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  • Dallas Independent School District

    EVALUATION OF THE REASONING MIND MATHEMATICS PROGRAM

    2012-2013

    EA13-514-2

    DEPARTMENT OF EVALUATION

    AND ASSESSMENT

    Mr. Mike Miles Superintendent of Schools

  • Dallas Independent School District

    Mr. Mike Miles Superintendent of Schools

    EVALUATION OF THE REASONING MIND MATHEMATICS PROGRAM

    2012-2013

    EA13-514-2

    Joan Bush, Ph.D. Myoungsook Kim, Ph.D.

    Dallas Independent School District

    Approved Report of the Department of Evaluation and Assessment

    Nancy Kihneman, Ph.D. Director Program Evaluation

    Cecilia Oakeley, Ph.D. Executive Director Evaluation and Assessment

    Dallas, Texas September 2013

  • Table of Contents

    Section Page

    ABSTRACT .............................................................................................................................. 1

    PROGRAM DESCRIPTION .................................................................................................... 2

    PURPOSE AND SCOPE OF THE EVALUATION ................................................................... 3

    MAJOR EVALUATION QUESTIONS AND RESULTS ............................................................ 3

    2.1 What were the sources and amounts of funding for Reasoning Mind? ............................ 3

    2.2 What were key findings from the literature review related to technology-based supplemental instruction and scaling up educational initiatives? ..................................... 4

    2.3 What were the demographic characteristics of students and teachers involved in Reasoning Mind? .............................................................................................................. 8

    2.4 Did staff members participate in Reasoning Mind training as planned? ........................... 10

    2.5 Was the Reasoning Mind program used by students as planned? .................................. 23

    2.6 What were teacher and administrator perceptions of Reasoning Mind? .......................... 53

    2.7 What were student mathematics achievement outcomes? .............................................. 82

    SUMMARY .............................................................................................................................. 101

    RECOMMENDATIONS ........................................................................................................... 104

    REFERENCES ........................................................................................................................ 105

  • ii

    List of Tables

    Table Page

    1 Demographic Characteristics of 2012-13 Reasoning Mind Students ............................ 9

    2 Number and Percentage of Teachers that Completed the Qualification Course by Time Period ............................................................................................................... 11

    3 Number and Percentage of Teachers that Completed the Qualification Course by

    Division ...................................................................................................................... 12

    4 Teacher Observation Ratings Based on the RM Rubric ................................................ 16

    5 Teacher Observation Ratings by Indicator for Observation 3 ........................................ 22

    6 Teacher Observation Rating Changes from Observation 1 to Observation 3 by Indicator .................................................................................................................... 23

    7 Number of Hours Students Spent on RM in Fall and Spring by Grade and Division .... 26

    8 Descriptive Statistics for Reasoning Mind Hours by Grade and Division ...................... 27

    9 School Information by RM Hour Categories in the Fall and Spring by Grade and Division ...................................................................................................................... 29

    10 RM Second-Grade Fall Hour Information with Schools Rank Ordered by Division

    and Mean Hours Online ............................................................................................ 30

    11 RM Third-Grade Fall Hour Information with Schools Rank Ordered by Division and Mean Hours Online ................................................................................................... 35

    12 RM Second-Grade Spring Hour Information with Schools Rank Ordered by Division

    and Mean Hours Online ............................................................................................ 40

    13 RM Third-Grade Spring Hour Information with Schools Rank Ordered by Division and Mean Hours Online ............................................................................................ 44

    14 Number of RM Objectives Students Completed in Fall and Spring by Grade and

    Division ...................................................................................................................... 50

    15 Descriptive Statistics for Reasoning Mind Objectives Completed by Grade and Division ...................................................................................................................... 51

    16 Mean Accuracy Rates in Fall and Spring by Grade and Division .................................. 52

    17 Results of Campus Administrator Overall Agreement Items ......................................... 55

    18 Results of Campus Administrator Survey Satisfaction Items ......................................... 57

    19 Results of Campus Administrator Survey Program Coordinator Items ......................... 58

    20 Results of Campus Administrator Survey Frequency of Use Items ............................... 58

    Continued

  • iii

    List of Tables Continued

    Table Page

    21 Results of Campus Administrator Survey Technology Implementation Items ............... 59

    22 Results of Campus Administrator Survey RM Awareness and Use Items .................... 60

    23 Campus Administrator Survey Comments ..................................................................... 62

    24 Supported and Non-Supported Teacher Respondent Characteristics .......................... 64

    25 Results of Teacher Survey Satisfaction Items ............................................................... 65

    26 Results of Teacher Survey Collaboration Items ............................................................ 66

    27 Results of Teacher Survey RM Support Items .............................................................. 67

    28 Results of Teacher Survey Professional Development Items ....................................... 68

    29 Results of Teacher Survey RM Program Coordinator Items ......................................... 69

    30 Results of Teacher Survey RM Resource Items ............................................................ 70

    31 Results of Teacher Survey Frequency of Use Items ..................................................... 71

    32 Results of Teacher Survey Student Change Items ........................................................ 72

    33 Results of Teacher Survey Frequency of Technology Issue Items ............................... 73

    34 RM Supported and Non-Supported Teacher Survey Comments .................................. 75

    35 Percentage of District Second-Grade Students At or Above the 40th Percentile on ITBS Mathematics Total from Spring 2012 to Spring 2013 ...................................... 85

    36 Percentage of District Second-Grade Students At or Above the 80th Percentile on

    ITBS Mathematics Total from Spring 2012 to Spring 2013 ...................................... 86

    37 Percentage of District Second-Grade Students At or Above the 40th Percentile on ITBS Mathematics Total by Student Group from Spring 2010 to Spring 2013 ......... 87

    38 Percentage of District Third-Grade Students that Met Satisfactory on STAAR

    Mathematics in Spring 2012 and Spring 2013 .......................................................... 88

    39 Percentage of District Third-Grade Students that Met Advanced on STAAR Mathematics in Spring 2012 and Spring 2013 .......................................................... 89

    40 Correlations between RM Use and Mathematics Achievement Test Scores ................ 91

    41 Multiple Regression Results for Second-Grade 2013 ITBS Math Scores and Various Predictors ..................................................................................................... 93

    Continued

  • iv

    List of Tables Continued

    Table Page

    42 Multiple Regression Results for Second-Grade 2013 ITBS Math Scores and Three Main Predictors ......................................................................................................... 93

    43 Multiple Regression Results for Third-Grade 2013 STAAR Scores and Various

    Predictors .................................................................................................................. 95

    44 Multiple Regression Results for Third-Grade 2013 STAAR Scores and Three Main Predictors .................................................................................................................. 95

    45 Multiple Regression Results for Third-Grade 2013 ACP Scores and Various

    Predictors .................................................................................................................. 97

    46 Multiple Regression Results for Third-Grade 2013 ACP Scores and Three Main Predictors .................................................................................................................. 97

    47 RM Student ITBS and STAAR Results by Hours Online and Level A Accuracy

    Rates ......................................................................................................................... 100

  • v

    List of Figures

    Figure Page

    1 Percentage of Supported and Non-Supported RM Teachers that Completed the

    Qualification Course by Time Period ........................................................................ 11

    2 Percentage of RM Teachers that Completed the Qualification Course by Division ...... 13

    3 Percentage of Supported Teachers that Met RM Training Requirements ..................... 14

    4 Percentage Distribution of Observation Ratings: Data Driven Decisions ...................... 17

    5 Percentage Distribution of Observation Ratings: Lesson Planning ............................... 17

    6 Percentage Distribution of Observation Ratings: Instructional Methods ....................... 18

    7 Percentage Distribution of Observation Ratings: Learning Modes ................................ 18

    8 Percentage Distribution of Observation Ratings: Teacher Engagement ....................... 19

    9 Percentage Distribution of Observation Ratings: Procedures ....................................... 19

    10 Percentage Distribution of Observation Ratings: Incentive Systems ............................ 20

    11 Percentage Distribution of Observation Ratings: Notebooks ........................................ 20

    12 Percentage Distribution of Observation Ratings: Independent Learning ....................... 21

    13 Percentage Distribution of Observation Ratings: Student Engagement ........................ 21

    14 Mean Hours Students Spent on RM in Fall and Spring by Grade ................................. 25

    15 Percentage of Students in RM Hour Categories by Grade and Semester .................... 25

    16 Percentage of Schools in RM Hour Categories by Grade and Semester ...................... 28

    17 Mean RM Objectives Students Completed in Fall and Spring by Grade ....................... 49

    18 Percentage of RM Objectives Completed by Grade and Semester .............................. 49

    19 Percentage of Campus Administrators, Supported Teachers, and Non-Supported Teachers that Agreed or Strongly Agreed with Selected Survey Items ............... 77

    20 Percentage of Campus Administrators, Supported Teachers, and Non-Supported

    Teachers that Agreed or Strongly Agreed with Selected Support Items ............. 78

    21 Percentage of Campus Administrators, Supported Teachers, and Non-Supported Teachers that Agreed or Strongly Agreed with Technology Issues ..................... 79

    22 Number of Staff Survey References to Improved Student Learning and

    Engagement .............................................................................................................. 80

    Continued

  • vi

    List of Figures Continued

    Figure Page

    23 Number of Staff Survey References to Technology-Related Challenges ...................... 80

    24 Number of Staff Survey References to Scheduling Issues ............................................ 81

    25 Number of Staff Survey References to Suggestions for Improving Technology ........... 81

    26 Percentage of Second-Grade Students At or Above the 40th Percentile on Spring 2013 ITBS Mathematics by Level A Accuracy and Total Hours Online .................... 99

    27 Percentage of Third-Grade Students that Met Level 2 Satisfactory on Spring 2013

    STAAR Mathematics by Level A Accuracy and Total Hours Online ......................... 99

  • EA13-514-2

    EVALUATION OF THE REASONING MIND MATHEMATICS PROGRAM: 2012-2013 Evaluators: Joan Bush and Myoungsook Kim, Dallas Independent School District

