08336 .627 -.394 -.07565 -.236

Std. Error

.096

.094

.099

.098

.097

.035

.098

.107

.034

.119

Standard Coefficient Beta

.622

.584 -.223 .521 -.220 -.181 .491 -.291 -.165 -.156

t

8.303 7.951 -3.043 6.807 -3.067 -2.400 6.393 -3.673 -2.194 -1.993

Sig.

.001

.001

.003

.001

.003

.018

.001

.001

.030

.049

The multiple regression model the represents sub-hypothesis 1.1 is

Post-test high = .491 (Pretest, overall) + (-.291)(Uncertain) +

(-.165)(Ethniclty) + (-.156)(Leadership). (1)

Second, a step-wise multiple regression equation was used to ascertain


variable, post-test, low level scores. The independent variables that were





other (r=.723, p=.01). Both pretest high and low levels were significantiy

73




QTI scale understanding was eliminated due to its high con-elation to QTI scale

helpful/friendly (r=.795, 2=.01). Helpful/friendly was retained because it was





significantiy related to student low level achievement outcomes. The multiple


post-test at low level and Independent variables pretest overall score, and QTI

scale uncertain. The descriptive statistics of the predictor variables are reported

In Table 5.


Variables

PRETTT Uncer Valid N (listwise)

N

111 111 111

Mean

.65359

.18211

Std. Dev.

.16083

.15167

Var.

.0287

.0230

Skewness

-.247 1.499

Kurtosis

-.765 3.152

74



L(2, 108) = 44.956, e<.001.


Model


Total B. Regression

Residual Total

SS

1.052 1.519 2.572 1.168 1.404 2.672

Df

1 109 110

2 108 110

Mean Sq.

1.052 .01394

.584

.01300

F

75.499

44.956

Sig.

.001

.001

A: Predictors: PRETTT B: Predictors: PRETTT, Uncertain



by the pretest, R-square = .409, F (1, 109) = 75.499, p < .001. The QTI scale




correlated with a B-value of-.217, that is, with an increase of uncertainty in

teacher behavior, students' academic achievement decreases. Other

independent variables were excluded from the regression equation because their

insignificant relationships with the high level post-test scores B-values, beta

values and tests for significance are presented in Table 7.

75

Table 7: Standardized beta coefficients of the retained independent variables for sub-hypothesis 1.2 model.

Model

A PRETTT B PRETTT

Uncer


.608

.573 -.217

Std. Error

.070

.069

.073


.640

.602 -.216

t

8.689 8.342 -2.987

Sig.

.001

.001

.003


Post-test low = .602 (Pretest, overall) + (-.216)(Uncertain). (2)

Third, a step-wise multiple regression equation was used to ascertain


variable, post-test, overall level scores. The independent variables that were





other (r=.723, Qr.O^). Both pretest high and low levels were significantly




QTI scale understanding was eliminated due to its high correlation to QTI scale

helpful/friendly (r=.795, p=.01). Helpful/friendly was retained because it was

76





significantiy related to student overall level achievement outcomes. The multiple


post-test at low level and independent variables pretest overall score, and QTI

scale uncertain. The descriptive statistics of the predictor variables are reported

in Table 8.


Variables

PRETTT Uncer Valid N (listwise)

N

111 111 111

Mean

.65359

.18211

Std. Dev.

.16083

.15167

Var.

.0287

.0230

Skewness

-.247 1.499

Kurtosis

-.765 3.152



F_(2, 108) = 56.890, p<.001.

77


Model


Total B. Regression

Residual Total

SS

1.217 1.432 2.649 1.359 1.290 2.649

Df

1 109 110

2 108 110

Mean Sq.

1.217 .01314

.680

.01195

F

92.611

56.890

Sig.

.001

.001

A: Predictors: PRETTT

B: Predictors: PRETTT, Uncertain



by the pretest, R-square = .459, L (1 . 109) = 92.611, p < .001. The QTI scale




correlated with a B-value of-.241, that is, with an increase of uncertainty In

teacher behavior, students' academic achievement decreases. Other

independent variables were excluded from the regression equation because their

insignificant relationships with the high level post-test scores B-values, beta

values and tests for significance are presented in Table 10.

78


Model

A PRETTT B PRETTT

Uncer


.654

.614 -.241

Std. Error

.068

.066

.070


.678

.637 -.235

t

9.623 9.339 -3.450

Sig.

.001

.001

.003


Post-test low = .637 (Pretest, overall) + (-.235)(Uncertain). (3)

Fourth, a step-wise multiple regression equation was used to ascertain


variable, BSAI Factor 1 (opinions about biology) scores. The independent

variables that were entered into the equation were: (1) BECE pre-test overall

scores, (2) QTI scales (except understanding), (3) BSAI factor 1 and 2 (pre-test

and posttest data), and (4) student demographic values; at-risk, ethnicity, and

gender. The BECE pretest high and low scores were eliminated due to their high

correlations to each other (r=.723, p=.01). Both pretest high and low levels were

significantiy correlated to the pretest overall scores (respectively: r=.884 and

r=.960, p=.01). The variable pretest, overall scores was retained because It was

more closely correlated to post-test high level than were pretest high and low

level scores. QTI scale understanding was eliminated due to its high correlation

to QTI scale helpful/friendly (r=.795, p=.01). Helpful/friendly was retained

79

because it was more closely correlated to all five dependent variables than was

understanding. The elimination of these three variables removed the threat of

multi-linearity to each multiple regression model.

Sub-hypothesis 2 stated that none of the QTI scales were significantiy

related to student affective learning outcomes. The results of factor analysis

revealed that the BSAI was composed of two factors named opinions and

feelings about biology. These two factors were significantiy related to each other

(r= -.261, p = .01). This resulted in two step-wise multiple regression equations,

one for each factor. The multiple regression model used to test hypothesis 2.1,

(none of the eight scales of the QTI are significantly related to student opinions

about biology), consisted of a dependent variable. Factor 1 opinions about

biology and independent variables QTI scale dissatisfied and BSAI Factor 2

feelings about biology. The descriptive statistics of the predictor variables are

reported In Table 11.


Variables

Dissat BSAI F2 Valid N (listwise)

N

111 111 111

Mean

.19094

.57207

Std. Dev.

.12642

.23328

Var.

.0160

.0544

Skewness

1.145 -.289

Kurtosis

2.348 -.640

80

An ANOVA was conducted (see Table 12) to ascertain how well the data

fit the model. The results of the ANOVA revealed that the model fit the data with

aL(2 , 108) = 14.029, p<.001.


Model


Total B. Regression

Residual Total

SS

.477 3.042 3.518

.726 2.793 3.518

Df

1 109 110

2 108 110

Mean Sq.

.477

.02791

.363

.02586

Li.

17.086

14.029

Sig.

.001

.001

A: Predictors: Dissatisfied B: Predictors: Dissatisfied, BSAI F2


A significant portion of the student opinions about biology variance was explained

by QTI scale dissatisfied, R-square = .136, F (1, 109) = 17.086, p < .001. BSAI

Factor 2 (feelings about biology) entered the equation and explained .071 of

additional variance of students feelings about biology scores, R-square = .207, F

(2, 108) = 14.029, p < .001. The relationship between QTI scale dissatisfied and

student opinions about biology scores were positively corelated with a B-value of

.452, that is, with an increase of dissatisfaction in teacher behavior, students'

opinions about biology increased. The relationship between BSAI Factor 2

(feelings) and student opinions about biology, BSAI Factor 1, were negatively

correlated with a B-value of -.207, that is, with an increase of student feelings

81

about biology, students' opinions about biology decreased. Other independent

variables were excluded from the regression equation because their Insignificant

relationships with the high level post-test scores B-values, beta values and tests

for significance are presented in Table 13.


Model

A Dissat B Dissat

BSAI F2


.521

.452 -.207

Std. Error

.126

.123

.067


.368

.319 -.270

t

4.133 3.662 -3.102

Sig.

.001

.001

.003


BSAI Factor 1 = .368 (Dissatisfied) + (-.270)(BSAI Factor 2, post-test). (4)

Sub-hypothesis 2.2 stated that none of the eight scales of the QTI are

significantly related to student feelings about biology. The multiple regression

model used to test hypothesis 2.2 consisted of a dependent variable, feelings

about biology and independent variables QTI scale helpful, BSAIF2F, BSAI_F1,

ethnicity, and QTI scale dissatisfied. The descriptive statistics of the predictor

variables are reported in Table 14.

82


Variables N

Helpful 1' BSAIF2F 1' BSAIF1F 1' Ethnicity 1' Dissat 1' Valid N 1' (listwise)

Mean

11 .81982 11 .5507 11 .6288 11 .27 11 .19094

Std. Dev.

.13204

.2445

.1429 -45 .12642

Var.

.0174

.5977 .0204 .199 .0160

Skewness

-.822 -.333 -.364 1.049 1.145

Kurtosis

.459 -.228 -.326 -.917 2.348

An ANOVA was conducted (see Table 15) to ascertain how well the data

fit the model. The results of the ANOVA revealed that the model fit the data with

aL(5 , 105) = 17.857, p< .001.

