the role of knowledge channel television shows on students ... · english test using the 2009...

The Role of Knowledge Channel Television Shows

on Students’ Learning

Lasallian Institute for Development and Educational Research (LIDER) Br. Andrew Gonzalez FSC College of Education (BAGCED)

De La Salle University (DLSU) 2401 Taft Avenue, Manila, Philippines Tel. +632-524-4611 local 435

Photo Credit: http://kchonline.ph

1 The Role of Knowledge Channel Television Shows on Students’ Learning

The Role of Knowledge Channel Shows on

Students’ Learning

Final Report

De La Salle University Br. Andrew Gonzalez FSC College of Education

Lasallian Institute for Development and Educational Research

Room A1608 Br. Andrew Gonzalez Hall, DLSU Manila

2401 Taft Avenue, Manila, Philippines

Telefax No. +632 536 0232

Telephone: +632 524 4611 local 435

Website: http://www.dlsu.edu.ph

The Research Team

Project Leader

Maricar S. Prudente, PhD

Research Associates 3

Studies 1, 2 and 4 Study 3

Leah Gustilo, PhD Thelma Mingoa, PhD

Minie Rose Lapinid, PhD Maria Cequena, PhD

Jessie Barrot, PhD Dena Culaba

Mari Karen Gabinete Jasper Vincent Alontaga

Research Associate 1

Jovito C. Anito, Jr.

Data Quality Assurance Specialist

Abdul Jhariel Osman

This study was funded by the Knowledge Channel Foundation, Inc.


Contents

Contents ............................................................................................................ 2

List of Figures ................................................................................................... 3

List of Tables .................................................................................................... 4

Executive Summary .......................................................................................... 8

Introduction .................................................................................................... 15

Study 1 Methods ............................................................................................. 31

Study 1 Results ............................................................................................... 34

Study 1 Conclusion ......................................................................................... 79

Study 2 Methods ............................................................................................. 80

Study 2 Results ............................................................................................... 82

Study 2 Conclusion ......................................................................................... 88

Study 3 Methods ............................................................................................. 90

Study 3 Results ............................................................................................... 98

Study 3 Conclusion ....................................................................................... 103

Study 4 Methods ........................................................................................... 105

Study 4 Results ............................................................................................. 109

Study 4 Conclusion ....................................................................................... 118

References .................................................................................................... 119


List of Figures

Figure 1. Percentage of students’ perceptions on competencies in

English developed in watching KC shows. 36

Figure 2. Percentage of students’ perceptions on competencies in Math developed in

Watching Knowledge Channel Shows. 37

Figure 3. Percentage of students’ perceptions on competencies in Science developed

in Watching Knowledge Channel Shows. 38

Figure 4. Percentage of students’ perceptions on competencies in Araling Panlipunan developed

in Watching KC Shows. .......................................................................................................... 38

Figure 5. Students mean scores in the achievement tests for the 5 subject areas for the 2009,

2010 and 2011 sample groups. ................................................................................................ 39

Figure 6. Gender differences in the students’ mean scores in 2009 , 2010 and 2011 sample

groups in the five subject areas and in the overall score. .......................................................... 43

Figure 7. Students’ Overall Mean Scores between Recipient and Comparison Schools across

regions in 2009. ...................................................................................................................... 44

Figure 8. Line graph illustrating the interaction between the type of respondents and Region in

English test using the 2009 sample group. ............................................................................... 48


English (E), Science (S), Math (M), Filipino (F), and Araling Panlipunan (AP) tests in the 2009

sample group. ......................................................................................................................... 49

Figure 10. Students’ Overall Mean Scores between Recipient and Comparison Schools across

regions in the 2010 sample group. ........................................................................................... 51

Figure 11 Line graph illustrating the interaction between the type of respondents and region in

Overall Mean scores in 2010. .................................................................................................. 54

Figure 12 Students’ Overall Mean Score between Recipient and Comparison Schools across

regions in the 2011 sample group. ........................................................................................... 57


English test in the 2011 sample group. .................................................................................... 60

Figure 14 Profile of the student participants in study 2 in terms of gender, location, and grade

level. ....................................................................................................................................... 83

Figure 15. Estimated marginal means of post-test scores by learning group and location. ......... 86

Figure 16. Hypothesized path diagram of the effect of students’ attitude towards Knowledge

Channel shows on their academic achievement........................................................................ 98

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Figure 17. Students’ achievement scores in the 5 subject areas. ............................................. 101

Figure 18. Path diagram when Teacher’s performance is introduced as a mediating variable on

the relationship between students’ attitude towards KC shows and students’ achievement. .... 103

Figure 19. Profile of teacher participants included in Study 4 (N=30). ................................... 106

Figure 20. Screenshot of the MaxQDA10 project illustrating the document, browser and code

system. ................................................................................................................................. 109

Figure 21. Screenshot of the MaxQDA10 document browser showing the coded segments in the

active transcript. .................................................................................................................... 110

List of Tables

Table 1

Summary of the number of schools involved in Study 1 .......................................................... 31

Table 2

Summary of the number of student respondents included in Study 1 ........................................ 32

Table 3

Reliability of Test Instruments used in study 1 ........................................................................ 33

Table 4

Mean ratings on how frequent students watch KC shows in three different platforms............... 35

Table 5

Students’ mean responses on the benefits of Knowledge Channel Shows ................................. 36

Table 6

Mean percent scores of respondents from recipient schools (RS) and comparison schools (CS)

across the 2009, 2010 and 2011 sample groups. ....................................................................... 40

Table 7

Mean percent scores between male and female students across the 2009, 2010 and 2011 sample

groups . ................................................................................................................................... 42

Table 8

Overall Mean Achievement Score per region between RS and CS in the 2009 sample group. .. 44

Table 9

Summary of Regions where RS performed better, and have more convergent scores than CS in

the 2009 sample based on their descriptive statistics. ............................................................... 45





Table 10

Two-way ANOVA test of between Subjects Effects using the 2009 sample group. ................. 46

Table 11

Summary of regions where recipient schools performed significantly better than the students in

the comparison schools in the 2009 sample group. .................................................................. 50

Table 12

Overall Mean Achievement Score per region between recipient and comparison schools in the

2010 sample group. ................................................................................................................. 51

Table 13

Summary of Regions where RS performed better, and have more convergent scores than CS in

the 2010 sample group based on their descriptive statistics. ..................................................... 52

Table 14

Two-way ANOVA Test of between Subjects Effects using the 2010 sample group. ................. 53

Table 15

Summary of Regions where students in recipient schools performed significantly better than

those in the comparison schools in the 2010 sample group....................................................... 55

Table 16

Overall Mean Achievement Score per region between recipient and comparison schools in the

2011 sample group. ................................................................................................................. 56

Table 17

Summary of Regions where RS performed better, and have more convergent scores than CS

based solely on their descriptive statistics in the 2011 sample group. ....................................... 57

Table 18

Two-way ANOVA Test of between Subjects Effects in the 2011 sample group. ...................... 58

Table 19

Summary of Regions where students in recipient schools performed significantly better than

those in the comparison schools in the 2011 sample group....................................................... 61

Table 20

Urban-Rural Classification of Provinces and Cities in the 2009 sample group. ......................... 62

Table 21

Differences in mean percent scores between urban and rural respondents in the 2009 sample

group. ..................................................................................................................................... 63

Table 22



Table 23

Differences in percent mean scores between urban and rural respondents in the 2010 sample

group. ..................................................................................................................................... 65

Table 24


Table 25

Differences in percent mean scores between urban and rural respondents in the 2011 sample

group. ..................................................................................................................................... 67

Table 26

Distribution of participants per type and per grade level in the 2009 sample group. ................. 68

Table 27

Mean achievement percent score per grade level between RS and CS in the 2009 sample group.