    ABSTRACT

    During 2012-13, the Reasoning Mind (RM) supplemental mathematics curriculum was provided in grades two and three at all elementary schools except for Allen and Dealey. The district budgeted Title I, Part A funds ($969,500) to pay for student accounts along with Title II, Part A funds ($525,000) to cover teacher training for a total of $1,494,500. Overall, 26,151 students were enrolled in RM schools; this included 13,398 (98.8%) of the districts 13,563 second-grade students and 12,753 (98.8%) of the districts 12,907 third-grade students. Whereas all RM teachers were supported in 2011-12, the district allocated one supported teacher per campus in 2012-13 due to cost. A total of 584 second- and third-grade teachers were trained including 129 supported teachers, 402 non-supported teachers, and 53 previous RM teachers. Less than half (48%) completed the RM Qualification Course by the end of the first six weeks, which delayed use of RM with students. Most supported teachers (92%) completed the required Best Practice and Curriculum Study workshops. Student use of RM improved from fall to spring; however, most students did not meet the 30-hour per semester requirement in fall (98% to 99%) or spring (68% to 69%). The majority completed Level A (easiest) learning mode problems in the fall and both Level A (easiest) and B (medium difficulty) learning mode problems in the spring; however, notably fewer completed Level C (hardest) learning mode problems, review mode problems (A, B, C), and test mode problems. School comparisons mirrored student data. Per the surveys, most campus administrators and teachers wanted to continue using RM, believed students benefited, and were satisfied with district and RM support. Supported teachers were more positive than administrators and non-supported teachers. The majority of campus administrators and supported teachers were positive toward RM training and resources; non-supported teacher responses were mixed. The most-cited success was increased student learning and engagement. The chief barriers were technology and scheduling problems; the main suggestion was to improve technology. Overall analyses provided a big picture of how district students progressed in mathematics over time; however, due to low implementation of RM, the use of RM as a supplemental program, and the lack of a control group, findings were limited and did not show the true impact of RM on student math achievement. The percentage of second-grade students that scored at or above the 40th percentile on ITBS Mathematics Total slightly decreased from 2012 (57.9%) to 2013 (56.2%), whereas the percentage of third-grade students that met the STAAR Satisfactory mathematics standard marginally increased from 2012 (55.2%) to 2013 (57.3%). Results of correlation analyses revealed that mastery of objectives and accuracy rates on Level A (easiest) learning mode problems were more strongly related to math achievement than time spent online. Per multiple regression analyses, three predictors (prior 2012 ITBS mathematics achievement, mastery of objectives, learning mode Level A accuracy rates) explained 64 percent of the variance of second-grade spring 2013 ITBS scores. Similarly, three predictors (learning mode Level A accuracy rates, prior 2012 ITBS mathematics achievement, STAAR test mode Level A accuracy rates) explained 68 percent of the variance in third-grade STAAR scores and 61 percent of the variance of spring third-grade ACP scores. Follow-up frequency analyses showed that time is important; however, students with accuracy rates at 75 percent or higher did notably better on ITBS and STAAR.

  • 2

    PROGRAM DESCRIPTION The Reasoning Mind (RM) technology-based mathematics curriculum program was provided to

    Dallas Independent School District (ISD) second-grade students in 2011-12 and expanded to include

    second- and third-grade students in 2012-13. Students at all but two district elementary schools (Allen,

    Dealey) were enrolled in 2012-13. RM developed the adaptive, online mathematics curriculum to be used

    as a supplement to the regular classroom teachers mathematics instruction in grades 2 through 4 and as

    the core mathematics curriculum for grades 5 and 6. The district chose to use RM in second- and third-

    grade classrooms due to a clear need to improve districtwide math achievement.

    Per the RM web site, A Reasoning Mind classroom is a hybrid of online and face-to-face

    instruction, where the teacher gives each child individual help and attention. During class time, students

    use individual computers to log into the online RM program and to work through lessons and

    corresponding problems at their own pace. The RM system develops a personalized path for each child

    based on an assessment that identifies strengths and weaknesses. When a student struggles with a

    problem, a request for the Genie Solution can be made. The Genie Solution provides a thorough

    explanation for computing the problem. While students are working online, the teacher can view an RM

    administrator screen to see how students are progressing and to note which students are having

    difficulties. At that point, the teacher provides one-on-one interventions or small-group tutorials.

    The support model for RM includes supported and non-supported teachers. In 2011-12, all

    teachers were supported; however, in 2012-13, the district opted to pay for one supported teacher per

    school for financial reasons as the cost per supported teacher was $3,500. As a result, the remaining

    second- and third-grade teachers were non-supported. Through the RM support model, the supported

    teachers worked closely with an RM program coordinator and were to collaborate with the non-supported

    teachers in the building. Both supported and non-supported teachers were required to take the RM

    Qualification Course and pass the course exam before using RM with their students. Supported teachers

    received six additional professional development courses (12 hours), three formal program coordinator

    observations, and periodic program coordinator visits and communication. In addition, the RM program

    coordinators were to set up periodic meetings that included the principal and all RM teachers. Program

  • 3

    coordinators were available to assist both supported and non-supported teachers as time allowed;

    however, the focus was on the supported teachers.

    Program Goals. The goal and action steps to achieve it were summarized in a November 2012

    memorandum from Superintendent Miles to the Board of Trustees and presented at the

    November 8, 2012 School Board Briefing. The overall goal of RM in 2012-13 was to increase

    mathematics achievement for second- and third-grade students. As for action steps, the district agreed to

    ensure that all campuses implemented the program with fidelity to the RM model, that all teachers

    completed the training, and that every campus had a schedule that provided each student with the

    required amount of time for the program. RM promised to provide training, on-site coaching, support, and

    weekly summaries of student time spent on RM, so the district could make adjustments as needed.

    PURPOSE AND SCOPE OF THE EVALUATION

    The purpose of this report is to summarize the context, implementation, and outcomes of the RM

    Program. This included interviews with RM staff members as well as with Dallas ISD program staff

    members assigned to the grant, a review of internal documents, and analyses of RM database files,

    campus administrator and teacher survey data, and math standardized assessment data.

    MAJOR EVALUATION QUESTIONS AND RESULTS

    2.1 What were the sources and amounts of funding for Reasoning Mind?

    Methodology

    The workscopes for Title I, Part A and Title II, Part A were reviewed to note sources and amounts

    of funding for RM. The Title I workscope was reviewed to the determine cost per student. Likewise, the

    Title II workscope was studied to find out the cost per teacher trained.

    Results

    The total district budget for RM during 2012-13 was $1,494,500, which included $969,500 of

    Title I, Part A funds and $525,000 of Title II, Part A funds. Title I funds were allotted to pay for 27,700

    individual student accounts at a cost of $35 per student account; this included 14,200 second-grade and

    13,500 third-grade student accounts. As for Title II, funds were allocated to pay for 150 supported

    teachers professional development at a cost of $3,500 per teacher.

  • 4

    2.2 What were key findings from the literature review related to technology-based supplemental

    instruction and scaling up educational initiatives?

    Methodology

    A literature review was conducted to summarize strengths and challenges of RM implementation

    found in other settings and to review best practices related to computer-based instruction and the process

    of scaling up initiatives. The review was not comprehensive but was meant to summarize findings that

    can guide future implementation of RM in Dallas ISD.

    Results

    Reasoning Mind Studies

    Five evaluation studies were found in the literature for RM. No studies were based on the second-

    and third-grade supplemental programs used in Dallas ISD. Rather, studies were conducted in grades

    four through seven. Three of the five studies took place in Houston ISD, whereas the other two were

    conducted in Angleton and Beaumont.

    Weber (2003) evaluated a small pilot project in Houston ISD that involved an experimental

    (N=30) and control group (N=26). He found meaningful differences between seventh graders in the two

    groups and concluded that the positive RM results were far beyond reasonable given the focus and

    duration of the project. The study showed that most students and the two teachers that participated in

    RM were positive about their RM experiences.

    In 2006, Weber published findings from a study conducted in 10 Houston middle schools. Results

    showed that the implementation and evaluation of RM were fraught with problems. For example, many

    schools did not use RM until the spring semester. The comparison group outperformed the RM group in

    most comparisons. Further outcome analyses showed that student achievement in reading and student

    performance on prior measures of mathematics achievement were better predictors of success in

    mathematics than RM. Weber noted that the field test accomplished its purpose by identifying problems

    and possible solutions. Implementation issues included confusing theory presentations, bugs in certain

    mathematics problems, overly wordy solutions, solutions skipped by students due to length, problems

    becoming too difficult too quickly, and so forth. The glitches led to some teacher and student frustration.

    Weber suggested that RM further investigate a variety of areas such as whether students with low

  • 5

    reading skills could be successful using RM, whether a sufficient number of teachers could be

    appropriately trained, what level of teacher support was required to ensure the success of the program,

    and the scalability of RM and the Deployment Coordinator model.

    In a study of fifth graders at three schools in Angleton ISD, Waxman and Houston (January 2008)

    found that RM students outperformed control students on an RM-developed pre- and posttest but not on

    the Texas Assessment of Knowledge and Skills (TAKS). Results of teacher and student surveys were

    positive.

    Waxman and Houston (2012) conducted a study that included fifth-grade students at 16 schools

    (eight treatment and eight control) in Beaumont ISD. Results showed that the higher TAKS scores of RM

    students versus comparison students were statistically and practically significant. In addition, results

    revealed that the percentage of correct answers was the best predictor of RM students performance on

    the math TAKS and that percentage of correct answers had a greater effect on math TAKS than

    students prior year performance. Teacher and student survey results revealed positive perceptions

    toward RM.

    Houston ISD (2011) conducted an internal evaluation of the RM fourth-grade supplemental

    program used in 21 schools. Outcome results for the RM and matched comparison group were mixed. On

    surveys, most students reported that they enjoyed RM and felt RM helped them understand math better.

    Teacher surveys were positive for the most part; however, some teachers had concerns about the

    alignment of RM and district curriculum and about students missing other instruction while participating in

    RM.

    In summary, the relationship between RM and student achievement was very positive in some

    studies but mixed in others. Webers external evaluation of the Houston ISD field study identified

    implementation issues that should be considered in future studies such as bugs in the program, teacher

    training and support, scalability of the RM model, and scalability of the Deployment Coordinator model.

    Educational Technology and Scalability Studies

    Cisco Systems commissioned the Metiri Group (2006 and 2009) to summarize findings in the field

    of educational technology. In reports, the authors state that technology advocates over-promised

    student learning outcomes due to underestimating the critical need for system changes required to use

  • 6

    technologies effectively in learning. Based on their literature review, they found that technology provided

    a small, but significant, increase in learning when implemented with fidelity and accompanied by

    appropriate pedagogical shifts. Barriers to effective use of instructional technology include lack of access

    due to unreliable or outdated technology along with lack of vision, absence of an innovative school

    culture, and/or limited resources. As a result, the authors emphasize the need to address challenges that

    may be unique to specific schools and to take into account the importance of leadership development,

    professional development, school culture, curricular redesign, and teacher preparation.

    Cheung and Slavin (2011) conducted a meta-analysis of math-related educational technology

    applications. Across the studies reviewed, educational technology programs produced a small, positive

    effect on mathematics achievement. Supplemental computer-assisted instruction (CAI) had the largest

    effect on students math achievement. Studies with small sample sizes produced twice the effect sizes of

    those with large sample sizes; they suggested that one of the likely reasons was that small-scale studies

    could be more tightly controlled than large-scale studies. They also found differences related to program

    intensity (i.e., larger effects were found for programs that required more student time). They conjecture

    that though some attribute the small effect of supplemental programs to low implementation, the limited

    time given to implementation could be part of a larger problem. That is, it is possible that separate CAI

    programs are not well accepted or seen as central to instruction by teachers, so teachers may not make

    sure that students get the full amount of time on technology recommended by vendors.