83


Model


Total B. Regression

Residual Total

C. Regression Residual

Total D. Regression

Residual Total

E. Regression Residual

Total

SS

1.644 4.342 5.986 2.148 3.838 5.986 2.412 3.574 5.986 2.607 3.379 5.986 2.751 3.235 5.986

Df

1 109 110

2 108 110

3 107 110

4 106 110

5 105 110

Mean Sq.

1.644 .03984

1.074 .03554

.804

.03341

.652

.03187

.550

.03081

F

41.256

30.221

24.064

20.451

17.857

Sig.

.001

.001

.001

.001

.001

A: Predictors: HelpfijI/friendly B: Predictors: Helpful/friendly, C: Predictors: Helpful/friendly, D: Predictors: Helpful/friendly, E: Predictors: Helpful/friendly,

BSAIF2F BSAIF2F, BSAI_F1 BSAIF2F, BSAI_F1, Ethnicity BSAIF2F, BSAI_F1, Ethnicity. Dissatisfied

QTI scale helpful/friendly was selected to enter the regression model. A

significant portion of the student opinions about biology variance was explained

by QTI scale helpful/fiiendly, R-square = .275, F_(1. 109) = 41.256, p < .001. The

relationship between QTI scale helpful/friendly and student feelings about biology

scores were positively correlated with a B-value of .934, that is, with an increase

of helpfulness/friendly actions in teacher behavior, students' feelings about

biology increased. BSAI Factor 2 (pre-test data, feelings about biology) entered

the equation and explained .124 of additional variance of students feelings about

biology scores, R-square = .399, F (2,108) = 30.221, p < .001. The relationship

between BSAI Factor 2 (pre-test data, feelings about biology) and student

84

feelings about biology scores were positively correlated with a B-value of .265,

that is, the more prior positive feelings about biology the student has the higher

his/her post-test feelings about biology. BSAI Factor 1 (post-test data, opinions

about biology) entered the equation and explained .094 of additional variance of

students feelings about biology scores, R-square = .493, F (3, 108) = 24.064, p <

.001. The relationship between BSAI Factor 1 (post-test data, opinions about

biology) and student feelings about biology scores were negatively correlated

with a B-value of-.345, that is, the more negative opinions about biology the

student has the lower his/her post-test feelings about biology. Ethnicity entered

the equation and explained .062 of additional variance of students feelings about

biology scores, R-square = .555, F (4,108) = 20.451, p < .001. The relationship

was positive with a B-value of .09561, that is, minority students had more positive

feelings about biology than did majority students. Lastiy, QTI scale dissatisfied

entered the equation and explained .009 of additional variance of students

feelings about biology scores, R-square = .564, F (5, 108) = 17.857, p < .001.

The relationship between QTI scale dissatisfied and student feelings about

biology scores were positively correlated with a B-value of .341, that is, with an

increase of dissatisfied actions in teacher behavior, students' feelings about

biology increased. Other independent variables were excluded from the

regression equation because their insignificant relationships with the high level

post-test scores B-values, beta values and tests for significance are presented in

Table 16.

85


Model

A Helpful B Helpful

BSAIF2F C Helpful

BSAIF2F BSAI F1

D Helpful BSAIF2F BSAI_F1 Ethnicity

E Helpful BSAIF2F BSAI_F1 Ethnicity Dissat


.926

.890

.278

.788

.286 -.284

.800

.260 -.283

.095632

.934

.265 -.345

.09561

.341

Std. Error

.144

.136

.074

.137

.072

.101

.134

.071

.099

.038

.146

.069

.101

.038

.158


.524

.504

.291

.446

.300 -.218 .453 .273 -.217 .183 .529 .278 -.265 .183 .185

t

6.423 6.519 3.767 5.745 3.996

-2.809 5.964 3.684

-2.863 2.478 6.407 3.816

-3.409 2.520 2.158

Sig.

.001

.001

.001

.001

.001

.001

.001

.001

.005

.015

.001

.001

.001

.013

.033


BSAI Factor 2 = .529(Helpful) + .278(BSAIF2F) + (-.265)(BSAI_F1)

+ .183(Ethnicity) + .185(Dissatisfied). (5)

Summarv

This chapter presented the statistical analysis procedures used in this

research and the data obtained from those analyses.

Using step-wise multiple regression techniques, the null hypothesis of

student perceptions as measured by the QTI effects on student high level

achievement was rejected. Students who perceived their biology teacher to be

uncertain or a leader scored significantiy lower than students who did not

86

perceive their biology teacher to be uncertain or a leader. Student ethnicity was

also significantiy negatively related to student high level achievement.


student perceptions as measured by the QTI effects on student low level


uncertain scored significantiy lower than students who did not perceive their

biology teacher to be uncertain or a leader.


student perceptions as measured by the QTI effects on student overall level


uncertain scored significantly lower than students who did not perceive their

biology teacher to be uncertain or a leader. In all three cognitive sub-hypotheses

prior knowledge as measured by pretest overall scores was the leading predictor

of scores as measured by the post-test high, low, and overall levels of

achievement. Plus, in all three cognitive sub-hypotheses QTI scale uncertain as

measured by the QTI was the second leading predictor of post-test scores as

measured by the post-test high, low, and overall levels of achievement.


student perceptions as measured by the QTI effects on student affective learning

outcomes as assessed by the BSAI Factor 1 (opinions about biology) was

rejected. Students who perceived their teacher to be dissatisfied scored higher

on the post-test BSAI Factor 1. Conversely, students who have positive feelings

about biology scored lower on the post-test BSAI Factor 1.

87


student perceptions as measured by the QTI effects on student affective learning

outcomes as assessed by the BSAI Factor 2 (feelings about biology) was

rejected. Students who perceived their teacher to be helpful scored higher on

the post-test BSAI Factor 2. Also students who had prior positive feelings about

biology scored higher on the post-test BSAI Factor 2. Students with poor

opinions about biology scored lower on the post-test BSAI Factor 2. Minority

students scored higher on the post-test BSAI Factor 2 and students that

perceived their biology teacher to be dissatisfied scored higher on the post-test

BSAI Factor 2.

88

CHAPTER V

DISCUSSIONS, CONCLUSIONS, AND

RECOMMEDATIONS

Summarv of Studv

The researcher's primary goal in this study was to investigate the

predictive power and effect of student perceptions of their biology teacher's

interpersonal teaching behaviors as measured by the QTI on student

achievement and affective learning outcomes. Student achievement

outcomes were assessed at three levels, high, low and overall.

Research indicated that students with differing perceptions of their

teacher's interpersonal teaching behaviors would differ in academic

achievement and in affective learning outcomes (see Brekelmans, 1990;

Fisher, 1995; Henderson, Fisher & Fraser, 1995; Tuckman, 1980; Wubbels,

Brekelmans, Creton & Hooymayers, 1989; Wubbels, Brekelmans &

Hooymayers, 1991; Wubbels, Creton & Holvast, 1988; Wubbels, Creton &

Hooymayers, 1985, 1987; Wubbels, Creton, Levy & Hooymayers, 1993;

Wubbels & Levy 1989).

For example, Brekelmans, Wubbels, and Creton (1990) found that

there was a significant different effect for differing types of Interpersonal

teaching behavior scales students perceived to be present in regard to both

cognitive and affective learning outcomes. Specifically the Brekelmans et al.

(1990) study found that students who perceived their teacher to be a leader

89

achieved higher cognitive outcomes than students who did not perceive their

teachers to be leaders. In this study, student outcomes were measured with

a standardized and internationally developed test for physics subject matter.

Conversely, Fisher, (1995) and Henderson, Fishers, and Fraser, (1995) found

that students who perceived their biology teacher to be uncertain scored

significantiy lower in cognitive outcomes than students who did not perceive

their teacher to be uncertain.

Based on previous research (see Wubbels, Brekelmans, Creton &

Hooymayers, 1989; Wubbels, Brekelmans & Hooymayers, 1991; Wubbels,

Creton & Holvast, 1988; Wubbels, Creton & Hooymayers, 1985,1987;

Wubbels & Levy 1989) and the interpersonal teaching behaviors model, it

was expected that students that perceived tiieir teacher to be strict, a strong

leader, to be helpful/friendly in nature, and to be understanding would have

significantly higher academic achievement outcomes than students that did

not perceive their teacher in the same manner. These same students will

also have higher affective learning outcomes if they perceive their teacher to

be a leader, helpful, understanding and giving students responsibility and

freedom to control tiieir learning activities.

The predictions of this study were partially supported by the data.

Sub-hypotheses 1.1, was rejected because the relationship between

uncertainty and high level achievement scores was negatively correlated, that

is, with the increase of uncertainty In teacher's behavior, students' high level

achievement scores decreased. QTI scale uncertainty was the second most

90

powerful predictor found in the step-wise multiple regression model (see

equation 1) which was developed to explain the relationship between

dependent variable post-test, high level and its independent predictor

variables. QTI scales entered the high level achievement regression equation

and explained 4.8% of the variance in the post-test scores.