............................................................................................................................................... 69

Table 28

Two-way ANOVA test of between subjects effects in the 2009 sample group.......................... 70

Table 29

Summary of the test performance of student participants in the Recipient Schools in the

different Grade levels for the 2009 sample group..................................................................... 71

Table 30

Distribution of Participants per Type and per Grade Level in 2010 sample group..................... 72

Table 31

Overall mean achievement percent score in the 2010 sample group. ........................................ 72

Table 32

Two-way ANOVA Test of between Subjects Effects in the 2009 sample group. ...................... 73

Table 33

Summary of performance in achievement tests across grade levels for recipient schools in the

2010 sample group. ................................................................................................................. 75

Table 34

Distribution of Participants per type and per grade Level in the 2011 sample group. ................ 76

Table 35

Overall Mean Achievement Score per grade level between RS and CS in the 2011 sample

group. ..................................................................................................................................... 76

Table 36

Two-way ANOVA Test of between Subjects Effects for the 2011 sample group. .................... 77


Table 37

Summary of performance in the achievements tests across 6 grade levels. ............................... 78

Table 38

Mean scores across learning groups and locations in study 2. .................................................. 84

Table 39

The 3 x 2 contingency table of mean gain scores in study 2. .................................................... 85

Table 40

ANOVA test of Between-Subjects Effects for Learning Group and Location. .......................... 87

Table 41

Learning Group Pairwise Comparison. .................................................................................... 87

Table 42

................................................................................. Number of teacher participants by school. 90

Table 43

Profile of teacher participants by grade level and subject taught. ............................................ 91

Table 44

Mean scores of the teachers’ ability to Integrate KC Shows. .................................................... 91

Table 45

Student respondents’ profile by school, grade level, and age. ................................................... 92

Table 46

Reliability of achievement test instruments used. .................................................................... 93

Table 47

Indicator loadings, average variance extracted, and reliability coefficients of the variables in the

study. ...................................................................................................................................... 95

Table 48

Average Variance Extracted and correlation coefficients among constructs. ............................ 96

Table 49

Teachers' Mean Ratings in ability to integrate KC shows. ........................................................ 99

Table 50

Mean ratings of students’ attitude towards KC shows. ........................................................... 100

Table 51

Estimates for the relationship between Students’ Attitude towards KC shows and Students’

Achievement when Teacher Performance is introduced as mediating variable. ...................... 102

Table 52

Summary of codes and the coder’s memos. ........................................................................... 111


Executive Summary

Study 1. Effect of Knowledge Channel Shows on Students’ Achievement

Study 1 utilized an exploratory descriptive survey research design to explain

whether the exposure of students on Knowledge Channel shows can make a difference

in their academic achievement compared to the achievement of students who have no

exposure to Knowledge Channel shows.

Student respondents from 267 public schools, which received the Knowledge

Channel package under the 2009, 2010, and 2011 grants, referred to as recipient schools

(RS); and student respondents from 248 public schools, which did not receive the

Knowledge Channels package, referred to as comparison schools (CS) were the

participants for Study 1. This translates to a total of 41,550 student respondents, from

both RS and CS, who were surveyed Exploratory data analysis, however, necessitated

that only those RS respondents who reported that they watch KC shows shall be

included in the analysis. Thus, further data analysis included 32,768 students, of whom

13,095 students were from RS and 19,673 students were from CS.

An assessment package was developed covering the subject areas of Science,

Mathematics, English, Filipino, and Araling Panlipunan. The test items were content

validated by subject area experts from De La Salle University - Br. Andrew Gonzalez

FSC College of Education (DLSU-BAGCED) and revised based on the experts’

evaluation. The reliability analysis of these tests were conducted and were found to be

reliable.


A team of field researchers trained by Lasallian Institute for Development and

Educational Research (LIDER) administered the tests in their respective regions in

coordination with the Schools Division Superintendents (SDS) of the DepEd Divisions

where the target schools belong. On the day of test administration, a total of 20

respondents per grade level in every school were randomly selected to participate in the

study.

Significant Findings in Study 1:

1. Students from RS have significantly higher scores than those from CS. Further

investigation on the effect of exposure to KC shows suggests that the mean scores of

RS students were 33% better than the students in the CS for the 2009 sample group,

31% better for the 2010 sample group and 45% better fo the 2011 sample group. These

results corroborated the findings of the 2007 impact evaluation of Knowledge Channel

shows on students’ achievement scores (Mapa, 2007). Interestingly, highest effects

were noted in English (in 2009 and in 2010) and in Math (in 2011).

2. Across the different regions in the country, students from the RS generally performed

better than the students from the CS.

3. Female students in both 2009 and 2010 sample groups generally outperformed the

male students. This finding confirms previous findings that females score significantly

higher than males (Quimbo, 2003; Wentzel, 1988; Amelink, 2009). In the 2011 sample

group, no significant gender difference in the students’ performance was observed.


4. Overall, students from RS performed better across all subjects in all grade levels

compared with students from CS. Interestingly, grade 6 students were found to

perform significantly higher than students from other grade levels.

Study 2. Effect of Knowledge Channel Shows on the Achievement Scores of

Students from Various Learning Groups

Study 2 utilized an experimental research design to investigate whether students

from various learning groups (i.e. Passive, Active, Lecture) will have significantly

different gains in terms of achievement scores. Students in the passive learning group

were simply made to watch Knowledge Channel (KC) videos. Students in the active

learning group watched KC videos with the teacher facilitating the processing of the

contents of the video. Students in the lecture group were taught using the lecture type of

instruction.

The experiment was conducted in four recepient schools located in Luzon and

Mindanao. Each location involved an elementary school and a high school. For each

school, five subjects areas (English, Math, Science, Filipino and Araling Panlipunan)

were included. A total of 926 students partipated in this study and these students were

randomly assigned to each of the three learning groups. A 15-item test for each subject

area was developed. For each subject area, the researchers selected three topics with

corresponding Knowledge Channel Shows video. A lesson plan was developed for the

teachers who were assigned to the Active Group. The contents in the videos were

incorporated into the lesson plan. Specific instructions were provided for teachers

regarding the processing activities that they should do before, during, and after viewing


the shows. A similar lesson plan was given to the teachers who were assigned to the

Lecture Group.