    In Levins recent study on what it takes to scale up innovations, he notes significant additional

    costs that make them difficult to replicate without significant additional resources. Specifically, Levin

    described large-scale change in American schools as a very daunting proposition due to a fundamental

    tension between replicating a program or practice exactly and adapting it to meet different local

    circumstances. He proposes comparing an innovation to the standard (original) model in five areas:

    cost, human capacity, tools and infrastructure, political support, and non-school factors. The more the

    innovation differs from the standard model, the harder it will be to scale. Brief descriptions of the five

    areas follow.

    Cost. It is important to understand the costs of carrying out an innovation in comparison to the

    standard model to determine how much more is required per student or per school.

  • 7

    Human capacity. Innovators must determine if the innovation demands a significantly higher

    level of skill or commitment than is found in the system now. For example, an innovation could require a

    higher level of competence, a higher level of time commitment, or a behavior that people cannot

    currently do. Also, an innovation could require particularly skilled leaders and key support people. In

    summary, the more complex an innovation, and the further it is away from current practice in most

    schools, the higher the human capital demands.

    Tools and infrastructure. If an innovation requires supports that are not typically available in

    schools, they must be identified. Examples include types of facilities, technology, training materials,

    additional time for training or development, and so forth.

    Political support. Internal and external supports are important and include support from elected

    leaders, school and district leaders, teachers, students, and parents. He pointed out that peoples

    perceptions are not always well informed, but they are real. Also, he noted, Innovations that do not

    meet the public-acceptability test are unlikely to succeed at scale.

    Non-school factors. Factors outside the school can affect the scaling of any initiative. Outside

    factors that differ from the model can include student demographic characteristics, mobility rates, home

    technology access, parent time or commitment, and so forth.

    Levin cautions that it cannot be assumed that the difficulty of any of these challenges will change

    in lockstep with an increase in scale of application. While some issues may become easier with wider

    implementation, others could become more difficult.

    Thus, implementation fidelity is of utmost importance and requires systemic changes including

    added support and attention to issues that can preclude successful implementation such as lack of staff

    buy in, limited support at all levels, the absence of pedagogical shifts, variation across schools, and so

    forth. For example, the Metiri Group emphasized the importance of developing visionary leaders,

    providing high-quality professional development, ensuring a healthy school culture, redesigning the

    curricula to take into account technology use, and adequately preparing teachers to teach using

    technology and/or technology-based programs. Cheung and Slavin note larger effect sizes for small-scale

    than large-scale programs due to tighter control of small-scale programs and the possibility that some

    teachers may not fully accept or implement supplemental programs; this could certainly be a possibility in

  • 8

    a district as large as Dallas ISD. Similarly, Levin suggests the importance of considering five areas when

    scaling up a program: cost, human capacity, tools and infrastructure, political support, and non-school

    factors. Some of these areas were suggested by Webers 2006 study in Houston ISD as well and

    certainly could be applicable to the large-scale roll out of RM in Dallas ISD.

    2.3 What were the demographic characteristics of students involved in Reasoning Mind?

    Methodology

    RM student demographic data were exacted from the Dallas ISD Public Education Information

    Management System (PEIMS) database. Specifically, data for enrolled students were pulled from the

    October 29, 2012 fall snapshot file, which is used for state accountability ratings. During the 2012-13

    school year, second- and third-grade students at all but two Dallas ISD elementary Schools (Allen,

    Dealey) were enrolled in RM. Frequency analyses were computed for student demographic

    characteristics by district division and grade level.

    Results

    Based on October 2012 PEIMS data, 26,151 second- and third-grade students were enrolled in

    RM schools during the 2012-13 school year. This included 13,398 (98.8%) of the districts 13,563

    second-grade students and 12,753 (98.8%) of the districts 12,907 third-grade students. Table 1 displays

    demographic characteristics of RM students by division and grade level. Across divisions and grade

    levels, there were slightly more male than female students in all comparisons; the major ethnic groups

    were Hispanic and African American. The majority of RM students (92%) were economically

    disadvantaged, and half (51%) were limited English proficient (LEP). Ten percent were identified as

    gifted, and six percent received special education services. When reviewed by district division, there was

    some demographic and grade-level variation.

  • 9

    Table 1

    Demographic Characteristics of 2012-13 Reasoning Mind Students

    Charac- teristic

    Division 1 N (%)

    Division 2 N (%)

    Division 3 N (%)

    Division 4 N (%)

    Division 5 N (%)

    All N (%)

    RM Second-Grade Students (N=13,398) Gender

    Male 1,457 (51) 1,394 (53) 1,218 (51) 1,180 (53) 1,672 (51) 6,921 (52) Female 1,396 (49) 1,233 (47) 1,185 (49) 1,042 (47) 1,621 (49) 6,477 (48)

    Ethnicity Af. Am. 555 (19) 618 (24) 375 (16) 842 (38) 742 (23) 3,132 (23) Hispanic 2,233 (78) 1,813 (69) 1,914 (80) 1,130 (51) 2,297 (70) 9,387 (70) White 47 (2) 111 (4) 80 (3) 202 (9) 215 (6) 655 (4) Other1 18 (1) 85 (3) 34 (1) 48 (2) 39 (1) 224 (4)

    Eco. Dis. 2,650 (93) 2,399 (91) 2,267 (94) 1,889 (85) 3,098 (94) 12,303 (92) Gifted 194 (7) 211 (8) 257 (11) 244 (11) 283 (9) 1,189 (9) Spec. Ed. 153 (5) 164 (6) 146 (6) 130 (6) 173 (5) 766 (6) LEP 1,589 (56) 1,461 (56) 1,363 (57) 766 (35) 1,631 (50) 6,810 (51)

    Total 2,853 (100) 2,627 (100) 2,403 (100) 2,222 (100) 3,293 (100) 13,398 (100) RM Third-Grade Students (N=12,753)

    Gender Male 1,423 (51) 1,311 (52) 1,120 (52) 1,106 (50) 1,630 (52) 6,590 (52) Female 1,349 (49) 1,211 (48) 1,031 (48) 1,088 (50) 1,484 (48) 6,163 (48)

    Ethnicity Af. Am. 541 (19) 572 (23) 289 (13) 848 (39) 739 (24) 2,989 (23) Hispanic 2,166 (78) 1,762 (70) 1,782 (83) 1,091 (50) 2,175 (70) 8,976 (70) White 47 (2) 110 (4) 46 (2) 213 (10) 152 (5) 568 (5) Other1 18 (1) 78 (3) 34 (2) 42 (1) 48 (1) 220 (2)

    Eco. Dis. 2,573 (93) 2,304 (91) 2,033 (95) 1,839 (84) 2,891 (93) 11,640 (91) Gifted 280 (10) 295 (12) 284 (13) 302 (14) 336 (11) 1497 (12) Spec. Ed. 185 (7) 172 (7) 130 (6) 124 (6) 221 (7) 832 (7) LEP 1,520 (55) 1,416 (56) 1,270 (59) 774 (35) 1,551 (50) 6,531 (51)

    Total 2,772 (100) 2,522 (100) 2,151 (100) 2,194 (100) 3,114 (100) 12,753 (100) All RM Students (N=26,151)

    Gender Male 2,880 (51) 2,705 (53) 2,338 (51) 2,286 (52) 3,302 (52) 13,511 (52) Female 2,745 (49) 2,444 (47) 2,216 (49) 2,130 (48) 3,105 (48) 12,640 (48)

    Ethnicity Af. Am. 1,096 (19) 1,190 (23) 664 (15) 1,690 (38) 1,481 (23) 6,121 (23) Hispanic 4,399 (78) 3,575 (69) 3,696 (81) 2,221 (50) 4,472 (70) 18,363 (70) White 94 (2) 221 (4) 126 (3) 415 (9) 367 (6) 1,223 (5) Other1 36 (1) 163 (3) 68 (1) 90 (2) 87 (1) 444 (2)

    Eco. Dis. 5,223 (93) 4,703 (91) 4,300 (94) 3,728 (84) 5,989 (94) 23,943 (92) Gifted 474 (8) 506 (10) 541 (12) 546 (12) 619 (10) 2,686 (10) Spec. Ed. 338 (6) 336 (7) 276 (6) 254 (6) 394 (6) 1,598 (6) LEP 3,109 (55) 2,877 (56) 2,633 (58) 1,540 (35) 3,182 (50) 13,341 (51)

    Total 5,625 (100) 5,149 (100) 4,554 (100) 4,416 (100) 6,407 (100) 26,151 (100)

    Source. PEIMS district database (10/29/2012) for all second- and third-grade students except for Allen and Dealey Note. Some percentages for ethnicity may not add to 100 due to rounding. 1Other included Asian, American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, two or more races, and not available.

  • 10

    2.4 Did staff members participate in Reasoning Mind training as planned?

    Methodology

    RM staff members extracted professional development completion data and classroom

    observation data from RM databases. Frequency analyses were conducted to note the number and

    percentage of teachers that completed various training courses. Qualification data was reviewed overall,

    by teacher type, by time period of course completion, and by district division. Other professional

    development data were reviewed overall and in some cases by teacher type and district division.

    Classroom observation data were analyzed by rubric indicator.

    Results

    RM provided training for supported teachers, non-supported teachers, and campus

    administrators. All first-year RM supported and non-supported teachers were required to complete the RM

    Qualification Course before they could use RM with their students; as part of the course, teachers had to

    pass an end-of-course assessment. In addition, supported teachers were expected to complete two Best

    Practice Workshops and four Curriculum Study Workshops; both online and in-person training options

    were provided. Non-supported teachers could participate in the Best Practice and Curriculum Study

    training as well, but participation was not a requirement. All online courses were designed to take two

    hours but could be completed flexibly. That is, teachers could begin a course, sign out, and return later to

    pick up where they left off; teachers could sign in and out as many times as necessary without penalty.

    Qualification Course for Teachers

    As mentioned above, both supported and non-supported teachers were required to complete the

    RM Qualification Course before they could use RM with the students. Table 2 shows that a total of 584

    teachers completed the course; this included 129 supported teachers, 402 non-supported teachers, and

    53 inactive teachers. One supported teacher and 25 non-supported teachers never completed the course.

  • 11

    Table 2

    Number and Percentage of Teachers that Completed the Qualification Course by Time Period Supported Non-Supported Inactive* Total N (%) N (%) N (%) N (%) Before 8/27/2012 73 (56) 45 (11) 24 (45) 142 (23) 8/28/2012-10/4/2012 21 (16) 123 (29) 11 (21) 155 (25) 10/05/2012-1/18/2013 33 (25) 198 (46) 17 (32) 248 (41) 1/19/2013-3/08/2013 2 (2) 28 (7) 1 (2) 31 (5) 3/09/2013-5/17/2013 0 (0) 8 (2) 0 (0) 8 (1) Not completed 1 (1) 25 (6) 0 (0) 26 (4) Total 130 (100) 427 (100) 53 (100) 610 (100)

    Note. Some percentages may not add to 100 due to rounding. *Inactive RM teachers received RM training but left the district or were reassigned to another position at some point during the school year.