Sub-hypothesis 1.1 was also rejected because there was a significant

relationship between teacher leadership behaviors and student high level

achievement scores. This relationship was also negative in nature as an

increase in teacher leadership behaviors was associated with decreased

student high level achievement scores. These findings conti-adict the

research that reports leadership teacher behaviors are associated with higher

students achievement scores (see Brekelmans, Wubbels, & Creton, 1990;

Fisher, 1995; Henderson, Fisher, & Fraser, 1995; Wubbels, Brekelmans,

Creton, & Hooymayers, 1989Wubbels, Creton, & Hooymayers, 1985;

Wubbels, Creton, Levy, & Hooymayers, 1993). The number of participants in

these reference studies greatiy exceeded the number of participants in this

study. The leadership scale Cronbach alphas found in the previous research

ranged from .80 to .89, while in this study the leadership Cronbach alpha was

.69. These data cast doubt on the predictive power of QTI scale leadership

within tiie parameters of this study.

Sub-hypotheses 1.2 was rejected because the relationship between

uncertainty and low level achievement scores was negatively correlated, tiiat

is, with an increase of uncertainty in teacher's behavior, students' low level

91

achievement scores decreased. QTI scale uncertainty was the second most

powerful predictor found in the step-wise multiple regression model (see


dependent variable post-test, low level and its independent predictor

variables. QTI scales entered the low level achievement regression equation



uncertainty and overall level achievement scores was negatively correlated,

that is, with an increase of uncertainty in teacher's behavior, students' overall

level achievement scores decreased. QTI scale uncertain was the second

most powerful predictor found in the step-wise multiple regression model (see


dependent variable post-test, overall level and its independent predictor

variables. QTI scales entered the low level achievement regression equation


These findings which led to the rejection of sub-hypotheses 1.1, 1.2,

and 1.3 support the research that reports that uncertain teacher behaviors are

associated with lower student achievement scores (see Brekelmans,

Wubbels, & Creton, 1990; Fisher, 1995; Henderson, Fisher, & Fraser, 1995;

Wubbels, Brekelmans, Creton, & Hooymayers, 1989; Wubbels, Creton, &

Hooymayers, 1985; Wubbels, Creton, Levy, & Hooymayers, 1993).


QTI scale dissatisfied and higher negative opinions about biology was

92

positively correlated, that is, with an increase of dissatisfaction in teacher's

behavior, students' negative opinions about biology increased. QTI scale

dissatisfied was the most powerful predictor found in the step-wise multiple

regression model (see equation 4) which was developed to explain the

relationship between dependent variable post-test, opinions about biology

and its independent predictor variables. QTI scale dissatisfied entered the

regression equation and explained 13.6% of the variance in the post-test

opinions scores.


QTI scales helpful/friendly and dissatisfied and higher positive feelings about

biology was positively correlated, that Is, with an increase of

helpfulness/friendly behavior or dissatisfaction in teacher's behavior, students'

positive feelings about biology increased. QTI scale helpful/friendly was the

most powerful predictor found In the step-wise multiple regression model (see


dependent variable post-test, feelings about biology and Its independent

predictor variables. QTI scale helpful/friendly entered the regression equation

and explained 27.5% of the variance In the post-test opinions scores. QTI

scale dissatisfied entered the regression equation and explained .9% of the

variance In the post-test opinions scores.

The findings which led to the rejection of sub-hypotheses 2.1 support

the research that reports that dissatisfied teacher behaviors are associated

with lower students achievement scores (see Brekelmans, Wubbels, &

93

Creton, 1990; Wubbels, Brekelmans, Creton, & Hooymayers, 1989; Wubbels,

Creton, & Hooymayers, 1985; Wubbels, Creton, Levy, & Hooymayers, 1993).

The findings which led to the rejection of sub-hypotheses 2.2 support the

research that reports that helpful/fi lendly teacher behaviors are associated

with higher students achievement scores (see Brekelmans, Wubbels, &


Creton, & Hooymayers, 1985; Wubbels, Creton, Levy, & Hooymayers, 1993).

But, the presence of QTI scale dissatisfied in both equation 4 and 5 is not

supportive of the interpersonal teaching behaviors research (see Brekelmans,

Wubbels, & Creton, 1990; Wubbels, Brekelmans, Creton, & Hooymayers,

1989; Wubbels, Creton, & Hooymayers, 1985; Wubbels, Creton, Levy, &

Hooymayers, 1993). Probable factors which have impacted the predictive

power In this research setting is the number of participants (n = 111) and the

QTI scale dissatisfied Cronbach alpha of .79. The most probable factor that

have impacted the predictive power of this research was the lack of variability

of teacher behaviors among the participant teachers. These factors led to the

probability that QTI scale dissatisfied Is not predictive of affective learning

outcomes in the study's setting.

The demographic variable ethnicity was a significant predictor in

equations 1 and 5. In the case of equation 1 (sub-hypothesis 1.1), ethnicity

has a negative standardized beta coefficient which indicates that persons of

minority ethnicity have lower predicted achievement high level scores than

persons of majority ethnicity. In the case of equation 5 (hypothesis 2.2),

94

ethnicity has a positive standardized beta coefficient, which indicates that

persons of minority ethnicity have higher predicted affective scores in the

area of feelings about biology. This is indicative of more positive affect

toward biology.

Limitations

After the completion of this study, the following limitations have been

acknowledged.

1. The sample of this study was from a rural, urban influenced west

Texas school; results of this study cannot be generalized to

urbanized or completely rural settings.

2. The BSAI was derived from a mathematics affective instrument.

The BSAI was found to be composed of two factors. Factor 1,

(opinions about biology) has a Cronbach alpha .52 and Factor 2,

(feelings about biology) has a Cronbach alpha of .77. These

reliabilities are particular to this sample under study.

Implications

Influence of student perceptions of their biology teacher on student achievement levels

A significant difference was found when the predictive power and

effects of the QTI scales on student achievement levels were tested.

Students that perceived their biology teacher to be uncertain scored lowered

on their BECE post-test at all three levels of assessment. The keywords used

95

to describe uncertain behaviors are; keep a low profile, apologized, wait and

see how the wind blows, admit one is in the wrong. The presence of this QTI

scale uncertainty in all three achievement assessments and Its negative

impact on all three scores led to the clear recommendation that all biology

teachers refrain form modeling behaviors associated with the uncertainty

scale keywords (see Brekelmans, Wubbels, & Creton, 1990; Wubbels,

Brekelmans, Creton, & Hooymayers, 1989; Wubbels, Creton, & Hooymayers,

1985; Wubbels, Creton, Levy, & Hooymayers, 1993). At the post-test, high

level of assessment the effect of student perceptions of teacher uncertainty

had a standardized beta coefficient of -.291. This means that if a student's

perceptions was one standard deviation above the mean group perception

then that student's predicted scores was .291 standard deviation below the

group's mean score for post-test, high level. In this study the predicted

student score would be reduced from 76.383% to 70.416% or approximately

one-half of a standard letter grade.


effects of prior knowledge about biology on student achievement levels was

tested. Students' prior knowledge, as assessed by the pre-test overall score,

was the most powerful predictor on all three levels of the BECE post-test.

Prior knowledge accounted for 38.7% of the variance in student high level

achievement scores. It accounted for 40.9% and 45.9% of the variances in

student low and overall achievement scores, respectively. These findings are

supportive of research which found student prior knowledge (Burkham, Lee, &

96

Smerdon, 1997; Stallings, 1976), or cognitive entry behaviors (Bloom, 1976)

are the best indicator of student achievement. Cognitive entry behaviors are

defined as"... those prerequisite types of knowledge, skills, and

competencies which are essential to the learning of a particular new task or

set of tasks" (Bloom, 1976, p. 42). Burkham et al. (1997) found that

achievement disadvantage by gender was a stable variable, those students

that did pooriy in 8* grade physical science classes did pooriy in 10 ^ grade

classes. While females were disadvantaged as a group both genders prior

scores were predictive of their future scores. Stated another way if a student

had a poor achievement record in the 8* grade science class he/she will

probably have a poor achievement level in 10* grade science class.

In this study the more prior knowledge about biology students

possessed, the higher their predicted scores on all three levels of assessment

were. Students that scored higher on the pre-test, overall scored higher on all

three post-test assessments. The presence of prior knowledge in all three

levels of assessment leads to the recommendation that science be vertically

Integrated K-12 by a scope and sequence document. This recommendation

is supported by research on the National Educational Longitudinal Study-

1988 data base. At the post-test, high level of assessment the effect of

student prior knowledge variable has a standardized beta coefficient of .491.

This means that if a student scores one standard deviation above the mean,

that student's predicted scores will be .491 standard deviation above the


97

student score would be raised from 76.383% to 86.434% or approximately

one standard letter grade.

Another significant difference found during the sub-hypothesis 1.1

testing was the discovery of the predictive power of QTI scale leadership.