To compare the mean scores of students across the three learning groups, independent

samples t-test and factorial ANOVA were employed.


1. The mean gain scores for each of the learning groups yielded significantly

positive results, with posttest scores > pretest scores. Students in the Active

group had the highest gain score, with the use of KC shows with teachers’

processing accounting for the 74.15% increase in the students’ posttest scores.

2. Post-Hoc Analysis of pairwise comparisons show that there is significant

difference between the gain scores of the students in the Active group and the

gain scores of the students in either the Passive or the Lecture group. Results

further revealed that the gain scores of the students in the Passive group is

comparable with the gain scores of the students in the Lecture group.

3. There is enough evidence to support that learning group accounts for the

differences in students’ achievement.

Study 3. Teachers’ Ability to Integrate KC shows, Students Attitude and

Achievement

Study 3 aimed to describe the relationship between teachers’ ability to integrate KC

shows and students’ achivement. It also explored whether the students’ attitude towards

KC shows can explain such relationship.


The participants in study 3 were 143 high school teachers and 950 high school

students from 8 public high schools in a province in Luzon. The province was chosen as

it had the most number of public high schools that are recipients of Knowledge Channel

package.

To gather pertinent data for study 3, an assessment package covering five subject

areas such as Science, Math, English, Filipino, and Araling Panlipunan were

administered to student respondents along with the checklist on Attitude Towards

Knowledge Channel Shows (ATKCS). Moreover, 8 supervisors from the Department of

Education conducted classroom observations on the 143 teachers. An observation

checklist describing the teachers’ proficiency skills was utilized. Path analysis was used

to estimate the parameters of the mediation model.


Analysis of quantitative data using path analysis, specifically the maximum

likelihood (ML) estimation method in AMOS, enabled us to depict the hypothesized

causal paths of variables in this study and to state the following conclusions:

1) The relatively high mean ratings of students’ attitude scores towards Knowledge

Channel shows indicate a positive attitude.

2) Students’ attitude towards KC shows has a significant direct positive effect on

students’ achievement.

3) Teachers’ ability to integrate KC shows has a direct positive effect on students’

achievement.

4) The mediation model reveals that teachers’ ability to integrate KC shows in

instruction partially mediate students’ attitude on achievement, since attitude


remained to have a positive significant effect on students’ achievement even

after introducing the mediation variable.

Study 4 Teachers’ Lived Experiences in Integrating Knowledge Channel Videos

in Instruction

Study 4 described the lived experiences of teachers in integrating Knowledge

Channel videos in instruction. Representative schools from Luzon, Visayas and

Mindanao participated in this study. Written questionnaire was used to determine how

frequent KC videos are viewed, how the videos are used in the lesson, and how the

videos enhance the teaching and learning experience. Focus group discussions (FGDs)

were employed to determine the different teaching strategies and methods that the

teachers employ when integrating KC videos into their lesson and how they use KC

videos in enhancing learning among the students.

For qualitative content analysis of the data from the FGD, the framework method

was used. This methodological approach examines the content of the FGD in order to

derive meaning and particular implications for describing the lived experiences of the

teachers. The approach involved identifying commonalities and differences in the

qualitative data, before focusing on relationships between different parts of the data,

thereby seeking to draw descriptive and explanatory conclusions clustered around

themes. The 3E continuum (Enhance, Extend, Empower) of technology-enhanced

learning was used as the framework for analysis. The analysis proceeded with these 3

stages set as primary categories in the coding process. The coding process was

conducted utilizing MaxQDA10™.


Significant Findings in Study 4

Reflecting carefully on the teachers’ lived experiences, the following findings are

deemed significant:

1. The use of the 3E framework of technology integration in interpreting and

analyzing the lived experiences of teachers was found to be appropriate.

2. The teachers’ techniques in integrating Knowledge Channel videos fall

primarily within the enhance level of the 3E continuum, suggestive that

teacher training is needed to further develop their pedagogy in integrating

technology in order to achieve the stage where students are self-motivated to

learn.

3. Following the social constructivist lens of the framework, it is maintained

that teachers must be cognizant of their primary role as facilitators of

learning. Through the lived experiences of the teacher participants, the social

constructivist roles of teachers at the enhance level is recognized.

4. There are some activities that the teachers employ in integrating KC videos

in teaching that are reflective of the extend level of technology integration.

5. The lived experiences of the teacher participants reveal that the empower

level manifests in the process of giving assignment questions which calls for

students to watch other KC shows and related learning materials.


Introduction

Since the 20th century, television (TV) has become the primary source of news and

information. As such, it has the potentials to greatly influence the way people live and

process information (Albertson & Lawrence, 2009). Debates persist on how, when, and

to what extent should children be exposed to TV considering the proliferation of sex,

violence, and adult language on advertisements and actual programs (Kunkel, 1998).

However, it cannot be denied that TV has a vast potential as an educational technology

(Greenhill, 1967; Hendrick, 1986; Huston & Wright, 1998; Kaymas, 1999; Moeller,

1996; Schramm, 1962; Seels, Fullerton, Berry, & Horn, 1996)—it educates children and

expands knowledge of the world (Huston & Wright, 1998). As a matter of fact, both in

Japan and in Europe, TV was originally conceived as an educational tool (Fuenzalida,

2011).

Benefits of educational TV shows

Many reasons have been advanced as to why TV gained much attention as an

educational tool. These include accessibility (Moeller, 1996; Nielson Reports, 1986),

positive effects on enhancing the literacy development of both children and adult

(Soudack, 1990), and entertainment value (Bates, 1983). Moreover, watching TV was

considered as the second most commonly used self-directed learning strategy among

European students and fourth among Chinese students (Gieve & Clark, 2005). On top of

these, learners are more motivated to learn via visual media than printed media

(Chapple & Curtis, 2000).

One of the foremost goals of any literacy programs and the reason why

stakeholders integrate educational television in learning is to see gains in viewers’


academic achievement. Seels et al. (2008) has documented positive and negative results

of television utilization projects in the 1950s, 60s, and 70s in the United States. The

Hagerstown, Maryland project, and early demonstration of instructional television saw

improvements in standardized test scores in the initial experiment. The most important

gain was on students’ improvement in learning by television. A survey of different

stakeholders revealed that students preferred television instruction. In the Samoan

project conducted between 1964 and 1970 in which researchers administered pre and

post tests, it was deduced that the greatest gain was found in the area of Mathematics

and slight advantage in reading.