    Less than half of the teachers (48%) completed the RM Qualification Course by October 4, 2012,

    which meant over half of the students could not use RM during the first six weeks of the school year. As

    seen in Table 2 and Figure 1, notably more supported (72%) than non-supported (40%) teachers

    completed the course by the end of the first six weeks. Almost half of the non-supported teachers (46%)

    finished the course between October 5, 2012 and January 18, 2013 (end of first semester). Two (2%)

    supported teachers and 36 (9%) non-supported teachers completed the course during the spring

    semester; most likely, this was related to teacher turnover. There were 26 teachers that did not meet the

    course requirement by the mid May cut-off date; most (25 of 26) were non-supported teachers.

    Figure 1. Percentage of Supported and Non-Supported RM Teachers that Completed the Qualification Course by Time Period

    56

    16

    25

    2 0 1

    11

    29

    46

    7 26

    0

    10

    20

    30

    40

    50

    60

    Before8/27/12

    8/28/12-10/4/12

    10/05/12-1/18/13

    1/19/13-3/08/13

    3/09/13-5/17/13

    Notcompleted

    % C

    ompl

    eted

    Qua

    lific

    atio

    n Co

    urse

    Supported Non-Supported

  • 12

    Table 3 and Figure 2 show the number of RM teachers who completed the RM Qualification

    Course by district division during 2012-13. For supported teachers, a sizeable proportion in

    Division 1 (58%), Division 2 (63%), and Division 3 (69%) completed the course before school started, but

    less than half did so in Divisions 4 (46%) and 5 (44%); rather, most supported teachers in Divisions 4 and

    5 finished the course during the first semester. The percentage of non-supported teachers that completed

    the course by the end of the first six weeks ranged from 20 percent (Division 4) to 48 percent (Division 5)

    versus 60 percent (Division 4) to 80 percent (Division 2) of supported teachers.

    Table 3

    Number and Percentage of Teachers that Completed the Qualification Course by Division

    Division 1 Division 2 Division 3 Division 4 Division 5

    Supp. Non-Supp.

    Supp.

    Non-Supp.

    Supp.

    Non-Supp.

    Supp.

    Non-Supp.

    Supp.

    Non-Supp.

    N (%) N (%) N (%) N (%) N (%) N (%) N (%) N (%) N (%) N (%) Before 8/27/12 18 (58) 7 (7) 15 (63) 10 (12) 18 (69) 8 (10) 10 (46) 7 (10) 12 (44) 13 (15)8/28/12- 10/4/12 3 (10) 30 (30) 4 (17) 29 (35) 2 (8) 28 (34) 3 (14) 7 (10) 9 (33) 29 (33)10/05/12- 1/18/13 9 (29) 43 (43) 5 (21) 37 (45) 4 (15) 38 (46) 9 (41) 48 (67) 6 (22) 32 (36)1/19/13- 3/08/13 1 (3) 7 (7) 0 (0) 4 (5) 1 (4) 6 (7) 0 (0) 6 (8) 0 (0) 5 (6)3/09/13- 5/17/13 0 (0) 1 (1) 0 (0) 1 (1) 0 (0) 2 (2) 0 (0) 1 (1) 0 (0) 3 (3)Not completed 0 (0) 12 (12) 0 (0) 2 (2) 1 (4) 1 (1) 0 (0) 3 (4) 0 (0) 7 (8)

    Note. Some percentages may not add to 100 due to rounding.

  • 13

    Figure 2. Percentage of RM Teachers that Completed the Qualification Course by Division

    Best Practice and Curriculum Study Workshops for Teachers

    In addition to the RM Qualification Course, supported teachers were required to earn six credits

    (12 hours) of RM professional development. Teachers that completed the requirement received a $500

    stipend. First-year RM teachers were expected to complete four Curriculum Study Workshops and two

    Best Practice Workshops. Teachers that had implemented RM for more than a year could choose any

    combination of Best Practice and Curriculum Study Workshops to meet the six-credit requirement.

    Teachers could also earn credits beyond the required credits if they chose to do so. Teachers had the

    choice of numerous in-person sessions during the fall and spring semesters along with online options.

    The in-person Curriculum Study and Best Practice Workshops were held at RMs Dallas office.

    Overall, 92 percent of supported teachers (133 out of 144) attained the six-credit professional

    development requirement; 85 percent met the Best Practice Workshop attendance expectation, and 83

    percent did so for the Curriculum Study Workshop requirement. Very few non-supported teachers

    (N=11; 2%) participated in workshops, and none completed six credits. Figure 3 shows the percentage of

    supported teachers that met the six-credit training requirement by division. Over 90 percent of the

    teachers in Division 1 (94%), Division 4 (96%) and Division 5 (94%) met the requirement, and 89 percent

    of teachers in Divisions 2 and 3 did so.

    5863

    69

    46 44

    712 10 10 15

    39 38

    23

    55 55

    7380 80 77

    69

    3 4 8 69 9 9

    412

    2 14 8

    0102030405060708090

    100

    Div. 1 Div. 2 Div. 3 Div. 4 Div. 5 Div.1 Div. 2 Div. 3 Div. 4 Div. 5

    Supported Non-supported

    % C

    ompl

    eted

    Qua

    lific

    atio

    n Co

    urse

    Before Fall 2012 During Fall 2012 During Spring 2013 Not completed

  • 14

    Figure 3. Percentage of Supported Teachers that Met RM Training Requirements

    Professional Development for Campus Administrators At the request of campus administrators, training sessions were provided during July and August

    2012. The purpose of the training was to give campus administrators an overview of the RM program. In

    all, 55 campus administrators from 51 elementary schools attended training. Attendees included

    principals (N=44), assistant principals (N=3), former principals (N=3), instructional coaches (N=4), and

    other (N=1). When reviewed by divisions, there were 10 from Division 1, 13 from Division 2, 15 from

    Division 3, 7 from Division 4, and 10 from Division 5. There were four schools represented by two

    administrators each; this included one school in Division 2, two in Division 4, and one in Division 5.

    RM Teacher Observations

    In addition to formal professional development sessions, RM program coordinators formally

    observed each supported teacher at least three times a year. Non-supported teachers were not formally

    observed. During the 45-minute observations, RM coordinators used an implementation rubric to rate the

    teachers in 10 areas. The rubric was used to monitor implementation fidelity as well as to help teachers

    identify strengths and areas that could be improved. Besides formal observations, program coordinators

    informally observed and visited with supported teachers throughout the school year to provide feedback,

    ideas, and suggested resources. Program coordinators also visited and provided ideas to non-supported

    teachers as time allowed. For example, the program coordinators helped teachers think through ways to

    Div. 1 Div. 2 Div. 3 Div. 4 Div. 5 District% Did Not Meet 6% 11% 11% 4% 6% 8%% Met 94% 89% 89% 96% 94% 92%

    0%

    20%

    40%

    60%

    80%

    100%

    % o

    f Sup

    port

    ed T

    each

    ers

    Mee

    ting

    PD R

    equi

    rem

    ent

  • 15

    overcome technology issues and classroom management challenges. Teachers also had access to

    tutorials and RM-hosted symposiums.

    The classroom observation rubric included ten indicators and four possible ratings (not

    established, established, proficient, advanced). Teachers were to aim for achieving at least proficient on

    each indicator. However, the main goal was to see improvement over the course of the year. For

    example, if a teacher began as not established, the program coordinator provided feedback to help the

    teacher move up to established. Program coordinators tried to spread observations out across the year

    to assess for growth over time. However, per one program coordinator, many of the observations could

    not begin until into the second semester due to teacher turnover or late program launches.

    Of the 144 supported teachers included in the database, most (97%) were observed three times,

    while the remaining teachers were observed four times. As would be hoped, across the indicators, the

    number of teachers who received ratings of not established and established tended to decrease over

    time, whereas the number of teachers who attained ratings of proficient or advanced increased. (See

    Table 4 and Figures 4 to 13.) Data from the fourth observation were not included in the figures due to the

    small number of teachers that were observed four times.

  • 16

    Table 4

    Teacher Observation Ratings Based on the RM Rubric

    Observation 1

    (N=144) Observation 2

    (N=144) Observation 3

    (N=140) Observation 4

    (N=4) Indicator Rating N % N % N % N % Data Driven Decisions

    Not Established 35 24 19 13 9 6 0 0 Established 60 42 23 16 26 19 0 0 Proficient 42 29 72 50 44 31 1 25 Advanced 7 5 30 21 61 44 3 75

    Lesson Planning

    Not Established 118 82 51 35 12 9 0 0 Established 22 15 70 49 85 61 2 50 Proficient 4 3 21 15 30 21 2 50 Advanced 0 0 2 1 13 9 0 0

    Instructional Methods

    Not Established 109 76 61 42 26 18 2 50 Established 24 17 42 29 42 30 1 25 Proficient 8 5 30 21 46 33 1 25 Advanced 3 2 11 8 26 19 0 0

    Learning Modes

    Not Established 119 83 67 47 41 29 1 25 Established 17 12 40 28 42 30 1 25 Proficient 5 3 25 17 23 17 2 50 Advanced 3 2 12 8 34 24 0 0

    Teacher Engagement

    Not Established 24 17 15 10 7 5 0 0 Established 32 22 26 18 21 15 0 0 Proficient 33 23 33 23 32 23 2 50 Advanced 55 38 70 49 80 57 2 50

    Procedures Not Established 15 10 10 7 8 6 0 0 Established 37 26 25 17 20 14 1 25 Proficient 69 48 68 47 58 41 1 25 Advanced 23 16 41 29 54 39 2 50

    Incentive Systems

    Not Established 67 46 22 15 6 4 0 0 Established 73 51 88 61 71 51 2 50 Proficient 4 3 24 17 35 25 2 50 Advanced 0 0 10 7 28 20 0 0

    Notebooks Not Established 96 67 62 43 28 20 1 25 Established 39 27 61 42 75 54 3 75 Proficient 9 6 18 13 30 21 0 0 Advanced 0 0 3 2 7 5 0 0

    Independent Learning

    Not Established 85 59 40 28 14 10 0 0 Established 54 37 98 68 112 80 2 50 Proficient 4 3 3 2 5 4 0 0 Advanced 1 1 3 2 9 6 2 50

    Student Engagement

    Not Established 17 12 10 7 5 3 0 0 Established 35 24 35 24 26 19 1 25 Proficient 12 8 13 9 8 6 0 0 Advanced 80 56 86 60 101 72 3 75