Students that perceived their biology teacher to be a leader also scored lower

on their BECE post-test at the high level of assessment. The keywords used

to describe leadership behaviors are; notice what is happening, lead,

organize, give orders, set tasks, determine procedure, structure the

classroom-situation, explain, hold attention. These findings contradict the

research that reports leadership teacher behaviors are associated with higher

students achievement scores (see Brekelmans, Wubbels, & Creton, 1990;

Fisher, 1995; Henderson, Fisher, & Fraser, 1995; Wubbels, Brekelmans,

Creton, & Hooymayers, 1989; Wubbels, Creton, & Hooymayers, 1985;

Wubbels, Creton, Levy, & Hooymayers, 1993). The number of participants in

these research studies greatiy exceeded the number of participants in this

study. The leadership scale Cronbach alphas found in the previous research

ranged from .80 to .89, while In this study the leadership Cronbach alpha was

.69. Both of these findings cast doubt on the predictive power of QTI scale

leadership in this study.

Another significant difference found during the sub-hypothesis 1.1

testing was the discovery of the predictive power of ethnicity. Students

belonging to an ethnic minority scored lower on their BECE post-test at the

high level of assessment. The two minority groups present In the sample

98

used were Hispanic and African-American (n=1). The presence of ethnicity in

the high level achievement assessments leads to the following

recommendations; (1) all biology teachers should examine their interactions

with the differing ethnic groups and determine if there is disparate treatment,

and (2) all biology teachers should become aware of the tenets of multi

cultural teaching techniques. At the post-test, high level of assessment the

effect of student of minority ethnicity variable had a standardized beta

coefficient of -.165. This means that if a student belonged to an ethnic

minority that student's predicted score was .165 standard deviation below the


student score would be reduced from 76.383% to 72.999% or approximately

one-quarter of a standard letter grade.

At the post-test, low level of assessment the effect of student

perceptions of teacher uncertainty had a standardized beta coefficient of -

.216. This means that if a student perceptions of his/her teacher's uncertainty

was one standard deviation below the mean group perception that student's

scores was lowered .216 standard deviation below the group's mean score

for post-test, low level. In this study the predicted student score would be

reduced from lowered from 75.051% to 71.748% or approximately one-third

of a standard letter grade. At the post-test, low level of assessment the effect

of student prior knowledge variable has a standardized beta coefficient of

.602. This means that If a student scores one standard deviation above the

mean, that student's predicted scores will be .602 standard deviation above

99

the group's mean score for post-test, low level. In this study the predicted

student score would be raised from 75.051% to 84.256% or approximately

one standard letter grade.

At the post-test, overall score level of assessment the effect of student

perceptions of teacher uncertainty had a standardized beta coefficient of

-.235. This means that if a student perceptions was one standard deviation

above the mean group perception that student's score was lowered .235

standard deviation below the group's mean score for post-test, overall level.

In this study the predicted student score would be reduced from lowered from

75.584% to 71.934% or approximately one-third of a standard letter grade. At

the post-test, overall level of assessment the effect of student prior knowledge

variable has a standardized beta coefficient of .637. This means that If a

student scores one standard deviation above the mean, that student's

predicted scores will be .637 standard deviation above the group's mean

score for post-test, overall level. In this study the predicted student score

would be raised from 75.051% to 85.470% or approximately one standard

letter grade.

Influence of student perceptions of their biology teacher on student affective outcomes


effects of the QTI scales on student opinions about biology, BSAI, Factor 1,

affective learning outcomes was tested. Students that perceived their teacher

100

to be dissatisfied scored higher on Factor 1 of the BSAI. Item statements that

loaded on to Factor 1 were: item 1) Learning biology is mostiy memorizing,

item 4) There are always rules to use In solving a biology problem, item 5)

there are not many new discoveries in biology, item 6) biology is mostiy about

classifications and not about ideas, and item 7) in biology, it is more important

to understand why an answer is correct than it Is to know a lot of facts. The

presence of this QTI scale dissatisfied in both equation 4 and 5 contradicts

the interpersonal teaching behaviors literature (see Brekelmans, Wubbels, &


Creton, & Hooymayers, 1985; Wubbels, Creton, Levy. & Hooymayers, 1993).

Probable factors which have impacted the predictive power of QTI scale

dissatisfied in this research setting is the number of participants (n = 111) and

the QTI scale dissatisfied Cronbach alpha of .79. This leads to the probability

that QTI scale dissatisfied is not predictive of affective (opinions) learning

outcomes within the parameters of this study.


effects of BSAI, Factor 2, feelings about biology, on BSAI, Factor 1, opinions

about biology, affective learning outcomes was tested. Students that had a

positive affect toward their feelings about biology scored lower on Factor 1 of

the BSAI. Item statements that loaded on to Factor 2 were: item 2) biology is

interesting, item 8) biology is useful in everyday life, item 9) I would like to

have a job where I could use the things I have learned in biology, and item

10) biology is fun. Factor 2 represented positive feelings about biology. The

101

strength of the predictive power of BSAI, Factor 2 is reflected in its

standardized beta coefficient of -.270. This means that if a student has

positive feelings about biology which are one standard deviation above the

mean group's perception that student's scores was lowered .270 standard

deviation below the group's mean score on BSAI, Factor 1 opinions about

biology. In this study the predicted student score would be decreased from

.41126 to .36303. Because Factor 1, opinions about biology is a negative

factor the lower a student scores the more he/she has developed a positive

affect toward biology.

A significant difference was found when the predictive power of the

QTI scales on student feelings about biology, BSAI, Factor 2, affective

learning outcomes was tested. Students that perceived their teacher to be

helpful scored higher on Factor 2 of the BSAI. This factor represents positive

feelings about biology. The strength of the predictive power of QTI scale

helpfulness is reflected in its standardized beta coefficient of .529. This

means that if a student's perceptions of his/her teacher's helpfulness was one

standard deviation above the mean group perception that student's scores

was raised .529 standard deviation above the group's mean score on Factor

2 of the BSAI. In this study the predicted student score would be raised from

.57207 to .69548. This is representative of the development of a major

increase In positive affect toward biology. As discussed in the previous

section, the presence of QTI scale dissatisfied as a predictor of positive

feelings about biology is an indicator that the dissatisfied scale is not

102

predictive in the study. The presence of QTI scale dissatisfied also indicates

that the BSAI is in need of further refinement to increase its reliability and

validity. Because the QTI scale is not predictive in this study, predicted

student scores can not be generated.

Another significant difference was found when the predictive power

and effects of the pre-test BSAI, Factor 2, (feelings about biology), on post-

test BSAI, Factor 2, student feelings about biology, affective learning

outcomes were tested. Students that pre-tested for a positive affect toward

their feelings about biology scored higher on post-test BSAI, Factor 2. The

strength of the predictive power pre-test BSAI, Factor 2 is reflected in its

standardized beta coefficient of .278. This means that if a student's pre-test

positive feelings about biology were one standard deviation above the mean

group's perception that student's scores was raised .278 standard deviation

above the group's mean score on the post-test BSAI, Factor 2. In this study

the predicted student score would be increased from .57207 to .63692. This

is representative of the development of a more positive affect toward biology.

A significant difference was found when the predictive power of the

post-test, BSAI, Factor 1, opinions about biology, on BSAI, Factor 1, student

feelings about biology, affective learning outcomes was tested. Students that

had a negative affect toward their opinions about biology scored lower on

BSAI, Factor 2. The strength of the predictive power of BSAI, Factor 1 is

reflected in its standardized beta coefficient of -.265. This means that if a

student has negative opinions about biology which are one standard deviation

103

above the mean group's perception that student's scores was lowered .265

standard deviation below the group's mean score for Factor 2 of the BSAI. In

this study the predicted student score would be decreased from .57207 to

.51025. This is representative of the development of a more negative affect

toward biology.

Lastiy, another significant difference found was the significance of

ethnicity. Students that belong to an ethnic minority scored higher on Factor

2, BSAI affective assessment. The two minority groups present in the sample

were Hispanic and African-American (n=1). At the Factor 2, BSAI

assessment the effect of student of minority ethnicity variable had a

standardized beta coefficient of .183. This means that a minority student's

predicted score was .183 standard deviation above the group's mean score

for Factor 2, BSAI. In this study the predicted student score would be

increased from .57207 to .61476. This is representative of the development

of a more positive affect toward biology.

Theoretical Contributions

Research has not compared the influence of student perceptions of

American biology teachers and student academic achievement and affective

learning outcomes. Previous research was concentrated in other countries

which operated academically stratified educational systems (see Brekelmans,

1990; Fisher, 1995; Henderson, Fisher & Fraser, 1995; Wubbels,


104



Wubbels & Levy 1989). In this study, the researcher established the

predictive power of QTI scale uncertain on academic achievement outcomes.