One of the most encouraging findings from recent studies on educational television

is the gains found among young children. Baydar et al. (2008) examined the effects of

an early childhood television program of 5-year old children in Turkey who belong to

the low socioeconomic level and who had limited access to preschool education. The

study found significant gains among children with high levels of exposure in arithmetic,

syllabication, and vocabulary. Moreover, the study found that instructional television

was more beneficial for children with lower school readiness skills prior to the

implementation of the program than those who had higher school readiness skills.

Register (2004, as cited in Shoemaker, 2011) tested the reading skills of three groups of

Kindergarten in Florida: (1) music-only exposure, (2) video-only exposure, and (3)

music and video combined exposure. Her findings indicated that the test scores of

students in the music and video exposure were six times higher than that of the music

only group when the former were tested after viewing a children’s television program,

Between the Lions. In a similar study, Linebarger, Kosanic, and Greenwood (2004)

investigated the impact of Between the Lions on Kansas’ first grade and kindergarten


students’ literacy skills. Results of the study showed that the viewers had higher word

recognition, reading test scores and phonemic awareness compared with non-viewers.

Great improvements on literacy skills (speech to print, word building, concepts of print)

were noted among moderately at-risk to non at-risk kindergarten children who viewed

the program than those at-great risk. Pelletier (2011) compared four groups who

participated in a 12-week family literacy program: one group participated in the book-

making project intervention, the other in television and book making, and the rest in the

regular family literacy programming. The study found that children who participated in

the television viewing intervention had greater gains in early reading, and children who

participated both in the television viewing and alphabet-book making had the greatest

gains.

Marshall (2002) presents other evidences that show that educational videos actually

help learning: (1) Bryant, Mullikin, McCollym, Ralastin, Raney, Miron et al (1998) said

watching Blues Clues has strong effect on the flexible thinking, problem solving, and

prosocial behaviors of pre-schoolers; (2) Wilson, et al. (1999) said the Choices and

Consequences program reduced verbal aggression (including teasing, swearing,

arguing) among middle school students; (3) Wright, Huston, and Kotler (2001) said

viewing Sesame Street was positively associated with subsequent performance in

reading, math vocabulary and in school readiness skills of pre-schoolers; and the

recontact study by Wright, Linebarger, and Schmitt (2001) on15 to 20-year olds who

frequently viewed Sesame Street at age 5 “had significantly better grades in English,

Science, and Math, read more books for pleasure, and had higher motivation to achieve.

From a social cognitive point of view, students learn from watching others. In

Albert Bandura Bobo doll experiment, children watched an adult act aggressively


towards a Bobo doll and they later imitated the aggressive behaviour during their free

play (Bandura, 1965, as cited in Bergin & Bergin, 2012). This brings to point the power

of television, in general, to influence behaviour of the viewers, and for educational

television, in particular, to facilitate learning of concepts, skills and attitudes of their

viewers. Learners construct knowledge from what they view on television in the

context of their prior knowledge, their past experiences, their beliefs, biases, and

expectations. Thus affects the students’ ability to learn from it (Center for Children and

Technology, 2004).

National policies on educational TV shows

Several countries have already created a law that requires TV networks to broadcast

educational programs. One of those countries is the Philippines, which enacted the

Children’s Television Act of 1997. The law requires broadcast networks to allot 15

percent of daily airtime to child-friendly TV programs. Eventually, its implementing

rules and regulations have been released to put more teeth into the law (Cruz, 2012).

One of the by-products of Children’s Television Act of 1997 is the Knowledge Channel

Television. It is the first and only educational channel in the Philippines which aims to

provide a wide array of curriculum-based and curriculum-relevant yet dynamic and

engaging programs for its more than 3 million viewers from both formal schools and

alternative learning system. Through its philanthropic support, Knowledge Channel

Foundation, Inc. (KCFI) was able to provide services to almost 2,000 public schools in

the Philippines with its educational programs in Science, Math, English, Filipino, and

Araling Panlipunan beamed nationwide daily.


Previous studies on Knowledge Channel shows

In an impact evaluation of the programs of Knowledge Channel on students’ NAT

scores from 101 elementary schools in 10 provinces in the country which was

commissioned by Knowledge Channel in 2006, Mapa (2007) claimed that there was a

positive and significant relationship between the presence of Knowledge Channel and

the improvement of NAT scores and its sub-components from 2004 to 2006 in schools

which regularly utilized Knowledge Channel facilities. The increase of at least 2

percentage points per year (average) in the NAT scores and its sub-components

(Science, Mathematics and English), all things being the same, can be attributed to

knowledge Channel shows. In addition, Mapa reported that the positive impact of

Knowledge Channel programs was felt most likely in the areas of Science and

Mathematics.

In another commissioned study of the programs of Knowledge Channel on

students’ performance reported by Mapa (2009), a test-questionnaire patterned after the

National Achievement Test (NAT) consisting of 60 items divided into three

components, namely: Mathematics (20 items), Science (20 items) and English (20

items) was constructed and administered to two groups of Grade 6 pupils. The first

group consisted of schools that were visited in the same school year 2007-2008 wherein

the same set of 458 pupils took the pre-test and post-test. The second group consisted of

schools in which the pre-test was administered in 2008, while the post-test was

conducted in 2009 to different sets of grade 6 pupils. Results indicated that the test

scores of Grade 6 pupils in Group 1 increased significantly by about 6 percentage

points, from 20.20 in the pre-test to 23.81 in the post test after four (4) months of

intervention. In addition, the test scores of Grade 6 pupils in Group 2 increased


significantly after one year of intervention by 11 percentage points, from 20.29 during

the pre-test to 26.96 in the post-test.

Given the findings of aforementioned studies as revealed in the literature, study 1

aimed at determining whether students from schools with Knowledge Channel shows

(recipient schools--RS) performed better in their academic achievement tests as

compared to students from schools without Knowledge Channel shows (comparison

schools--CS).

Mode of integrating educational TV shows in instruction

Moreover, the above review of research suggests that educational television is a

great aid in students’ learning and academic achievement. Moeller (1996), however,

claims that the use of television, in itself does not guarantee positive gains. Using

television alone, without careful consideration of the viewing process and level of

interactivity in the instructional environment, will not readily result in learning since

educational television is a complex medium whose messages are not easy to decode.

Educational television exemplifies the components of the communication process which

comprises of the following: the sender (teacher, script writer, producer, and director);

target audience in a particular context, in the case of ETV, students, whose meaning-

making process may be influenced by aptitudes, interests, needs, and desires; purpose or

coded messages transmitted via satellite; channel or medium by which the coded

messages are transmitted or broadcast; and feedback or students’ reactions on the TV

programs that are shaped by environmental context (Aghi et al., 1981). With this

complex process of meaning making on educational television, it is a combination of


technology use and teacher’s processing of content which allow students to integrate

concepts meaningfully.