  • 17

    Figure 4. Percentage Distribution of Observation Ratings: Data Driven Decisions

    Figure 5. Percentage Distribution of Observation Ratings: Lesson Planning

    6

    13

    24

    19

    16

    42

    31

    50

    29

    44

    21

    5

    0% 20% 40% 60% 80% 100%

    Obs. 3

    Obs. 2

    Obs. 1

    Data Driven Decisions

    Not Yet Established Established Proficient Advanced

    9

    35

    82

    61

    49

    15

    21

    15

    3

    9

    1

    0% 20% 40% 60% 80% 100%

    Obs. 3

    Obs. 2

    Obs. 1

    Lesson Planning

    Not Yet Established Established Proficient Advanced

  • 18

    Figure 6. Percentage Distribution of Observation Ratings: Instructional Methods

    Figure 7. Percentage Distribution of Observation Ratings: Learning Modes

    18

    42

    76

    30

    29

    17

    33

    21

    5

    19

    8

    2

    0% 20% 40% 60% 80% 100%

    Obs. 3

    Obs. 2

    Obs. 1

    Instructional Methods

    Not Yet Established Established Proficient Advanced

    29

    47

    83

    30

    28

    12

    17

    17

    3

    24

    8

    2

    0% 20% 40% 60% 80% 100%

    Obs. 3

    Obs. 2

    Obs. 1

    Learning Modes

    Not Yet Established Established Proficient Advanced

  • 19

    Figure 8. Percentage Distribution of Observation Ratings: Teacher Engagement

    Figure 9. Percentage Distribution of Observation Ratings: Procedures

    5

    10

    17

    15

    18

    22

    23

    23

    23

    57

    49

    38

    0% 20% 40% 60% 80% 100%

    Obs. 3

    Obs. 2

    Obs. 1

    Teacher Engagement

    Not Yet Established Established Proficient Advanced

    6

    7

    10

    14

    17

    26

    41

    47

    48

    39

    29

    16

    0% 20% 40% 60% 80% 100%

    Obs. 3

    Obs. 2

    Obs. 1

    Procedures

    Not Yet Established Established Proficient Advanced

  • 20

    Figure 10. Percentage Distribution of Observation Ratings: Incentive Systems

    Figure 11. Percentage Distribution of Observation Ratings: Notebooks

    4

    15

    46

    51

    61

    51

    25

    17

    3

    20

    7

    0% 20% 40% 60% 80% 100%

    Obs. 3

    Obs. 2

    Obs. 1

    Incentive Systems

    Not Yet Established Established Proficient Advanced

    20

    43

    67

    54

    42

    27

    21

    13

    6

    5

    2

    0% 20% 40% 60% 80% 100%

    Obs. 3

    Obs. 2

    Obs. 1

    Notebooks

    Not Yet Established Established Proficient Advanced

  • 21

    Figure 12. Percentage Distribution of Observation Ratings: Independent Learning

    Figure 13. Percentage Distribution of Observation Ratings: Student Engagement

    Table 5 shows rating results from the third observation by indicator. The majority of teachers

    received proficient or advanced for data driven decisions (75%), teacher engagement (80%),

    procedures (80%), and student engagement (78%). Thus, teachers used data to guide individual and

    small group instruction, worked with students during most of RM time, and had good procedures in place

    to ensure students logged in quickly and so forth. About half (52%) attained proficient or advanced for

    10

    28

    59

    80

    68

    37

    4

    2

    3

    6

    2

    1

    0% 20% 40% 60% 80% 100%

    Obs. 3

    Obs. 2

    Obs. 1

    Independent Learning

    Not Yet Established Established Proficient Advanced

    3

    7

    12

    19

    24

    24

    6

    9

    8

    72

    60

    56

    0% 20% 40% 60% 80% 100%

    Obs. 3

    Obs. 2

    Obs. 1

    Student Engagement

    Not Yet Established Established Proficient Advanced

  • 22

    instructional methods, which means about half of the teachers differentiated student instruction during

    observed interventions.

    Less than half scored proficient or advanced for lesson planning (30%), learning modes (40%),

    incentive systems (45%), notebooks (26%), and independent learning (10%). The majority scored

    established (61%) for lesson planning because they chose the students and objectives to focus on

    during the lesson rather than before. Many teachers did not achieve proficient for learning modes

    because their students did not spend enough time in review mode; this means low-performing students

    were given fewer opportunities to review basic computation questions, and high-performing students

    missed chances to work on more rigorous problems. In the case of incentive systems, teachers must set

    both class and individual goals to reach proficient; thus, over half (55%) did not set both types of goals.

    Sometimes it takes teachers longer to reach proficient for the notebook indicator because taking notes

    is a new skill for many students. To receive a rating of proficient on independent learning, no more than

    three students can skip the Genie Solution (instructional feedback) when they miss a question; the

    purpose of this indicator is to ensure that students learn from their mistakes rather than moving forward

    without an understanding of why they missed a question.

    Table 5

    Teacher Observation Ratings by Indicator for Observation 3

    Indicator

    Not Established

    N (%)

    Established

    N (%)

    Proficient

    N (%)

    Advanced

    N (%) Data Driven Decisions 9 (6) 26 (19) 44 (31) 61 (44) Lesson Planning 12 (9) 85 (61) 30 (21) 13 (9) Instructional Methods 26 (19) 42 (30) 46 (33) 26 (19) Learning Modes 41 (29) 42 (30) 23 (16) 34 (24) Teacher Engagement 7 (5) 21 (15) 32 (23) 80 (57) Procedures 8 (6) 20 (14) 58 (41) 54 (39) Incentive Systems 6 (4) 71 (51) 35 (25) 28 (20) Notebooks 28 (20) 75 (54) 30 (21) 7 (5) Independent Learning 14 (10) 112 (80) 5 (4) 9 (6) Student Engagement 5 (4) 26 (19) 8 (6) 101 (72)

    Note. Some percentages may not add to 100 due to rounding.

  • 23

    Table 6 shows the percentage of teachers that had decreased or increased ratings from

    Observation 1 to 3 as well as the percentage that had no change. The majority of teachers had increased

    ratings for data driven decisions (71%), lesson planning (81%), instructional methods (69%), learning

    modes (64%), incentive systems (71%), notebooks (59%), and independent learning (58%). In contrast,

    half (51%) had no change in ratings for student engagement; this is likely because the majority of

    teachers were proficient or advanced from the start. Similarly, results were mixed for teacher

    engagement and procedures; again, over half were proficient or established from the first observation

    on. In general, there was a pattern of increase across time even if teachers did not reach proficient in all

    areas.

    Table 6

    Teacher Observation Rating Changes from

    Observation 1 to 3 by Indicator

    Decreased No Change Increased Indicator N (%) N (%) N (%) Data Driven Decisions 11 (8) 30 (21) 99 (71) Lesson Planning 1 (1) 25 (18) 114 (81) Instructional Methods 5 (4) 38 (27) 97 (69) Learning Modes 6 (4) 45 (32) 89 (64) Teacher Engagement 20 (14) 60 (43) 60 (43) Procedures 19 (14) 53 (38) 68 (49) Incentive Systems 1 (1) 39 (28) 100 (71) Notebooks 8 (6) 50 (36) 82 (59) Independent Learning 8 (6) 51 (36) 81 (58) Student Engagement 24 (17) 72 (51) 44 (31)

    Note. Some percentages may not add to 100 due to rounding.

    2.5 Was the Reasoning Mind program used by students as planned?

    Methodology

    RM provided second- and third-grade student data files for the fall and spring semesters. The

    data files included students time spent online using RM, number of objectives completed, and math

    problem accuracy rates. Data were aggregated to prepare for school-level analyses. The evaluators

    conducted frequency and descriptive analyses to determine student- and school-level implementation

    related to time online, objectives mastered, and accuracy rates within various modes of the RM program.

  • 24

    The evaluators dealt with two challenges related to RM data files. First, RM did not receive full

    district data files except at the very beginning of the year and did not have access to student identification

    numbers until March. As a result, RM did not know when students were not included and/or moved in and

    out of the district. Upon teacher request to add student accounts, the RM district coordinator worked with

    district technology staff to get the information to RM; however, no ongoing, automatic updates were in

    place. Second, the fall files used in this report differ from the fall files used in the interim report because

    RM provided updated data that included many students that were missing in the previous fall files. The

    evaluators used the updated fall files to be as accurate as possible.

    Results

    Student Hour Results

    To meet RMs two hour per week requirement, the number of hours students spent online should

    be approximately 35 hours each semester; the evaluators used 30 hours or more per semester as the

    actual goal to compensate for instructional time lost due to holidays or school events such as assemblies

    or field trips. In general, average implementation in terms of hours online was about 35 percent of what it

    should have been in the fall and about 80 percent of what it should have been in the spring. As seen in

    Figures 14 and 15, mean hours of student use increased from 10.71 (fall) to 24.19 (spring) for

    second-grade students and from 10.52 (fall) to 24.33 (spring) for third-grade students. Whereas a few

    second- (2%) and third-grade (1%) students met the 30-hour goal in the fall, about a third of second-

    (31%) and third-grade (32%) students did so in the spring. In the fall, about half of second- (50%) and

    third-grade (52%) students logged less than 10 hours. As for spring, approximately 60 percent of second-

    (63%) and third-grade (61%) students logged 20 or more hours. In comparison to 2011-12, the fall 2013

    mean of 10.71 hours was lower than fall 2012 hours (13.40) for second grade; however, the spring 2013

    mean (24.19) was higher than in spring 2012 (17.20).

  • 25

    Figure 14. Mean Hours Students Spent on RM in Fall and Spring by Grade

    Figure 15. Percentage of Students in RM Hour Categories by Grade and Semester Tables 7 and 8 display overall and division-level student hour information by semester and grade.

    In general, there were increases from fall to spring for all divisions. Almost half of Division 1 second-

    (49%) and third-grade students (48%) spent 30 or more hours online in the spring; percentages varied for

    other second- (21% to 34%) and third-grade (19% to 34%) spring division comparisons. The percentages

    of students with 20 or more hours ranged from 55 percent (Division 3) to 80 percent (Division 1) for

    second-grade students and from 48 percent (Division 3) to 80 percent (Division 1) for third-grade

    students.

    10.71 10.52

    24.19 24.33

    0

    10

    20

    30

    2nd Grade 3rd Grade

    Mean

    Fall

    Spring

    2nd GradeFall

    2nd GradeSpring

    3rd GradeFall

    3rd GradeSpring

    30+ Hours 2 31 1 3220-29.99 Hours 11 32 11 2910-19.99 Hours 37 24 36 250-9.99 Hours 50 13 52 14

    0

    20

    40

    60

    80

    100Percentage

    30+ Hours

    20-29.99 Hours

    10-19.99 Hours

    0-9.99 Hours

  • 26

    Table 7

    Number of Hours Students Spent on RM in Fall and Spring by Grade and Division Second Grade Third Grade Fall

    N (%) Spring N (%)

    Fall N (%)

    Spring N (%)

    All 0-9.99 hours 6,770 (50) 1,715 (13) 6,676 (52) 1,739 (14) 10-19.99 hours 5,063 (37) 3,238 (24) 4,606 (36) 3,235 (25) 20-29.99 hours 1,425 (11) 4,312 (32) 1,366 (11) 3,763 (29) 30+ hours 228 (2) 4,188 (31) 182 (1) 4,084 (32)

    Division 1 0-9.99 hours 1,385 (48) 158 (5) 1,668 (58) 171 (6) 10-19.99 hours 1,277 (44) 447 (15) 805 (28) 399 (14) 20-29.99 hours 232 (8) 883 (31) 329 (12) 901 (32) 30+ hours 11 (