The data on QTI scale leadership were suspect because they were not

supportive of the interpersonal teaching behaviors literature (see Brekelmans,

1990; Fisher, 1995; Henderson, Fisher & Fraser, 1995; Wubbels,




Wubbels & Levy 1989). The other six scales were not significantiy related to

student achievement. Because seven of the eight QTI scales were either

insignificant or suspect, the predictive power of the QTI on student

achievement in a West Texas 9th grade setting was not been established.

This research also established the predictive power of QTI scale

helpful/friendly and BSAI Factors on student affective learning outcomes. The

data on QTI scale dissatisfied were suspect because they did not support the

interpersonal teaching behaviors literature (see Brekelmans, 1990; Fisher,

1995; Henderson, Fisher & Fraser, 1995; Wubbels, Brekelmans, Creton &



Wubbels, Creton, Levy & Hooymayers, 1993; Wubbels & Levy 1989), and

because It was a positive predictor for both opinions and feelings about

105

biology. Because these two constructs are negatively correlated, a QTI scale

could predict one construct or the other, but not both constructs. The other

six scales were not significantiy related to student affective learning

outcomes. Because seven of the eight QTI scales were either insignificant or

suspect, the predictive power of the QTI on student affective learning

outcomes in a West Texas 9 '' grade setting was not established.

Using a sample from the Texan population of ninth grade biology

students, the results of this study were not consistent with the findings of

research from other countries (see Brekelmans, 1990; Fisher, Henderson, &

Fraser, 1995; Henderson, 1995; Wubbels, Brekelmans, Creton &



Wubbels, Creton, Levy & Hooymayers, 1993; Wubbels & Levy 1989). While

the interpersonal teaching behavior model does function as a predictor in

senior secondary grades (American equivalent 10-12 grades) as indicated by

the literature the exact composition of significant predictive QTI scales varied

with the nationality and grade of the sample. This also occurred in another

study concerning student perceptions of their biology teacher's interpersonal

teaching behaviors in the People's Republic of China and in the USA (Bilica &

Smith, 1998).

106

Educational Practice

The results of this study support the widely held assertion that

education and educational activities operate within a social/cultural context.

The findings of this research is not consistent with previous studies conducted

by Wubbels and associates (see Brekelmans, 1990; Fisher, 1995;

Henderson, Fisher & Fraser, 1995; Tuckman, 1980; Wubbels, Brekelmans,

Creton & Hooymayers, 1989; Wubbels, Brekelmans & Hooymayers, 1991;

Wubbels, Creton & Holvast, 1988; Wubbels, Creton & Hooymayers, 1985,

1987; Wubbels, Creton, Levy & Hooymayers, 1993; Wubbels & Levy 1989).

Interpersonal teacher behavior theory asserts that students' perceptions of

their teacher will be significantiy associated with student academic

achievement and affective learning outcomes. Before the model that is

derived from the interpersonal teacher behavior theory can be used, its

predictive power must be established in American educational settings. The

model also needs to be normed to that particular culture's or society's

students and teachers.

From the results of this study, it is not clear that interpersonal teacher

behavior model and theory offer Important avenues to effect student

academic achievement and affective learning outcomes in a West Texan

educational setting. Students who perceive their teachers not to be uncertain

scored significantiy higher on all post-test academic achievement

measurements than did students who perceived their teachers to be

uncertain. All other QTI scales were either not significant or suspect.

107

student ethnic status also was a negative indicator of success for

students assessed by tiie post-test, high level. Because ethnicity was a

significant predictor of lessened success at the post-test high level, biology

teachers should examine their interactions with their students and ascertain if

there are any disparate treatments occurring In their classroom and teachers

should become aware of the aspects of a multi-culturally based educational

system. Also, the school district should examine its policies to assure the

funding of schools Is not being administered In a manner that negatively

impacts minority students. Also school officials should insure that Hispanic

students are sufficientiy fluent in English to insure that the lack of

comprehension is not impeding their academic progress.

Lastly, this research supported Bloom's (1976), Burkham, Lee and

Smerdon (1997), and Stalling's (1976) studies that prior knowledge is a major

predictor of future academic achievement. These leads to the final

recommendation that all teachers should use various methods of

preassessment to determine the knowledge student bring to specific tasks

and areas of study. This will enable teachers to organize their classes in

such a manner as to insure that students with greater prior knowledge are

grouped with students with lessor amounts of prior knowledge.

Suggestions for Future Research

Based on the results of this study, future research is needed to

examine several questions. Some of these include the following.

108

1. The effect of student perceptions of their teacher's interpersonal

teaching behaviors on student academic achievement should be

investigated in a single subject setting within the context of one

grade with at least 20 teachers as participants.



investigated in an across subjects setting within the context of one

grade with at least 20 teachers as participants.


teaching behaviors on student affective learning should be

investigated in an across subjects setting within the context of one

grade.



investigated in an across grades (9-12) and across subjects

context.



investigated in an across subject setting within the context of one

grade.



109

investigated in an across grades (9-12) and across subjects

context.

7. The effect of locality (uriDan, rural, rural but urban influenced) on

student perceptions of their teacher's interpersonal teaching

behaviors in an across subjects setting with the context of a single

grade.

8. The effect of locality (urban, rural, rural but urban influenced) on

student perceptions of their teacher's interpersonal teaching

behaviors in an across subjects setting with the context of the 9-12

grade setting.

110

REFERENCES

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Brekelmans, M. (1989). Interpersonal Teacher Behavior in the Classroom. Utrecht, The Netheriands: W.C.C.

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Brekelmans, M., Levy, J. & Rodriguez, R. (1993). A typology of teacher communication style. In Th. Wubbels & J. Levy (Eds.). Do You Know What You Look Like? (pp. 46-55). London: The Falmer Press.

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Brekelmans, M., Wubbels, T. & Creton, H. (1990). A study of student perceptions of physics teacher behavior. Journal of Research in Science Teaching, 27(4), 335-350.

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Burkham, D. T., Lee, V. E. & Smerdon, B. A. (1997). Gender and science learning eariy in high school: subject matter and laboratory experiences. American Educational Research Journal. 34(2). 297-331.

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Burnett, R. W. (1957). Teaching Science in the Secondarv School. New York: Rinehart and Co.

Burton, W. H. (1952). The Guidance of Learning Activities. (2nd ed.). New York: Appleton-Century-Crofts.

Charter, W. W. & Waples, D. (1929). The Commonwealth Teacher-Training Study. Chicago: University of Chicago Press.

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Fisher, D. (1995). Interpersonal behavior in senior high school biology classes. Research in Science Education. 25(2). 125-133.

112

Flanders, N. A. (1960). Teacher Influence. Pupil Attitudes, and Achievement; Final Report. Cooperative Research Monograph No. 397. Washington, DC: Office of Education, U.S. Department of Health, Education and Welfare.

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Flanders, N. A. (1970). Analyzing Teaching Behavior. Reading, MA: Addison-Wesley Pub. Co.

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Haige, G. V. & Schmidt, W. (1956). The learning of subject matter in teacher centered and student centered classes. Journal of Educational Psychology. 47. 295-301.

Hart, F. W. (1934). Teachers and Teaching: By Ten Thousand High School Seniors. New York: Macmillan Co.

Henderson, D., Fisher, D. & Fraser, B. (1995, April). Associations Between Learning Environments and Student Outcomes in Biology. Paper delivered at the meetings of the American Educational Research Association, San Francisco, CA.

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Hill, P. W. (1984). Testing hierarchy in educational taxonomies: A theoretical and empirical investigation. Evaluation in Education. 8. 179-278.

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Kropp, R. P. & Stoker, H. W. (1966, February) The Construction and Validation of Tests of the Cognitive Processes as Described in the Taxonomv of Educational Obiectives. Research report, Institute of Human Learning, Tallahassee, FL: Florida State University (ED 010 044).

Leary, T. (1957). Interpersonal Diagnosis of Personality. New York: The Ronald Press Company.

Madaus, G. F., Woods, E. M., & Nuttall, R. L. (1973). A causal model analysis of Bloom's taxonomy. American Educational Research Journal. 10(4). 253-262.

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Rosenshine, B. (1976). The Psychology of Teaching Methods. In The 75th Yearbook of the National Society for the Studv of Education (pp. 335-371). Chicago: The University of Chicago Press.

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Rothman, A. I. (1969). Teacher characteristics and student learning. Journal of Research in Science Teaching. 6. 340-348.

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Soar, R. S. (1968). Optimum Teacher-Pupil Interaction for Pupil Growth. Educational Leadership. 26. (Research Supplement, 2 of 3), 275-280.

Talese, J & Olivarez, 0. (1995, Jan.). Psychometric Properties of High School Student's Views About Mathematics Learning and Instruction Instrument. Paper delivered at the Annual Meetings of the Southwestern Educational Research Association, Austin, TX.

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Tuckman, B. W. (1995). The interpersonal teacher model. The Educational Forum. 59(2). 177-185.

Tuckman, B. W. & Yates, D. (1980). Evaluating the student feedback strategy for changing teacher behavior. Journal of Education Research. 74(2). 74-77.

Veldman, D. J. & Brophy, J. E. (1974). Measuring teacher effects on pupil achievement. Journal of Educational Psychology. 66(3). 319-324.