For instance, in an experimental study conducted at Sta. Ana Unified School

District (1971, cited in Seels, Fullerton, Berry, & Horn, 2008) that investigated the

effectiveness of three methods of instruction: conventional classroom instruction,

televised instruction only, and a combination of classroom and televised instruction for

teaching science content and vocabulary were investigated. Results of the study show

that the combination of televised and classroom instruction returned the greatest

achievement. On the other hand, there was no significant difference that was noted in

the achievement of the televised and conventional classroom instruction. Likewise,

Hardwood and McMahon (1997) found that video-enhanced curriculum for senior high

school students had positive effect on students’ knowledge achievement and attitude

toward science subject. Experimental groups that watched video series juxtaposed with

teachers’ interactive processing of chemistry concepts shown in videos outperformed

the control groups that only received classroom instruction without videos.

Even in e-learning environments, in which direct instruction or processing of

concepts by teachers are not provided, the effect of level of interactivity applied in

learning still holds true. In Zhang, Zhou, Briggs, and Nunamaker’s (2006) study, they

examined the effect of interactive video on learning achievement and learner

satisfaction in e-learning environments. They compared four groups of learners in four

learning environments—with interactive video, with non-interactive video, without

video, and traditional classroom. The 138 undergraduate students were randomly

assigned in each group. The subjects were given pre-lecture and post-lecture tests. Zang

et al.’s findings supported their hypothesis on the positive effects of interactive video.


Students in the e-learning setting with interactive video outperformed those in other

settings and showed higher levels of learner satisfaction.

Clearly, the available research suggests that educational television has a great

potential for enhancing learning. Its effect is contingent upon several variables such as

interactivity and type of instructional settings students are exposed to. However, we

know very little about instructional television’s effects on the academic performance of

students in different learning environments across academic subjects in elementary and

secondary education in the local setting.

Study 2 aims at occupying the aforementioned gap in research by investigating the

difference in the post-test gains on achievement tests of students in three learning

groups: (1) instruction using Knowledge Channel Shows (Passive group), instruction

using Knowledge Channel Shows with interactive processing of content by the teacher

(Active group), and traditional instruction (Lecture group).

Given the findings from previous research that television improves learning, we

hypothesized that Knowledge Channel shows in videos will improve learning outcomes.

Specifically, the following hypotheses inspired this study:

1. Students from the active learning group who watch Knowledge Shows with

interactive processing of content will achieve better post-test scores than do students

from passive learning group who view Knowledge Shows without interactive

processing.


2. Students who watch Knowledge Shows with interactive processing of content from

the active learning group will achieve better post-test scores than do students from

lecture group, who merely utilized the traditional instruction.

In addition, based on the results of our study 1, which suggest that student

achievement varies across regions, study 2 proposed the third hypothesis (study 2 was

conducted after data of study 1 had been analyzed):

3. Student achievement in various learning groups is moderated by the area variable

which would result in the differences of student achievement across experimental areas.

Educational TV shows vs. classroom teachers

Literature posits that educational TV shows make a difference with other methods of

teaching (see Ayers, 1972; Jones, 1962). Furthermore, it also shows that instruction

which covers the ability of the teacher to use television as a teaching tool makes a

difference on how students learn and achieve inside the classroom (Savenye, Davidson,

& Smith, 1991). Hence, about 50 years ago, the notion that instructional television

could replace the traditional classroom teacher was proven false (Hendry, 2001). Up

until now, the teacher still remains to be the major in-school influence to student

learning outcomes (Hattie, 2011) and educational television shows simply enhance the

learning that the teacher provides.

Educational videos can engage student interest with dramatization, animation, and

application portions, which is covered in detail during class discussions and class

activities that would ensure comprehension (Hendry, 2001). Findings in this study

show that educational television stimulates class discussion, reinforces lectures and

reading, provides a common base of knowledge among students, and helps teachers


teach more effectively. As a result, the teachers revealed that their use of technology in

the classroom enhanced student comprehension and discussion of content, better

accommodation of students with diverse learning styles, and an increase in student

motivation to learn.

Chen and Hodder (1997) underscored the elements of effective classroom

television. The study examined a ten-year track of formative and summative research

conducted by the Foundation for Advancements in Science and Education (FASE), the

creator of Futures and The Eddie Files, and other ITV programs (FASE, 1997). The

study found that shorter programming was of higher value and had greater impact, and

that video was “most useful when used to support, rather than replace, the teacher”.

Educational television plays the supporting role, catching the interest of students,

helping them focus on particular subjects, and emphasizing key points (Hendry, 2001).

Teachers’ ability to integrate technology (videos) in their lessons

The literature has established that educational television plays a significant role to

enhance classroom instruction. However, the ability of the teachers to integrate

technology using educational TV shows in teaching to student learning must also be

studied. Teachers’ effectiveness in enhancing student achievement with the use of

educational videos or television shows lies in their ability to integrate technology in

their lessons. The national teacher training institute (n.d.) presents some guidelines on

how teachers can use educational television videos as an enhancing and enriching

resource that matches curriculum area. These guidelines cover the following areas (1)

prior to the video-based lesson (teacher and student preparation), (2) a focus on media

interaction while students view the video (like watching specific information or asking


the students to complete a task during or after a video segment is shown), (3) viewing

activities, and (4) post-viewing activities (discussion, recognizing reactions, connecting

the program to class work, introducing extension activities).

A survey of teachers’ use of television in the classroom (Center for Children and

Technology, 2004) show some best practices. These (1) Planning ahead (“spark

interest or inspire, demonstrate something you can’t do any other way, enrich

curricular content, practice a skill, reinforce or review a topic”), and (2) Promoting

Active Viewing (in three steps). The recommended steps in promoting active viewing

include: a) Preparing – previewing the program for alignment to lesson goals;

determining the setting and length of the video; setting clear expectations for students

(what you want them to gain and what activities will be done); and practicing the

equipment; b) Participating – while keeping the lights on, the teacher will preface or

introduce the video lesson with a few key questions or learning objectives; use the pause

button once in while, to allow for some questions; or break students into small groups

for discussion and share their thoughts to the bigger class; and (c) Connecting – where

the teacher will choose follow up activities that connect to hands-on, real world

experience, and the teacher will explain the connections made, specially for younger

students.

Although educational television shows may be very interesting and excellent for

explaining content and illustrating applications, they may not be truly interactive and

may not address any misunderstandings of some students. This is where the classroom

teacher, who presumably knows his/her students, would be irreplaceable (Skolnik and

Smith, 1993). And since the teacher is a major factor in the successful integration of


educational television shows to school curriculum, it is imperative to introduce these

shows to schools with accompanying teacher training.