  • 27

    Table 8

    Descriptive Statistics for Reasoning Mind Hours by Grade and Division

    Second Grade Third Grade Fall Spring Fall Spring

    All Number of students 13,486 13,453 12,830 12,821 Range of hours 0.00-68.93 0.00-131.91 0.00-72.91 0.00-159.56 Mean hours 10.71 24.19 10.52 24.33 Standard deviation 7.96 12.73 7.76 13.26

    Division 1 Number of students 2,904 2,896 2,857 2,851 Range of hours 0.00-42.87 0.00-131.91 0.00-50.61 0.00-103.45 Mean hours 10.36 28.62 10.24 29.47 Standard deviation 6.83 11.32 7.99 12.51

    Division 2 Number of students 2,495 2,487 2,428 2,430 Range of hours 0.00-68.93 0.00-124.46 0.00-57.56 0.00-148.02 Mean hours 11.45 25.67 11.43 24.92 Standard deviation 9.18 14.00 7.53 13.33

    Division 3 Number of students 2,456 2,448 2,146 2,142 Range of hours 0.00-37.83 0.00-85.95 0.00-49.33 0.00-159.56 Mean hours 8.97 21.29 8.46 20.64 Standard deviation 6.60 12.50 6.29 12.94

    Division 4 Number of students 2,272 2,269 2,247 2,265 Range of hours 0.00-46.91 0.00-69.44 0.00-57.60 0.00-110.45 Mean hours 9.49 22.75 8.92 24.53 Standard deviation 7.78 12.72 7.57 13.30

    Division 5 Number of students 3,359 3,353 3,152 3,133 Range of hours 0.00-65.22 0.00-87.00 0.00-72.91 0.00-97.49 Mean hours 12.55 22.35 12.63 21.57 Standard deviation 8.48 11.91 8.15 12.49 School Hour Results

    As would be expected, school-level data mirrored student-level data. The percentage of schools

    that averaged thirty or more hours increased from fall to spring for both second (1% to 29%) and third

    grade (0% to 29%). Even so, less than a third (29%) of the RM schools averaged at least 30 hours in the

    spring. (See Figure 16.) Most RM schools (66%) averaged 20 hours or more in the spring. When viewed

    by division, Division 1 had the highest level of implementation in the spring, whereas Divisions 3 and 5

    had the lowest. (See Table 9.)

  • 28

    Figure 16. Percentage of Schools in RM Hour Categories by Grade and Semester

    2nd GradeFall

    2nd GradeSpring

    3rd GradeFall

    3rd GradeSpring

    30+ Hours 1 29 0 2920-29.99 Hours 8 37 7 3710-19.99 Hours 45 29 46 280-9.99 Hours 46 5 47 6

    0

    20

    40

    60

    80

    100Percentage

    30+ Hours

    20-29.99 Hours

    10-19.99 Hours

    0-9.99 Hours

  • 29

    Table 9

    School Information by RM Hour Categories in the Fall and Spring by Grade and Division Second Grade Third Grade Fall

    N (%) Spring N (%)

    Fall N (%)

    Spring N (%)

    All 0-9.99 hours 67 (46) 8 (6) 67 (46) 8 (6) 10-19.99 hours 65 (45) 41 (28) 67 (46) 40 (28) 20-29.99 hours 12 (8) 53 (37) 10 (7) 54 (37) 30+ hours1 1 (1) 42 (29) 0 (0) 42 (29)

    Division 1 0-9.99 hours 14 (45) 0 (0) 17 (55) 1 (3) 10-19.99 hours 15 (48) 5 (16) 11 (36) 4 (13) 20-29.99 hours 2 (7) 11 (36) 3 (10) 11 (35) 30+ hours 0 (0) 15 (48) 0 (0) 15 (48)

    Division 2 0-9.99 hours 12 (43) 1 (4) 12 (43) 1 (4) 10-19.99 hours 12 (43) 6 (21) 15 (54) 8 (29) 20-29.99 hours 3 (11) 11 (39) 1 (4) 9 (32) 30+ hours 1 (4) 10 (36) 0 (0) 10 (36)

    Division 3 0-9.99 hours 14 (52) 3 (11) 14 (52) 4 (15) 10-19.99 hours 11 (41) 9 (33) 13 (48) 8 (30) 20-29.99 hours 2 (7) 11 (41) 0 (0) 11 (41) 30+ hours 0 (0) 4 (15) 0 (0) 4 (15)

    Division 4 0-9.99 hours 14 (56) 1 (4) 15 (60) 0 (0) 10-19.99 hours 11 (44) 8 (32) 8 (32) 7 (28) 20-29.99 hours 0 (0) 10 (40) 2 (8) 10 (40) 30+ hours 0 (0) 6 (24) 0 (0) 8 (32)

    Division 5 0-9.99 hours 13 (38) 3 (9) 10 (29) 2 (6) 10-19.99 hours 16 (47) 13 (38) 20 (59) 14 (41) 20-29.99 hours 5 (15) 11 (32) 4 (12) 13 (38) 30+ hours 0 (0) 7 (21) 0 (0) 5 (15)

    Note. Some percentages may not add to 100 due to rounding. 1If the 100+ missing second-grade students were included for Titche, there would be no schools in the highest implementation category in the fall. There were 15 students in the RM files versus 134 in the fall PEIMS file.

    Schools within each hour category were rank ordered by division and average RM hours. (See

    Tables 10 to 13.) It should be noted that many Titche students were not included in the RM files.

    Specifically, there were 15 Titche second-grade students in the fall and spring RM files versus 134 in the

    October 2012 PEIMS file; as for third-grade students, there were 42 in the fall RM file and 43 in the spring

    RM file versus 116 in the October PEIMS file. As a result, the Titche averages for time online are

    misleading and would be much lower if the missing students with no time in RM were included.

  • 30

    Table 10

    RM Second-Grade Fall Hour Information with Schools Rank Ordered by Division and Mean Hours Online

    Division School

    Number of

    StudentsMean Hours

    Minimum Hours

    Maximum Hours

    Standard Deviation

    0-9.99 Hours 1 Twain 62 0.84 0.00 13.74 2.13 1 Foster 123 0.91 0.00 9.62 1.06 1 Weiss 84 4.21 0.00 9.46 1.70 1 Rosemont 181 4.41 0.00 19.87 1.98 1 Alexander 72 5.52 0.00 17.09 4.16 1 Saldivar 151 6.07 0.00 12.49 2.19 1 Carpenter 58 6.26 0.00 31.55 7.78 1 Webster 123 6.44 0.00 23.30 3.25 1 Terry 61 7.22 0.00 14.08 4.10 1 Kahn 103 8.33 0.00 22.62 4.70 1 Turner 58 8.49 0.00 22.42 4.03 1 Hooe 75 9.12 0.00 24.50 7.28 1 Field 69 9.26 0.00 18.67 3.90 1 U. Lee 90 9.95 0.00 26.84 6.67 2 DeGolyer 54 0.00 0.00 0.00 0.00 2 San Jacinto 98 0.25 0.00 7.57 1.27 2 Gooch 65 2.16 0.00 31.12 5.35 2 Bryan 80 4.51 0.00 16.91 3.35 2 Blanton 94 4.99 0.00 14.15 2.13 2 McShan 97 5.37 0.00 12.66 2.72 2 J. Adams 97 6.33 0.00 16.89 4.55 2 Mills 78 7.36 0.00 18.96 4.69 2 H. Meadow 136 7.39 0.00 16.79 3.56 2 Cabell 106 7.62 0.00 23.39 4.14 2 Lowe 115 8.01 0.00 27.41 4.62 2 Caillet 109 9.81 0.00 52.34 6.26 3 Salazar 127 1.42 0.00 10.21 1.70 3 Kramer 89 1.65 0.00 13.57 1.62 3 Frank 179 3.09 0.00 7.36 1.60 3 Stevens 113 3.60 0.00 15.95 2.46 3 Zaragoza 87 3.71 0.00 10.12 3.02 3 Lanier 84 3.73 0.00 6.13 1.56 Continued

  • 31

    Table 10 Continued

    Division School

    Number of

    StudentsMean Hours

    Minimum Hours

    Maximum Hours

    Standard Deviation

    3 Soto 118 6.32 0.00 11.34 1.48 3 Carr 81 6.66 0.00 18.61 2.56 3 Pershing 67 7.01 0.00 15.86 2.87 3 Ray 74 8.30 0.00 15.90 4.65 3 E. Medrano 99 8.45 0.00 12.88 2.77 3 Houston 47 8.68 0.00 21.55 6.10 3 Preston Hollow 62 8.83 0.00 16.10 4.56 3 Bethune 125 9.11 0.00 14.91 2.91 4 Callejo 83 0.00 0.00 0.27 0.03 4 Young 115 0.59 0.00 20.46 2.65 4 Guzick 114 0.73 0.00 6.07 1.01 4 Marsalis 79 1.22 0.00 26.48 3.13 4 Lipscomb 88 3.25 0.00 9.98 1.83 4 Thornton 53 3.95 0.00 7.48 1.90 4 Truett 157 4.79 0.00 18.83 4.00 4 Mata 59 4.94 0.00 8.90 1.55 4 Bushman 85 6.34 0.00 21.77 6.89 4 Pease 95 6.68 0.00 20.49 3.87 4 Conner 91 8.32 0.00 22.41 4.94 4 Urban Park 115 8.56 0.00 14.38 3.34 4 Lisbon 52 8.86 0.00 15.79 3.03 4 H. Stone 49 9.05 4.69 16.25 2.83 5 Douglass 109 2.91 0.00 23.14 3.86 5 Sanger 76 3.07 0.00 15.07 2.67 5 Ervin 103 3.52 0.00 23.73 4.28 5 Reagan 79 4.37 0.00 29.34 3.66 5 Wilmer-Hutchins 117 4.64 0.00 14.90 3.86 5 Casa View 114 6.15 0.00 10.38 1.93 5 Bayles 110 6.56 0.00 12.86 3.80 5 Lagow 106 6.86 0.00 21.73 3.82 5 Botello 88 7.60 0.93 16.57 1.99 5 Moseley 109 8.26 0.00 19.32 3.00 5 Smith 169 8.87 0.00 19.25 5.73 5 Reilly 84 8.99 0.00 21.20 3.10 5 Reinhardt 109 9.54 0.00 16.48 3.56 Continued