Watzlawick, P., Beavin, J. H., & Jackson, D. D. (1967). Pragmatics of Human Communication: A Study of Interpersonal Patterns. Pathologies, and Paradoxes. New York: W. W. Norton & Company, Inc.

Wispe, L. G. (1951). Evaluating section teaching methods. Journal of Educational Research. 45. 161-186.

115

Wubbels, Th., Brekelmans, M., Creton, H. A. & Hooymayers, H. P. (1989). Teacher behavior style and learning environments. In Ch. Ellet & H. Waxman (Eds.), The Study of Learning Environments. 4. (pp. 1-12) Houston, College of Education.

Wubbels, Th., Brekelmans, M. & Hooymayers. (1991). In B. J. Fraser & H. J. Walberg (Eds.), Educational Environments: Evaluation. Assessment and Conseguences. pp. 141-160 Oxford, England: Pergamen Press.

Wubbels, Th., Creton, H. A. & Holvast, A. (1988). Undesirable classroom situations: A systems communication perspective. Interchange. 19(2). 25-40.

Wubbels, Th., Creton, H. A. & Hooymayers, H. P. (1985, March- April). Discipline Problems of Beginning Teacher. Interaction Teacher Behavior Mapped Out. Paper delivered at the meetings of the American Educational Research Association, New York, NY.

Wubbels, Th., Creton, H. A. & Hooymayers, H. P. (1987). A school based teacher induction programme. European Journal of Teacher Education. 10,(1), 811-91.

Wubbels, Th., Creton, H. A. & Hooymayers, H. P. (1992). Reviews of research on teacher communication styles with use of Leary's model. Journal of Classroom Interaction. 27(1). 1-11.

Wubbels, Th., Creton, H., Levy, J. & Hooymayers, H. (1993). The model for interpersonal teacher behavior. In Th. Wubbels & J. Levy (Eds.), Do You Know What You Look Like? (pp. 13-28). London: The Falmer Press.

Wubbels, Th. & Levy. J. (1989, March). A Comparison of Dutch and American Interpersonal Teaching Behavior. Paper delivered at the meetings of the American Educational Research Association, San Francisco, CA.

116

APPENDIX A

QUESTIONNAIRE ON TEACHER INTERACTION

117

QUESTIONN.AIRI ON TE.\CHER ENTER-\CTION

Last Six Digits of SS.\ Thus questionnaire asks you to describe your teacher's behavior. Your cooperation can help your teacher improve his.lier instruction. DO NOT WTUTE YOUR NAME, for your responses are coniidential. Thjs is NOT a test, " 'our teacher \\-ill NOT read your answers and they will NOT affect your grade. They will only recei% e the average results of the classes, NOT individual student scores.

For each sentence on the questionnaire, circle the lener which you think most applies to the teacher of your class. Please use only a pen. For example:

The teacher expresses himsel&Tierself clearly Alwa\s A ' B

Never D E

If \ou thirJ< that \-our teacher always expresses himself'herself clearly, circle lener .A.. If \ou think \our teacher never expresses himsclf^erself clearlv-, circle lener E. You can also choose leners B, C or D, which are in between. Thank you for your cooperation.

.AJwavs Never

The teacher is strict. We have to be silent in the teacher's class. The teacher talks enthusiastically about his/her subject. Tlie teacher trusts us.

A A A A

B B B B

C C C C

D D D D

E E E E

5. The teacher is concemed when we have not understood hinvher. 6. If we don't agree with him/her we can talk about it. 7. The teacher threatens to punish us. 8. We can decide some things in his/her class.

A A A A

B B B B

C C C C

D D D D

E E E E

9. The teacher is demanding. 10. The teacher thinks we cheat on assignments. 11. The teacher is willing to explain things again. 12. Tne teacher thinks we don't know anvthins.

A A A A

B B B B

C C c c

D D D D

E E E E

13. If we want som.ething he/she is willing to cooperate. 14. The teacher's tests are hard. 15. The teacher helps us with our work. 16. The teacher gets angrv- unexpectedly'.

A A A A

B B B B

C C c c

D D D D

E E E E

17 If we have something to say he/she will listen. 18. The teacher svmpathizes with us. 19. The teacher tries to make us look foolish. 20. The teacher's standards are ver\- hieh.

A A A A

B B B B

C C c c

D D D D

E E E E

21 22. 23. 24.

We can influence hinVher. we need his/her permission before we speak. The teacher seems uncertain. The teacher looks down on us.

A A A A

B B B B

C c c c

D D D D

E E E E

25. We have Lhe opportunity- to choose assignments which are most mteresting to us.

26. The teacher is urihappy. 27 The teacher lets us fool around in class. 28. The teacher puts us down.

A

. \

A A

B

B B B

C C

c

D

D D D

E E E

118

29. The teacher ta-kes a personal interest m us. 30. The teacher thinks we can't do things well. 31 . The teacher explains things clearly. 32. The teacher realizes when we don't understand.

A B A B A B A B

C D E

C D E C D E C D E

33. The teacher lets us get away with a lot in class. 34. The teacher is hesitant. 35. The teacher is friendly. 36. We learn a lot from him/her.

A B A B A B A B

C D E C D E C D E C D E

37. The teacher is someone we can depend on. 38. The teacher gets angrv' quickly.

39. The teacher acts as if he/she does not know what to do. 40. The teacher holds our attention.

A B A B A B A B


41 . The teacher's too quick to correct us when we break a rule. A B D

42. The teacher lets us boss himher around. 43. The teacher is impatient. 44. The teacher's not sure what to do when we fool around.

A B A B A B

C D E C D E C D E

45. The teacher knows even.thing that goes on in the classroom. 46. It's easv- to make a fool out of him/her. 47. The teacher has a sense of humor. 48. The teacher allows us a lot of choice in what we studv.

A B A B A B A B


49. The teacher gives us a lot of free time in class. 50. The teacher can take a joke. 5 1. The teacher has a bad temper. 52. The teacher is a eood leader.

A B A B A B A B


53. If we are not prepared, we're scared to go to his/her class. 54. The teacher seems dissatisfied. 55. The teacher is timid. 56. The teacher is patient.

A B A B A B A B


57. The teacher is severe when marking papers. 58. The teacher is suspicious. 59. It is easy to pick an argument with him/her. 60. The teacher's class is pleasant.

A B A B A B A B


61. We are afraid of him/her. 62. TTie teacher acts confidentlv'. 63. The teacher is sarcastic. 64. The teacher is lenient.

A B A B A B A B


THA^K YOU!

Modified bv- Wade Smith, From: Wubbels. Th., Creton, H. A. & Hoo.vmayers, H. P. (1985, March-.\pnl). Discipline Proble-s of Begmr.ir.g Teacher. Interaction Teacher Behavior Mapped Out. Paper delivered at the meetings of Lhe American Educational Research .Association, New York, NY'

119

APPENDIX B

BIOLOGY STUDENT AFFECTIVE INSTRUMENT

FACTOR ANALYSIS

RELIABILITIES

120

Table 17: Resultant Factors from Principal Component Analysis

SB01S SB02S SB04S SB05S SB06S SB07S SB08S SB09S SB10S

Factor 1

-.205 .775 -.510 -.369 -.575 -.257 .705 .616 .710

Factor Analysis Factor

2 .399 .352 .02452 .663 .583 .382 .09403 .321 .332

Extraction Method: Principal Component Analysis.

121

Table 18: Reliability analysis, Cronbach Alpha scale, Factor 1, BSAI (Opinions)

1. 2. 3. 4. 5.

SBOIS SB04S SB05S SB06S SB07S


Mean

2.1351 1.3874 .7117

1.5495 2.4414

Correlation Matrix

SBOIS

1.0000 -.0990 .1910 .2244 .0372

N of Cases =

Statistics for Scale

Mean 8.2252

Item-total Statistics


Scale Mean if Item Deleted

6.0901 6.8378 7.5135 6.6757 5.7838

Reliability Coefficients

Alpha .5120

SB04S

1.0000 .1002 .2943 .1683

111.0

Variance 12.7943

Scale Variance if Item Deleted

10.3009 9.8826 9.3248 7.1666 9.2619

5 items

Standardized

Std Dev

1.2022 1.2073 1.0820 1.3399 1.2981

SB05S

1.0000 .4301 .1044

Cases

111.0 111.0 111.0 111.0 111.0

SB06S SB07S

1.0000 .2617 1.0000

N of Std Dev Variables 3.5769

Corrected Item-Total

Correlation

.1358

.1916

.3479

.5342

.2338

item alpha =

5

Squared Multiple

Correlation

.0901

.1236

.1945

.3101

.0777

.5082

Alpha if Item Deleted

.5426

.5108

.4206

.2619

.4885

122

Table 19: ReliabilitY analvsis. Cronbach Aloha

1. 2 . 3 . 4 .