In another research, educational television series, the Peabody Award-winning

series The Eddie Files, which was produced for elementary students, (Foundation for

Advancements in Science Education, 1997) found that educational television shows,

when combined with other activities, can change the attitudes towards math and

improve student performance. Each episode was focused on a topic from the

elementary curriculum such as fractions, or statistics. Pre-test interviews showed that

90% of students found math “boring”. After watching episodes for two months with

teacher guidance, post-test interviews showed that 75% of those students did not find

math boring anymore. The same study showed that the number of students who wanted

a career in math increased by 14%. A later poll showed increased ability of students to

define concepts covered in the television series, to answer questions correctly, and to

list applications of the curriculum topics that were addressed.

Nowadays, visual media, in the form of television or video has become an essential

part of classroom instruction. This is likely to increase due to the presence of cable

digital media and streaming video. A study by the center for children and technology

(2004), focused on key questions concerning the relationship of television to learning.

Television is said to promote children’s learning when children they use more senses for

taking in information, which can be explained by the information processing theory. So

instead of just seeing something, or hearing something, children remember more by

seeing and hearing at the same time

Much research has shown the possitive effect of educational television or videos to

enhance learning in students. Previous researches recommend how educational


television could be integrated in the classroom. For teachers to effectively use this

media in the classroom, teacher training and exposure to the material is necessary.

A study by Eckenrod and Rockman (1988), (as cited in EDC Center for Children

and Technology, 2004), show that teachers attended training session and were given a

resource guide. Upon returning to their classes however, they tended to use the videos

and activities demonstrated during the training, and tended not to use the videos and

activities in the resource guide but were not demonstrated.

Another professional development training, Thirteen/WNET, New York’s National

Training Institute, in partnership with 15 other stations, trained teachers how to integrate

internet, software applications and television and video programs into hands-on

classroom learning activities (EDC Center for Children and Technology, 2004).

Evaluation showed that 81% of the teachers reported an increase in their students’

learning and 75% reported that their students retain more information. The ability of

teachers to integrate technology with the lesson content, keeping in mind the learnig

theories and teaching strategies (pedagogical knowledge) is the prime focus of teacher

training in technology integration.

To understand better how teachers integrate technology in the classroom,

understanding Technological Pedagogical Content Knowledge (TPACK), a theoretical

framework for describing and understanding teacher knowledge required for effective

technology integration (Mishra and Koehler, 2006, as cited in Mishra, Kochler and

Shin, 2009) is imperative.

In the 20th century, it has been known that teachers should be able to bring together

content of the lesson with pedagogy, transforming how to teach a certain content

depending on the learner’s context known as Pedagogical-content knowledge. In the 21st


century, technology has provided other forms of information, giving both teachers and

students new ways to acquire knowledge. This added ICT to content and pedagogy,

known as TPACK.

To be able to assess the quality of integration of technology in the classroom, a

rubric was designed and validated, which included teacher attitudes, IT fluency (regular

use), seamless integration (regular use, appropriate choice), use of technology by

students, and classroom management (Schmidt, Thompson, Mishra, Kochler and Shin

(2009). This was used for self-assessment of 124 pre-service teachers. Data analysis

included Cronbach’s alpha statistics on TPACK knowledge domain and factor analysis

for each domain. Results showed that with the modification and deletion of 18 survey

items, the survey instrument is reliable and valid in helping educators design

longitudinal studies in assessing pre-service teachers’ development of TPACK.

Another study by Kaya, Kaya and Emre (2013) adapted this “Survey of Pre-service

Teachers Knowledge of Teaching and Technology” to assess pre-service primary

teachers’ Technological Pedagogical content Knowledge (TPACK) to 407 Turkish pre-

service primary teachers (227 female, 180 male). With the use of exploratory and

confirmatory factor analyses, which includes Cronbach’s alpha and item-total

correlation coefficients to check psychometric properties, it was concluded that the

adapted scale should not be used in academic studies that focused on Turkish pre-

service primary teachers’ TPACK.

Harris, Grandgenett, and Hofer (2011) tested a TPACK-Based Technology

Integration Assessment Rubric. They said there are only few measures that are available

to measure quality of teacher integration, most of which favour the constructivist

approaches to teaching, not accurately assessing the quality of technology integration


across various teaching approaches. They developed a more “pedagogically inclusive

instrument reflecting TPACK concepts and has proven to be reliable and valid in two

successive rounds of testing.

The current study used a TPACK – Based technology Integration Assessment

Rubric with more detailed adaptations that took into consideration levels in Bloom’s

Taxonomy in the portion of pedagogy.

Attitude of students towards technology integration

Several studies on attitude of students towards technology integration were about

using various forms of media. Aliasgari, Riahinia, and Mojdehavar (2010) made a

study on the effects of computer-assisted instruction (CAI) on the attitude of math

students towards learning math. It studied second year girls of math or science in two

high schools of Hashtgerd, Iran, for control and experimental groups. Results show that

CAI increases the learning level of students and improve their attitudes toward

mathematics, although the study suggests this should be done in a larger sample and in

another setting.

Another study that analysed student attitudes and beliefs towards e-learning was

done in two universities in Libya, with engineering students (Rhema & Miliszewska,

2014). This also studied their satisfaction with technology and past e-learning

experiences, finding out the perspective of the user. This analysed the relationship

between student attitudes towards e-learning and their demographic characteristics, their

access to technology, their use of technology for learning, their skill in technology and

their satisfaction with technology.

Another study on student attitude toward the use of technology in learning is a case

study investigating student attitude towards computer-assisted language learning


(CALL), by taking note of student attitude towards computer-assested learning (CAL)

and foreign language learning (FLL), (Bezen, 2010). Factors affecting student attitude,

relationship of CAL, CALL and FLL was also explored. Findings show that most

students have positive attitude towards CAL, CALL and FLL. Factors that affect

student attitudes were age, grade, gender, years of studying English, and prior

experienced in CALL. It was also found that CAL, CALL and FLL are interrelated.

Study 3 explored students’ attitude towards educational television shows of

Knowledge Channel, instead of CAI, and its relationship to student achievement and

teachers’ ability to integrate educational shows into their lessons. The study sought to

establish the mediating effect of teacher’s ability to integrate KC shows in teaching on

the effect of students’ attitude towards KC shows on their academic achievement.

Specifically, it sought to answer the following research questions:

1. Is there a significant effect of attitude towards KC Shows on student

achievement?

2. Is there a significant effect of teachers’ ability to integrate KC Shows on student

achievement?

3. Is the effect of the students’ attitude towards KC on student achievement

mediated by teacher’s ability to integrate KC shows in teaching?


Study 1 The Effect of Knowledge Channel Shows on the Students’

Academic Achievement

Study 1 Methods

Research Design

Study 1 utilized an exploratory descriptive survey research design to explain

whether the exposure of students on Knowledge Channel shows can make a difference

in their academic achievement compared to the achievement of students who have no

exposure to Knowledge Channel shows.