  • 32

    Table 10 Continued

    Division School

    Number of

    StudentsMean Hours

    Minimum Hours

    Maximum Hours

    Standard Deviation

    10-19.99 Hours 1 Brashear 93 10.01 0.00 26.51 8.95 1 McNair 147 11.03 0.00 22.58 3.58 1 Burnet 187 11.13 0.00 32.90 4.34 1 Moreno 80 11.52 0.00 19.98 4.69 1 Knight 106 11.56 0.00 21.64 4.86 1 Cigarroa 99 12.60 0.00 24.49 5.30 1 Donald 67 12.94 0.00 19.02 3.70 1 Hall 88 13.25 0.00 22.31 4.39 1 Winnetka 113 14.11 0.00 40.69 3.96 1 Williams 45 14.12 0.00 22.96 6.41 1 Peabody 85 14.30 0.00 22.35 4.41 1 Stemmons 124 15.90 0.00 26.21 4.34 1 Jones 122 16.08 0.00 33.66 5.32 1 Walnut Hill 45 17.14 0.00 24.70 4.82 1 Tolbert 67 19.45 0.00 42.87 7.56 2 Ireland 106 10.15 0.00 26.22 6.33 2 Runyon 92 10.77 0.00 17.41 3.63 2 Bush 117 11.48 0.00 30.52 6.11 2 Marcus 156 12.74 0.00 24.41 4.09 2 Hotchkiss 153 14.20 0.00 37.64 6.32 2 J. Stone 50 14.56 0.00 21.88 5.36 2 N. Adams 75 15.94 0.00 29.83 5.25 2 Budd 79 17.27 0.00 28.11 5.63 2 Withers 75 17.85 10.31 33.29 4.63 2 Junkins 105 18.23 0.00 59.40 10.89 2 Hawthorne 67 18.76 0.00 25.95 4.31 2 Starks 51 18.77 0.00 41.97 11.19 3 Earhart 47 10.47 0.00 16.98 5.32 3 Martinez 85 10.63 0.00 21.84 5.15 3 Arcadia Park 125 10.71 0.00 26.61 5.08 3 DeZavala 58 11.04 1.85 24.28 7.00 3 Carver 76 11.08 0.00 20.34 5.44 3 Chavez 107 11.53 0.00 23.87 4.45 3 Cochran 96 12.01 0.00 26.28 5.00 Continued

  • 33

    Table 10 Continued

    Division School

    Number of

    StudentsMean Hours

    Minimum Hours

    Maximum Hours

    Standard Deviation

    3 Kennedy 115 12.76 0.00 30.39 4.27 3 Hernandez 65 13.02 0.00 31.38 6.38 3 Milam 49 13.37 0.00 19.85 3.26 3 Cowart 97 14.86 0.00 25.76 5.15 4 R. Lee 52 10.40 0.00 27.05 4.40 4 Tatum 111 12.53 0.00 36.18 6.04 4 Lakewood 131 13.08 0.00 45.74 6.37 4 Oliver 49 14.35 0.00 22.92 4.26 4 Dunbar 100 14.97 0.00 25.81 6.47 4 Mt. Auburn 138 15.34 0.00 23.80 4.43 4 Rowe 65 15.71 5.51 23.18 3.69 4 King 74 15.86 0.00 41.66 8.13 4 S. Jackson 114 16.29 0.00 46.91 6.47 4 Jordan 86 19.22 0.00 29.13 7.55 4 Russell 117 19.64 0.00 36.61 6.35 5 Blair 95 10.24 0.00 20.76 6.33 5 Kiest 114 10.74 0.00 17.24 4.18 5 Rhoads 87 11.24 0.00 38.91 8.60 5 Gonzalez 128 11.55 0.00 26.24 6.23 5 Cuellar 132 11.57 0.00 20.08 4.27 5 Hexter 100 13.44 0.00 27.40 5.44 5 Silberstein 116 14.65 4.18 18.22 2.30 5 Hogg 45 15.85 0.00 41.76 8.61 5 Central 83 16.17 0.00 52.21 9.98 5 W. Anderson 107 16.54 0.37 30.74 6.23 5 Rice 87 16.57 0.00 41.46 8.89 5 Bowie 63 16.90 0.00 22.37 4.70 5 Burleson 104 17.06 0.00 39.05 6.61 5 Macon 79 18.05 0.00 30.35 6.28 5 Seagoville North 112 19.53 0.00 47.73 6.28 5 Dorsey 63 19.99 0.00 28.81 5.03

    20-29.99 Hours 1 Henderson 67 21.90 0.00 31.73 5.29 1 Polk 59 22.38 8.43 36.16 3.75 2 Pleasant Grove 99 21.66 0.00 56.61 16.11 Continued

  • 34

    Table 10 Continued

    Division School

    Number of

    StudentsMean Hours

    Minimum Hours

    Maximum Hours

    Standard Deviation

    2 Johnston 75 22.90 0.00 41.87 8.22 2 Miller 51 22.98 5.79 46.00 5.69 3 Rogers 88 20.28 0.00 37.83 9.58 3 Maple Lawn 96 20.88 0.00 31.24 4.59 5 Seagoville 86 20.33 0.00 39.29 5.34 5 Kleberg 92 20.43 0.00 32.70 5.92 5 Gill 153 23.21 0.00 35.61 7.24 5 Peeler 53 27.01 0.00 65.22 9.15 5 Halliday 87 29.03 7.46 36.63 3.88

    30+ Hours1 2 Titche1 15 46.94 6.24 68.93 13.70

    1Many Titche students were missing in the RM file; there were 134 second-grade students in the PEIMS file. The mean of 46.94 is misleading and was not the true level of implementation at Titche.

  • 35

    Table 11

    RM Third-Grade Fall Hour Information with Schools Rank Ordered by Division and Mean Hours Online

    Division School

    Number of

    StudentsMean Hours

    Minimum Hours

    Maximum Hours

    Standard Deviation

    0-9.99 Hours 1 Alexander 58 2.67 0.00 19.92 3.36 1 Weiss 83 3.08 0.00 9.38 1.84 1 Moreno 110 3.18 0.00 9.67 1.64 1 Rosemont 173 3.26 0.00 23.53 2.22 1 McNair 132 3.60 0.00 17.15 3.34 1 Saldivar 153 3.78 0.00 14.63 2.16 1 Carpenter 52 4.90 0.00 10.74 3.24 1 Burnet 159 5.75 0.00 12.10 3.13 1 Brashear 93 6.13 0.00 16.60 2.03 1 Twain 56 6.53 0.00 15.26 4.75 1 Foster 121 7.14 0.00 13.39 2.55 1 Turner 63 8.29 0.00 15.31 2.46 1 Winnetka 124 8.32 0.00 14.61 2.16 1 Hooe 63 8.49 0.00 18.05 3.76 1 Cigarroa 83 9.40 0.00 24.18 3.80 1 Terry 69 9.46 0.00 17.08 4.05 1 Tolbert 76 9.60 0.00 21.55 4.61 2 San Jacinto 102 0.24 0.00 8.24 1.14 2 Gooch 70 0.63 0.00 5.45 0.99 2 DeGolyer 61 2.59 0.00 7.30 1.34 2 Mills 71 4.74 0.00 12.91 3.47 2 Blanton 108 5.40 0.00 8.79 2.11 2 N. Adams 73 7.54 0.78 16.21 4.67 2 Withers 66 7.64 0.00 15.30 3.47 2 H. Meadow 146 8.56 0.00 16.12 3.90 2 Ireland 108 8.61 0.00 21.59 5.64 2 Caillet 105 8.93 4.00 20.23 2.88 2 Marcus 137 9.72 0.00 26.97 3.88 2 Cabell 73 9.83 0.00 28.18 3.98 3 Stevens 97 0.00 0.00 0.00 0.00 3 Pershing 83 1.34 0.00 6.19 1.63 Continued

  • 36

    Table 11 Continued

    Division School

    Number of

    StudentsMean Hours

    Minimum Hours

    Maximum Hours

    Standard Deviation

    3 Zaragoza 54 1.66 0.00 9.00 1.59 3 Kramer 95 1.94 0.00 5.08 1.03 3 Preston Hollow 19 3.12 0.00 4.97 1.18 3 Frank 169 3.46 0.00 11.54 1.74 3 Soto 92 4.65 0.00 10.08 1.84 3 Carver 74 4.91 0.00 25.36 6.17 3 Lanier 90 5.11 0.00 9.43 2.12 3 E. Medrano 91 5.90 0.00 21.43 4.64 3 Martinez 78 6.61 0.00 11.20 2.21 3 Chavez 70 7.57 0.00 19.91 4.02 3 Earhart 30 8.97 0.00 15.97 4.19 3 Carr 77 8.99 0.00 29.76 4.40 4 Young 108 0.74 0.00 19.51 2.74 4 Thornton 73 0.84 0.00 2.19 0.62 4 Guzick 104 1.16 0.00 44.30 5.17 4 Lipscomb 88 2.50 0.00 4.37 0.88 4 R. Lee 56 2.55 0.00 6.27 1.34 4 Truett 177 4.88 0.00 18.35 2.93 4 Marsalis 96 5.36 0.00 11.17 3.08 4 Pease 94 5.54 0.00 17.46 3.55 4 Lakewood 136 6.14 0.00 11.45 2.39 4 Mata 80 7.35 0.00 13.88 2.82 4 H. Stone 48 7.63 1.27 17.20 2.99 4 Tatum 112 7.83 0.00 25.14 3.69 4 Mt. Auburn 110 9.44 0.00 34.58 4.28 4 Rowe 73 9.57 0.00 16.81 5.00 4 Urban Park 102 9.76 0.00 17.89 2.56 5 Moseley 120 0.38 0.00 23.15 2.38 5 Douglass 102 4.55 0.00 23.56 3.76 5 Reagan 93 4.77 0.00 13.58 1.92 5 Wilmer-Hutchins 123 5.01 0.00 26.73 4.22 5 Bayles 100 5.10 0.00 17.48 6.78 5 Lagow 93 6.44 0.00 16.03 4.10 5 Sanger 77 6.54 0.00 17.97 4.02 5 Rhoads 77 8.68 0.48 17.53 4.34 5 Rice 78 8.88 3.26 29.09 3.84 5 Botello 76 9.40 0.00 21.97 5.32

    Continued

  • 37

    Table 11 Continued

    Division School

    Number of

    StudentsMean Hours

    Minimum Hours

    Maximum Hours

    Standard Deviation

    10-19.99 Hours 1 Webster 85 11.31 0.00 23.30 5.54 1 Field 77 11.59 0.00 20.17 3.07 1 U. Lee 109 12.05 0.00 21.17 4.28 1 Jones 120 12.47 0.00 19.32 4.65 1 Hall 85 12.84 0.00 21.42 4.80 1 Knight 105 13.46 0.00 24.83 5.90 1 Kahn 100 14.43 0.00 47.91 12.49 1 Peabody 73 14.82 9.04 21.37 2.98 1 Walnut Hill 43 18.08 0.00 36.73 4.86 1 Donald 70 18.88 0.00 27.13 5.07 1 Williams 47 19.86 0.00 30.30 4.74 2 Bush 96 10.04 0.00 23.62 4.07 2 McShan 75 10.34 0.00 20.10 4.34 2 J. Adams 81 12.30 0.00 26.29 5.79 2 Bryan 62 14.07 5.08 31.23 3.44 2 Miller 64 14.15 3.23 40.40 4.92 2 Junkins 110 14.58 0.00 37.16 6.26 2 Runyon 111 15.08 0.00 26.98 3.93 2 Hawthorne 72 15.12 0.00 25.59 5.49 2 Johnston 61 16.09 0.00 28.51 4.40 2 Hotchkiss 148 16.11 0.00 34.47 6.92 2 Budd 108 16.18 0.00 31.26 5.45 2 Lowe 96 18.49 0.00 36.71 6.52 2 Titche1 42 18.84 0.00 22.59 4.63 2 Starks 48 19.27 0.00 27.21 6.06 2 Pleasant Grove 86 19.86 0.00 57.56 11.43 3 Rogers 57 10.13 0.00 16.28 2.95 3 Salazar 128 10.31 0.00 19.79 3.43 3 Maple Lawn 73 11.42 0.00 21.71 3.74 3 Arcadia Park 98 11.72 0.00 25.49 4.63 3 Bethune 115 11.76 0.00 21.37 3.70 3 DeZavala 59 12.03 0.00 26.30 4.62 3 Cochran 85 12.03 0.00 26.15 5.62 3 Ray 64 12.73 0.00 21.19 5.30 3 Milam 40 13.11 0.00 21.37 5.54 3 Cowart 106 15.49 0.00 25.43 4.96