SB02S SB08S SB09S SBIOS


Mean

2.7477 2.2252 1.3694 2.8108

C o r r e l a t i o n Mat r ix

SB02S

1.0000 .4642 .4356 .6450

N of Cases =

S t a t i s t i c s fo r Mean S c a l e 9.1532

I t e m - t o t a l S t a t i s t i c s


I

S c a l e Mean

Lf I tem Deleted

6.4054 6.9279 7.7838 6.3423

R e l i a b i l i t y C o e f f i c i e n t s

Alpha .7546

SB08S

1.0000 .3498 .3843

111.0

Var i ance 13.9309

Sca l e Var i ance

i f I tem De le t ed

8.4069 8.3584 8.3892 8.7545

4 i t ems

S t a n d a r d i z e d

Std Dev

1.1157 1.3395 1.3344 1.1079

SB09S

1.0000 .4105

scale, Factor 2, Cases

111.0 111.0 111.0 111.0

SBIOS

1.0000

N of Std Dev V a r i a b l e s

3.7324 4

C o r r e c t e d I t em-T o t a l

C o r r e l a t i o n

.6614

.4878

.4866

.6023

i t em a lpha =

Squared M u l t i p l e

C o r r e l a t i o n

.4898

.2490

.2400

.4417

.7647

BSAI (Feelings)

Alpha i f I tem De le t ed

.6431

.7368

.7371

.6742

123

Biology Student Affective Instrument Last Six Digits of SSN.

This questionnaire asks you to describe your feelings and opinions about biology. Your cooperation can help develop an understanding about the relationship between a student's feelings and leaming outcomes. DO NOT WRITE YOUR NAME, for your responses are confidential. This is NOT a test. Your teachers will NOT read your answers and they will NOT affect your grade. They will only receive the average results of the classes, NOT individual student scores.

For each sentence on the questionnaire, circle the letter which you think best describes your feeling or opinion about each statement. Please use only a pencil. For example:

Agree Disagree Learning science is mostly memorizing A B C D E

If you agree that leaming science is mostly memorizing, circle the letter A. If you disagree that leaming science is mostly memorizing, circle the letter E. You can also choose letter B, which mean slightly agree, letter C, which means no opinion, or letter D, which means slight disagreement. Thank you for your cooperation.

Agree

A

A

B

B

C

c

D

D

Disagree

E

E

1. Learning biology is mostly memorizing.

2. Biology is interesting.

3. Guessing is OK to use in developing a solution to

a biology problem. A B C D E

4. There are always rules to use in solving a biology

problem. A B C D E

5. There are not many new discoveries in biology. A B C D E

6. Biology is mostly about classifications and not

about ideas. A B C D E

7. In biology, it is more important to understand why

an answer is correct than to know a lot of facts. A B C D E

8. Biology is useful in everyday life. A B C D E

9. I would like to have a job where I could use the

things I have learned in biology. A B C D E

10. Biology is fun. A B C D E

Thank You! Modified by Wade Smith, From: Telese, J. A. & Olivarez, A. (1997, January). Psychometric Properties of High School Student's Views about Mathematics Leaming and Instruction. Paper delivered at the meetings of the Southwest Educational Research Association, Austin, TX.

124

APPENDIX C

BIOLOGY END OF COURSE EXAMINATION

125

BIOLOGY I

126

DIRECTIONS

Read each quest ion and choose the best answer. Then .fill in the correct answer below and on your ajxsw^er documenL

SAMPLE A

Biology is the study of —

A atoms

B energy

C life

D stars

SAMPLE A

© © © ®

SAMPLE B

F Acuit

G Egg

H Lan^a

J P-ca

Listed above are some stages in the development of a butterfly. The first stage is G. It has been marked for you. In the box provided below and on your answer docjjz:en:, rsark the correct order for the other stages.

Youngest

Oldest

nrst

Second

Third

Fourth

©

©

©

©

•

®

®

®

©

©

©

©

o

0

o

o

127

CeUs

A

Organs

B

Tissues

C

Systems

D

Sequence the levels of organization &X)m LEAST complex to MOST complex. The least complex level is A. It has been marked for you. Mark the correct order for the other levels in the box below and on your answer document.

Least

'^^r Most

•

®

® (A:

®

d'

(§)

d;'

©

©

©

(Di

(D)

(D)

®

128

® ® ® (D

5X 7X 5X 10X

2 Which of the following shows the correct order of the developing organism?

A Z, X, Y, W

B W, X, Y, Z

C W, Z, Y, X

D Y, Z, W, X

129

Control GrouD

Experimental Group

Before Incubation After Inc'joation

3 According to the resul ts of the experiment above, which antibiotic is MOST effecrive in controlling this bacteria?

F A

G B -

H C

J D

100 Senses Used to Identify Foods

o 5 en c < o o o O c o u a C

Smell

4 The graph shows the results of an experjnerLt requiring people to use only one sense to idenufy foods. According to this mformancn. which sense was most accurate for idenrj~.."Lng foods?

A Touch

B Taste

C Sight

D SrneU

130

5 A student would like to investigate careers in biology. Which summer job would offer the suident the LEAST relevant experience?

F Nursing-home aide

G Flag person for a construction crew

H Lab assistant for a biomechanical engineering firm

J Proofreader of advertisements for a pharmaceutical company

Key

•Maje wTth

OFen:aie witfi Tive lingers

Male wiCi I SIX fingers

I Female wi;^ sixrincers

Ccn

o o a u Pa-; an DecDie Ken Mary AJics Juan

D D O O Ann George Jerl Pa.-.c-/ Sa,-an Jane Mana

6 According to the pedigree shown above, the parents of Sarah and Jane are —

A Anita and Don

B Mar;/ Alice and Juan

C Debbie and Ken

D Paul and Jaji

7 The map shows the ranges of four different species of snakes. Which species would probably have the MOST genetic varianon?

F W

G X

H Y

J Z

131

Order Diptera Order Hymenoptera

8 Insects in the order Hv"menoptera diner from those in the order Diptera by the presence of —

A two pairs of wings

B antennae

C trajisparent wmgs

D three pairs of legs

9 Hair is constantly being cut or broken vrithout causing pain. What is the BEST hv'pothesis for this observation?

F Hair is used for warmth.

G Hair is made of ver>' small cells.

H Hair contains small capillaries.

J Hair contains no nerv'e endings.

132

Ac.ivrty

Swimming

Running

Rowing

Dancercize

Normal Pulse Rate per Minute

72

72

72

72

Pulse Rate During Aaivity

per Minute

120

140

115

124

vXv

10 According to this chart, which exercise raises the pulse rate LEAST?

A Swimming

B Running

C Rowing

D Dancercize

11 Daisies were grown in one flower bed and marigolds in another nearby flower bed. Equal announts of salt were added to the flower beds. All the daisies died and all the marigolds lived. The BEST conclusion for this experiment is that marigolds —

F need less sunlight than daisies

G produce more seeds than daisies

H need less water than daisies

J can tolerate more salt than daisies

•• o

" . r ^ ^

As pine trees age, they add one whorl of branches per year unless there is some damage. The picture shows a Norway spruce (Picea abies), a t y-pical member of the pine family. What is the approximate age of this tree from the bottom whorl?

A 21 years

B 34 years

C 47 years

D 58 years

133

People in one culture often eat foods that people in other cultures do not eat. For example, squid is an Italian delicacy but not frequendy foimd in American cooking. Barbara has decided to cry some squid if it is inexpensive and nutrinous. The statements following the key were made by some of Barbara's friends. Use the key below to classify each statement in questions 13 and 14.

W This factual statement, Lf correct, would support her decision to trv* some squid.

X This factual statement, if correct, would NOT support her decision to try some squid.

Y This factual statement, if correct, would have no value in making a decision for or against trymg some sqmd.

Z This statement cannot be proven because it is a value judgment or opinion.

15 A new process has been deveIof>ed that helps keep fruits and vegetables fresh. Water loss is reduced by spraying the fruits and vegetables with a chemical obtained from fat. A panel of scientists will decide whether the product is safe for use on hiiman foods. Which question is most important in making this decision?

F Wdl using the product result in increased food cost?

G Does the product change the color or taste of the food?

H V^Tiat are the long-term side effects of consiiming this product?

J Can this product reduce water loss in other foods or manufactured items!*

13 "Barbara, you are allergic to sq'Jid."

F W

G X

H Y

J Z

14 "Crabs are a source of food for squid.

A W

B X

C Y

D Z

134

Thermometer

Holes in can

Holes in can

Glass tube

Sample

A

B

C

D

Mass of

Sample

Temperature of Water ('C)

Before Burning

After Euming

16 The picture shows a calorimeter used to measure the number of calories in a food sample. The chart shows the basic types of measurements that should be recorded. Which improvement to the apparatus would probably allow more accurate and precise measurements?

A Extend the glass tube by attaching a rubber hose.

B Replace the water with mineral oil.

C Insulate the can instead of leaving it open.

D L'se a metal container for the water instead of a flask.

135

17 The pictures below show wings of different animals. Which animal does NOT share a recent common ancestor with the others?