Participants

Student respondents from 267 public schools, which received the Knowledge

Channel package for the 2009, 2010, and 2011 grants, referred to as recipient schools

(RS); and student respondents from 248 public schools, which did not receive the

Knowledge Channel package, referred to as comparison schools (CS) were the

participants for Study 1 (Table 1). This translates to a total of 41,550 student

respondents, from both RS and CS, who were surveyed.

Table 1

Summary of the number of schools involved in Study 1

Year Recipient Schools Comparison Schools

2009 111 100

2010 111 105

2011 45 43

Total 267 248


In order to satisfy the condition that RS respondents must have been truly exposed to

KC shows, only respondents who reported that they watch KC shows were included in

the analysis. Table 2 presents the summary of the analyzed samples.

Table 2

Summary of the number of student respondents included in Study 1

Surveyed Samples Analyzed Samples

RS CS Total RS CS Total

2009 8889 7516 16405 5318 7516 12834

2010 9305 8537 17842 4550 8460 13010

2011 3683 3620 7303 3227 3620 6847

Total 21877 19673 41550 13095 19673 32768

Instruments

An assessment package was developed for grades 3-6 and first year to second year

high school covering the subject areas in Science, Mathematics, Filipino, English, and

Araling Panlipunan. The items were content validated by subject area experts from De

La Salle University - Br. Andrew Gonzalez FSC Collede of Education (DLSU-

BAGCED) and revised based on the experts’ evaluation. The reliability of the test

instruments was determined using Cronbach’s alpha (Table 3). As can be seen in Table

3, reliability estimates ranged from .63 to .89 indicative that the test instruments used to

measure students’ achievement were reliable.


Table 3

Reliability of Test Instruments used in study 1

Level Subject No.

Items

Cronbach's

Alpha Level Subject

No.

Items

Cronbach's

Alpha

Gra

de

3

English 20 0.833

Gra

de

6

English 25 0.776

Science 20 0.693 Science 25 0.634

Math 20 0.850 Math 25 0.870

Filipino 20 0.815 Filipino 25 0.835

Araling

Panlipunan

20 0.774 Araling

Panlipunan

10 0.743

Gra

de

4

English 20 0.859

Gra

de

7

English 30 0.724


Math 20 0.823 Math 27 0.714


Araling

Panlipunan

20 0.731 A.

Panlipunan

30 0.772

Gra

de

5

English 25 0.758

Gra

de

8

English 30 0.720


Math 25 0.856 Math 25 0.664


Araling

Panlipunan

25 0.830 A.

Panlipunan

30 0.623

Procedure

A team of field researchers (FRs) were invited to attend an orientation conducted

by the Lasallian Institute for Development and Educational Research (LIDER) in

selected cluster areas in Regions 1, 2, 3, 4A, 4B, 5, 6, 10, 12, CAR, CARAGA, and

NCR (July 9-25, 2014). The purpose of the orientation was to train the FRs about the

procedure and scope of work that they will render for the duration of Study 1. LIDER

sent initial communications to the Schools Division Superintendents (SDS) of the

DepEd Divisions where the target schools belong. The letter indicated the list of

recipient schools and requested that the assigned FRs be made to select the comparison

schools based on proximity and teacher-student (TS) ratio criteria. In calculating the TS

ratio, the masterlist of public school teachers and the public school enrolment data


obtained from the Department of Education division offices, were used. The school with

the closest TS ratio as the recipient school was chosen as the comparison school. The

field researchers were tasked to follow up the approval of the letter of request and to

secure the Superintendent’s endorsement letter addressed to the respective principals.

Once endorsement was secured, the FRs coordinated with the Principals and set the

schedule of test administration. The FRs followed the random sampling procedure using

sample randomizer (www.randomizer.org) and a total of 20 student respondents per

grade level per school were randomly selected to participate in this study.

Data Analysis

To compare the mean scores between recipient and comparison schools,

independent samples t-test and factorial ANOVA were used. Cohen’s d was calculated

to determine the effect size of the differences between the means of student repondents

the recipient and comparison schools.

Results

Students from the recipient schools were asked to report how often they watch

KC shows in various platforms. Specifically, RS respondents were asked how often

they watch KC shows from the KC website, Television, and Youtube. Results revealed

that students watch KC shows more often using the Television platform. Table 4 shows

the mean rating for each media in the three sample groups. It can be deduced that KC

Website has already emerged as a popular platform to watch KC shows as the

television.

http://www.randomizer.org/


Table 4

Mean ratings on how frequent students watch KC shows in three different platforms.

Knowledge

Channel

Platfrom

2009 Sample Group 2010 Sample Group 2011 Sample Group

Mean SD Mean SD Mean SD

Website 2.98 1.17 3.0 1.27 3.07 1.134

Television 3.24 1.20 3.16 1.32 3.20 1.21

Youtube 2.26 1.25 2.34 1.31 2.32 1.29

Scale: 0=Never, 1=Rarely, 2=Sometimes, 3=Often, 4=Always.

The RS students were queried on whether Knowledge Channel shows have

helped them gain knowledge, skills, and attitudes in the four subject areas (English,

Mathematics, Science, and Araling Panlipunan). Using the scale of 1 to 4, wherein “4”

represents strong agreement to the statement that KC shows helped the respondent gain

knowledge, skills, and attributes and the scale “1” represents strong disagreement. Table

5 shows that for the 2009 sample groups, the mean responses ranged from 3.43 to 3.65,

which implies relatively strong agreement among RS respondents that KC shows helped

them gain knowledge, skills, and attitudes in all subject areas. The same pattern of

strong agreement can be observed for the 2010 and 2011 sample groups.


Table 5

Students’ mean responses on the benefits of Knowledge Channel Shows

Grade

Level Subject

2009

(Mean)

2010

(Mean)

2011

(Mean)

Grade 3

English 3.49 3.43 3.49

Science 3.48 3.41 3.39

Math 3.57 3.53 3.31

Araling Panlipunan 3.47 3.42 3.45

Filipino 3.47 3.39 3.39

Grade 4

English 3.50 3.40 3.50

Science 3.53 3.38 3.36

Math 3.56 3.48 3.39


Filipino 3.52 3.34 3.22

Grade 5

English 3.57 3.46 3.61

Science 3.57 3.52 3.58

Math 3.65 3.55 3.56


Filipino 3.52 3.43 3.54

Grade 6

English 3.51 3.46 3.54

Science 3.50 3.46 3.48

Math 3.51 3.50 3.51


Filipino 3.47 3.43 3.43

The study also delved into the specific competencies that students perceived they

have developed from watching Knowledge Channel Shows.

Figure 1. Percentage of students’ perceptions on competencies in English

developed in watching KC shows.


It can be seen in Figure 1 that 90% of the students perceived that watching KC

shows have helped improved their competencies in English language in terms of reading

comprehension and in learning new words.

Figure 2. Percentage of students’ perceptions on competencies in Math

developed in Watching Knowledge Channel Shows.