    Continued

  • 38

    Table 11 Continued

    Division School

    Number of

    StudentsMean Hours

    Minimum Hours

    Maximum Hours

    Standard Deviation

    3 Hernandez 60 16.65 0.88 31.92 5.62 3 Kennedy 104 16.71 2.49 49.33 5.59 3 Houston 38 16.94 4.40 19.93 3.62 4 Bushman 80 10.49 0.00 26.33 5.47 4 Jordan 75 10.86 0.00 18.43 4.39 4 Lisbon 44 11.50 0.00 24.79 6.44 4 Oliver 50 11.64 0.00 18.27 3.71 4 S. Jackson 101 11.67 0.00 24.89 4.16 4 Callejo 96 13.01 0.00 30.47 4.80 4 Conner 89 15.67 0.00 57.60 9.73 4 Dunbar 77 16.68 0.00 26.31 6.68 5 Casa View 120 10.27 0.00 24.38 6.01 5 Reilly 84 10.46 0.00 45.22 5.61 5 Kiest 109 11.09 0.00 17.39 4.40 5 Ervin 91 11.14 0.00 22.10 4.61 5 Silberstein 116 11.15 0.00 28.37 4.14 5 Central 71 11.45 0.00 23.56 4.43 5 Gonzalez 107 12.67 0.00 34.85 4.75 5 Blair 91 12.89 0.00 41.17 6.30 5 Hexter 98 14.29 1.69 50.96 5.21 5 Seagoville North 109 14.67 0.00 28.83 6.76 5 Seagoville 103 14.83 0.00 35.71 9.30 5 Hogg 30 14.98 0.00 21.63 5.63 5 Reinhardt 91 16.12 0.00 22.86 4.91 5 Smith 133 16.62 0.00 36.32 6.82 5 Burleson 108 16.87 0.00 41.63 7.71 5 Bowie 67 17.00 0.00 25.15 6.86 5 Halliday 88 18.37 0.00 32.62 6.22 5 W. Anderson 109 18.51 0.00 43.36 5.69 5 Gill 99 19.71 0.00 72.91 7.35 5 Cuellar 102 19.77 0.00 41.00 6.72

    20-29.99 Hours 1 Henderson 72 24.05 0.00 34.63 5.14 1 Stemmons 130 24.76 0.00 40.29 7.16 1 Polk 73 27.34 0.00 50.61 5.69 2 J. Stone 48 24.95 0.00 37.10 9.00 4 King 58 21.98 0.00 35.30 6.02

    Continued

  • 39

    Table 11 Continued

    Division School

    Number of

    StudentsMean Hours

    Minimum Hours

    Maximum Hours

    Standard Deviation

    4 Russell 120 24.91 0.00 54.23 7.15 5 Macon 77 20.22 0.00 30.11 4.73 5 Peeler 62 21.21 0.00 37.63 4.90 5 Dorsey 74 22.40 9.70 40.44 5.78 5 Kleberg 74 24.71 0.00 65.37 10.92

    1Many Titche students were missing in the RM file; there were 116 third-grade students in the PEIMS file. The mean of 18.84 is misleading and was not the true level of implementation at Titche.

  • 40

    Table 12

    RM Second-Grade Spring Hour Information with Schools Rank Ordered by Division and Mean Hours Online

    Division School Number of Students

    Mean Hours

    Minimum Hours

    Maximum Hours

    Standard Deviation

    0-9.99 Hours 2 Gooch 64 3.26 0.00 28.21 5.33 3 Pershing 67 2.46 0.00 15.17 2.86 3 Stevens 108 4.17 0.00 22.50 2.20 3 Kramer 89 4.97 0.00 12.72 2.28 4 Callejo 82 0.00 0.00 0.00 0.00 5 Reagan 79 4.58 0.00 21.43 2.51 5 Douglass 109 6.21 0.00 30.58 7.34 5 Lagow 106 7.17 0.00 15.96 3.87

    10-19.99 Hours 1 Donald 67 11.27 0.00 18.47 5.66 1 Weiss 84 14.51 0.00 27.46 6.04 1 McNair 134 14.73 0.00 36.06 7.01 1 Alexander 72 18.42 0.00 30.65 9.25 1 Carpenter 60 18.62 0.00 53.27 10.95 2 H. Meadow 138 10.72 0.00 17.80 4.32 2 Bryan 80 11.67 1.49 32.50 7.35 2 McShan 98 12.71 0.00 30.35 4.93 2 Mills 80 13.60 0.00 29.98 6.68 2 Caillet 109 14.08 1.55 47.59 4.58 2 Blanton 99 18.75 0.00 35.27 8.00 3 Lanier 85 10.24 0.00 13.83 3.65 3 Carr 81 13.95 0.00 32.42 6.11 3 Ray 75 15.30 0.00 32.06 8.86 3 Rogers 88 17.26 0.00 40.75 9.92 3 Frank 180 17.58 0.00 35.83 6.68 3 Salazar 126 17.84 0.00 48.27 9.80 3 Earhart 47 18.31 0.00 37.01 9.44 3 Cochran 95 18.81 0.00 29.79 5.15 3 Houston 47 19.98 1.04 30.06 5.04 4 Truett 157 10.29 0.00 30.21 7.81 4 Conner 91 10.41 0.00 32.31 5.70 4 Young 114 13.37 0.00 35.84 8.50 4 S. Jackson 114 14.85 0.00 41.62 5.16 4 Guzick 111 16.03 0.14 41.05 9.06

    Continued

  • 41

    Table 12 Continued

    Division School Number of Students

    Mean Hours

    Minimum Hours

    Maximum Hours

    Standard Deviation

    4 Lisbon 54 18.60 2.85 39.42 6.31 4 Marsalis 80 19.67 0.00 35.45 8.05 4 Pease 93 19.90 0.00 30.51 8.22 5 Ervin 102 10.70 0.00 24.41 4.59 5 Wilmer-Hutchins 117 13.85 0.00 34.70 7.89 5 Hexter 100 14.15 0.00 32.49 4.97 5 Reilly 84 15.03 5.21 34.42 3.71 5 Central 83 15.65 1.10 58.89 14.08 5 Smith 169 17.90 0.00 43.03 11.60 5 Casa View 114 18.43 0.89 34.04 4.38 5 Rhoads 86 18.59 6.86 42.45 4.97 5 Bayles 109 18.91 0.00 39.64 9.00 5 Silberstein 116 18.94 0.00 49.96 4.30 5 Hogg 45 19.03 7.26 32.19 5.81 5 Rice 87 19.39 0.00 32.41 6.63 5 Blair 96 19.74 0.00 47.18 5.98 5 Kiest 114 20.00 0.00 44.25 6.55

    20-29.99 Hours 1 Stemmons 124 21.55 0.00 45.15 9.57 1 Turner 57 23.46 7.86 35.01 4.69 1 Hall 88 23.54 14.41 36.30 4.96 1 Tolbert 65 24.45 10.10 41.53 7.70 1 Burnet 186 25.81 0.00 42.26 5.12 1 Knight 106 25.96 0.00 38.52 9.20 1 Hooe 75 26.59 0.00 34.81 8.84 1 Twain 62 26.84 0.00 122.86 22.80 1 Moreno 80 28.00 0.00 49.93 10.54 1 Webster 124 28.95 13.83 59.82 6.62 1 Winnetka 113 29.87 18.77 47.35 5.48 2 Bush 117 22.09 10.72 47.87 6.05 2 Lowe 104 22.76 0.00 48.63 8.81 2 Marcus 156 22.84 5.05 48.91 5.85 2 Ireland 106 23.79 1.88 40.52 7.87 2 San Jacinto 98 23.92 0.00 46.14 7.05 2 N. Adams 75 26.13 0.00 40.19 6.28 2 Budd 78 26.29 2.82 57.19 9.04 2 Hotchkiss 153 26.51 0.00 47.94 8.70 2 Starks 49 28.62 8.07 38.19 6.96

    Continued

  • 42

    Table 12 Continued

    Division School Number of Students

    Mean Hours

    Minimum Hours

    Maximum Hours

    Standard Deviation

    2 Withers 75 29.79 15.77 71.35 10.98 2 DeGolyer 55 29.84 7.31 64.99 7.90 3 Arcadia Park 127 20.81 0.00 44.24 8.57 3 Hernandez 61 21.48 1.74 47.41 11.86 3 DeZavala 58 23.64 0.00 40.33 7.34 3 Bethune 123 23.68 5.85 42.65 7.73 3 Kennedy 116 24.22 0.54 46.74 6.42 3 Zaragoza 87 25.31 0.00 42.47 6.78 3 Soto 117 25.42 4.19 45.04 6.68 3 Carver 75 25.79 0.00 44.81 9.71 3 Milam 49 27.22 14.80 34.72 3.81 3 Preston Hollow 63 28.10 0.00 62.58 18.46 3 Maple Lawn 96 28.93 13.57 36.96 3.79 4 H. Stone 49 20.30 6.34 56.01 8.58 4 Lipscomb 88 21.00 0.00 40.39 8.11 4 Thornton 54 21.01 5.63 30.89 5.17 4 Tatum 111 22.39 0.00 63.19 6.80 4 Rowe 65 23.04 14.92 33.85 4.22 4 Bushman 85 24.46 4.52 38.15 6.20 4 Lakewood 131 26.89 0.00 66.49 11.86 4 King 76 27.01 0.31 53.38 9.15 4 Urban Park 115 28.30 0.00 46.54 9.91 4 Dunbar 101 29.31 4.20 54.27 12.38 5 Moseley 106 21.45 6.47 35.25 4.81 5 Sanger 78 22.90 3.54 47.48 6.90 5 Dorsey 63 25.06 9.72 30.66 3.31 5 Botello 88 25.43 0.00 63.54 7.02 5 Macon 79 25.76 0.00 42.88 6.65 5 Gill 153 26.95 0.00 48.67 7.07 5 Seagoville 85 28.32 17.20 65.64 6.15 5 Burleson 105 28.70 0.00 46.92 7.46 5 W. Anderson 107 29.10 0.41 63.96 7.39 5 Reinhardt 109 29.40 0.00 44.10 9.51

    30+ Hours 1 Brashear 93 31.05 2.17 51.99 6.33 1 Kahn 105 31.43 5.01 73.38 13.46 1 Terry 61 32.47