->^

H

Use the chart below to answer questions 18 and 19.

A Study of Hatching Success of Wood Frog Eggs

Under Different Conditions

Number of Trials

18

18

18

15

5

Number pH of of Eggs j Pond Water

Mean Hatcr.ing Success (^o)

500 1 7.00 1 90

490 4.00 85

490 1 3.75 1 25

100 3.50 1 less tr.an 1

100 ! 3.25 none

18 According to the chart, which conclusion is MOST valid?

A Increases in pH are caused by increases in the number of wood frogs.

B The number of tnais controls the n-jmhe.'" of wood frog eggs tested.

C The hatching success in wood frog eggs decreases as the pH decreases.

D Decreasing the number of wood frog eggs in the pond water increases the hatching success rates.

19 NMiich variable seemed to influence the mean hatching success rate of the wood frog eggs^

F The amount of water

G The number of trials

H The number of eggs

J The pH of pond water

136

^ : ^ > . )

\\v^^ Miilipece

Cer.ticeda

20 Both of these animals have numerous legs. Wnat advantage does this provide?

A Scares other animals

B Confjses predators

C Increases pushing power

D Captures prey

C' R r 1

RR Rr

i '

Rr rr

21 The P'jnnett square above shows a cross between two heteroz^'gous long-haired g^ome: pigs. Virliat is the chance of any of the offspring ha%'ing short hair^

F

G

H

J

0%

25 Tc

50^c

75~<r

137

\ 1 | . . >UI

Area

A ^

>^j!tiljl;'V:fl,;:/.'^."':viV.i:r.:',':,'.'iii,r'iii'-j:''v/p^'i,'-''-, i ' i , r ' i i i - j :"i^p^' ' , ' - ' ' , i ' ;. - W f . - '

A.:

->iiii I,, .J ^ . „

Area II

22 Lightning struck and ignited a tree in both Area I and Area II. More trees were killed or damaged in Area I because of the —

A population densit>'

B amount of water available

C tree size

D tree r.^ie

138

Use the graph below to answer questions 23 and 24.

Populations of Long-tailed Mealybugs and Mealvbua Predators on Citrus Trees

7001 '-

600

500 V)

5 400 .a i 300 2

200 100

0 M

1 i 1 ' A ' MeaiyDUyS 1 / i \ i MealvbuQ i / ; \ 1 Hreaatcrs

/ • \ • . . •

' / ' \ ' / ' / ' \ | S I

/ ''' ' iV '\ 1 ^ .' 1 ^ N . . I l l -^ ' ' ^^' ' - . « « . , as Apr May Jun Jul Aug Sep Oct Nov 0 SC

23 Which conclusion is best supported by the graph?

F Mealybugs and thefr predators deposit eggs in the winter months.

G Mealybug populations are smaller than their predator populations.

H Mealybug predators are more tolerant of warm temperatures than mealybugs.

J Mealybug predator populations are affected by the number of mealybugs.

24 According to this graph, which of these is M O S T dependent on the time of the year?

A The number of mealybugs

B The kind of mealybugs

C The size of the mealybugs

D The kind of mealybug predators

25 Which of these is the advanLage of using the low power objective over using the high power objective of a microscope?

F More detailed structures can be seen.

G More area can be seen at one time.

H Smaller organisms can be seen.

J Shorter depth of field can be produced.

Lignt 6C°2 * 6H2O chloropnyli* C^^'205 ^ 6O2?

26 The equation shows the reaction for photosvTithesis. According to the equation, one PRODUCT of photos\Tithesis is —

A carbon dioxide

B chlorophyll

C water

D ox>-gen

27 Organisms labeled as "pathogenic" should be handled with care because they —

F take oxygen from the air

G produce alcohol

H release explosive gases

J cause diseases

139

28 An entomologist wants to determine whether cold temperature triggers hihemation in insects. Which of these variables is LEAST important to control in this experiment?

A The temperature of the air in the insect cages

B The length of time the insects are e.tposed to each temperature

C The r-npe of insects used in the experiment

D The type of cages in which the insects are placed

29 Each of the following graphs represents the human population in a different country. Which of these populations has the largest number of people over the age of 60?

H

es* « 75-34

5 60-74

C 45-59

§, z:>-i^

Maies

'

1 1

1 1 1

1

-err^es

1

I

"^ 15-23 1 1

0-14 i l l 1 1 1 1 1

as. 1/1 75-&4

i 60-74 C 4 ^ : 9

CT 3 0 - U

'^ 15-29

.Majes

Poputauon

I 1 > ' 1

Females

1

0-14 , ' 1 1 1 ( . 1

es* ^ 75-54

= 60-74

C -15-59 o - - . ,

" 15-29 0-14 ,

8 5 *

>n 75-34 = 6C^74

C 45-59 i i 3C -t4

" 15-291

Males

, i

1 1

MaJes

r" 1

0 Po;ui ; luon

1

1

1

i 1

1 1

Popu

r

1330

1

Females

1

'

1

n

Fema:es

1

1

1 1

0-14 1 1 1 1 1 1 ' 1 1

— Poou

1

laoon

140

30 The s tructure of a ceil is most closely related to its —

A age

B fiincnon

C size

D color

31 The name "carbohydrates' indicates that comoounds contain all of the following E X C E P T —

F carbon

G oxygen

H hydrogen

J phosphorus

220

2B0

iftn

2

!c ' * u

Q 1»

a 2 100

LU

1 " £>

1 60 2r

40

20

; \

:^ \ :A \

I •

•

— SunaaPi^ Hoooao Pas

I A / \

J

- • \

2 4 6 8 10 12 14 :6 18 20 Z2 24 25 28 30 32

TriaJs

32 These data were obtained during an experiment in which guinea pigs and hooded rats were trained to run a maze. Which conclusion is best supported by these data?

A Hooded rats leam faster than guinea pigs.

B Guinea pigs learned more than hooded rats.

C Guinea pigs remember longer than hooded rats do.

D Hooded rats make more errors than guinea pigs.

141

33 ^ l i i ch of these will correctly complete the missing bases of DNA?

F CGA

G G€T

H TAG

J TCG

34 I>ry sand should be kept in the laix)rator>' to be used to —

A absorb harmful gases

B e.Ttinguish small fires

C prevent rust

D kill bacteria

Crayfish — > - Raccoon 4 I Snake

Dragonfly A nymph ^ ^ ^

k Frog Clam

Beetle ^

/ / Mosqu

\ Algae

ito

\

Bird 4

Dragonfly

35 If all of the frogs in an area were killed, there would probably be a sudden DECREASE m the number of —

F snakes

G raccoons

H birds

J dragonflies

36 .\ grocer wanted to conduct a controlled experiment to determine the effect of storage temperature on the rate at which mWV spoils, ^"hich of these factors would be the LEAST important consideration when planning the experiment?

A The range of temperatures at which the milk is stored

B The length of time the rr.-ik is held at each temperature

C The tv-pe of refrigerator used

D The age of the milk prior to testing

142

37 Which bird would most hkelv eat seeds?

143

38 V»"nat :s the average length of these organisms?

A 1.0 mm

B 0.1 mm

C 0.2 mm

D 0.5 mm

^/^/C^/^ Nutrition Facts Serving Size: 1 can (3£5 ml)

Vnojni Pef Safvina

CaJones 0 *i Car* va!ue'

Total Fat Oo Soaium 35.0a Total Camonvcrate 0.8a Sucars Go Protein Og

'v1ta;nin A 1.8% • Vltaniin C 1.8% r.i>>oSavin 1.3% • CaJaum 1.3% -

0°'o 1.75°o 001 "'o

0% 0%

• Niacn 1.8% TT-jamine 1 £°'o

'^cara.'ne (Aspamc Acd • Phenylalanine • V,e;nyi • Es;er)« Potassium Ber.zoa!e (Preservatrvei

' Psrcant Oilfy Viiue'.ara Cis«a on j 2000 caiona d)eL Your Caily Value n-jv oe nigner or lov¥«f. oeoencmg on your caiona n^^ai.

39 .According to this soft drink label, which of these contains phenylalanine?

F Riboflavin

G Niacin

H Potassium Benzoate

J Aspartame

144

40 Which of tnese MUST a 10-meter-tail terresrrtai plant have m order to rLirvive?

A Rapid seed dispersal

B A broad canopy of leaves

C Colorful flowers

D An extensive vascular svstem

Number of Lent and Darx Moths CciIec:eo in a Feres:

100 A . . ^ ^ ^

Pe.-rent of

Mct.^.s

• - - • .

Key

—— Oarx McTs

Years Cc.lecteo

41 If the trends shown in this grach ccnn. forest will have —

F mostiy dark moths

G mostly Light moths

H a smaller total number of mcths

lue, t:

145

42 The animal above is a t>-pical bfrd of prey, ^"hich characteristic may be used to idennr.' this bird as a predator?

A The length of the wings

B The shape of the feet

C The size of the eyes

D The width of the tail

146

APPENDIX D

CORRELATION OF ALL VARIABLES

147

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