As for Mathematics, among the 2009, 2010 and 2011 sample groups, more than 90

% of the student perceived that KC shows help them improve in their problem solving,

computational skills and understanding of mathematical terms. While more than 80% of

the students expressed that they can see the relationship of math operations in their daily

life through KC shows (Fig. 2).

Regarding competencies in Science, Fig. 3 indicates that majority of the students

(96%) perceived that they benefit from KC shows in terms of understanding concepts

related to living things. More than 90% of the students found KC shows to be helpful in

understanding force, motion, space, energy, earth and matter.


Figure 3. Percentage of students’ perceptions on competencies in Science

developed in Watching Knowledge Channel Shows.

With regard to the competencies developed in watching KC shows related to

Araling Panlipunan, more than 90% of the RS students expressed that KC shows help

them in understanding their own selves as Filipino citizens, roles in the family, and

participation in the community. Across the 2009, 2010 and 2011 sample groups, similar

patterns of students’ perceptions can be observed (Fig. 4).

Figure 4. Percentage of students’ perceptions on competencies in Araling

Panlipunan developed in Watching KC Shows.


Comparative Analysis

Students’ mean scores in their achievement test in each subject area and overall test

are shown in Fig. 5 to show comparison between RS and CS across three sample

groups.

Figure 5. Students mean scores in the achievement tests for the 5 subject areas for the

2009, 2010 and 2011 sample groups.


As can be gleaned in Figure 5, students from RS performed better in the English

achievement test than students from CS. The same pattern can be seen in all the other

four subject areas and in their overall acheivement scores.

In order to determine whether there is a significant difference in the achievement in

the 5-subject tests between students from RS and CS, an independent samples t-test was

used. Results showed that the mean percent scores of students from RS were

significantly (p < 0.005) higher than the scores of students from CS across all the 5-

subject areas in 2009, 2010 and 2011 sample groups. Further, the reported scores in the

overall mean percent scores from the RS across the five subject areas were significantly

higher than the CS (Table 6).

Table 6

Mean percent scores of respondents from recipient schools (RS) and comparison

schools (CS) across the 2009, 2010 and 2011 sample groups.

Variable N M p

2009 2010 2011 2009 2010 2011 2009 2010 2011

English 0.00* 0.00* 0.00*

RS 5318 4550 3227 53.8 48.9 51.1 (0.35) (0.32) (0.38)

CS 7182 8460 3619 47.1 43.0 43.8

Science 0.00* 0.00* 0.00*

RS 5318 4550 3227 47.7 44.4 43.6 (0.25) (0.20) (0.22)

_ CS 7182 8460 3620 43.8 41.3 40.1

Math 0.00* 0.00* 0.00*

RS 5318 4550 3227 52.4 45.7 48.3 (0.28) (0.22) (0.40)

CS 7182 8460 3620 46.6 41.3 40.3

Filipino 0.00* 0.00*

0.00*

RS 5318 4550 3227 49.8 44.0 48.3 (0.24) (0.24) (0.40)

CS 7182 8460 3620 45.0 39.2 40.5

Araling P 0.00* 0.00* 0.00*

RS 5318 4550 3227 47.3 42.9 43.6 (0.15) (0.17) (0.23)

CS 7182 8460 3620 43.7

8

38.9 38.6

Overall

Achievem

ent

0.00* 0.00* 0.00*

RS 5318 4550 3137 49.8 44.8 47.2 (0.33) (0.31) (0.45)

CS 7182 8460 3494 44.8 40.3 41.0 *Mean difference between RS and CS in the indicated subject and year is significant.

Cohen’s d in parentheses below p value


The effect size was also calculated for each of the 5 subject areas and for the overall

achievement score. Comparatively, for the 2009 and 2010 sample groups, English

returned the highest effect size (d=0.35) followed by Mathematics (d=0.28) in 2009 and

Filipino (d=0.25) in 2010. This pattern, however, is different in 2011, wherein the

biggest effect size (d=0.40) was seen in Mathematics and in Filipino, then followed by

English (d=0.38). The effect size of the overall achievement scores was calculated at

0.33 in 2009, 0.31 in 2010 and 0.45 in 2011 (p < 0.005). This suggests that the mean

scores of RS students were at 33%, 31%, and 45% advantage over the mean scores of

the CS students for the 2009, 2010, and 2011 sample groups respectively. These results

corroborate the findings of the 2007 impact evaluation of Knowledge Channel shows on

students’ achievement scores (Mapa, 2007). While the present study found that the

higher effects were found in English test performance (in 2009 and in 2010) and in

Math test performance (in 2011), Mapa (2007) found that the presence of Knowledge

Channels was likely to be felt in Science and in Mathematics.

Study 1 also looked at gender differences in the test scores across the 5 subject

areas. Each chart displays the pattern of gender differences in the students’ mean

scores. For the 2009 sample group, results showed that the female student respondents

scored significantly higher than males in English, Science, Filipino, and Araling

Panlipunan. However, the difference in mean scores in Mathematics between male and

female students was not statistically significant (Table 7). The overall achievement

scores of the student respondents across the 5-subject areas indicated that the mean

percent scores of females (47.49) was statistically different (p-value< 0.005) from those

of males (46.15).


Table 7

Mean percent scores between male and female students across the 2009, 2010 and 2011

sample groups .

Variable N M P

2009 2010 2011 2009 2010 2011 2009 2010 2011

English 0.00* 0.00* 0.98

Female 7063 6757 3845 50.5 45.7 47.2

Male 5437 6253 3001 49.2 44.4 47.3

Science 0.00* 0.10 0.05

Female 7063 6757 3845 46.2 42.4 41.4

Male 5437 6253 3002 44.6 42.4 42.2

Math 0.07 0.01* 0.21

Female 7063 6757 3845 49.3 43.3 43.8

Male 5437 6253 3002 48.7 42.4 44.4

Filipino 0.00* 0.00* 0.01*

Female 7063 6757 3845 47.7 41.7 44.7

Male 5437 6253 3002 46.2 40.0 43.5

Araling

Panlipunan

0.00* 0.01* 0.25

Female 7063 6757 3845 46.3 40.8 40.7

Male 5437 6253 3002 44.0 39.7 41.3

Overall

Achievement

0.00* 0.00* 0.83

Female 7063 6757 3733 47.5 42.3 44.0

Male 5437 6253 2898 46.2 41.4 43.9 *Mean difference between gender cohorts in the indicated subject and year is significant.

Females outperformed males in all subjects.

For the 2010 sample group, the female students significantly outperformed the male

students in all subject areas except in Science. For the 2011 sample group, it was only in

Filipino that the female students scored significantly (p< 0.005) higher than the males.

In all other subject areas, male students’ mean scores were higher but the difference is

not significant. Overall scores of respondents from the 2011 sample group show that

females have higher mean scores but the difference is not statistically significant.

It can be deduced that it is only the 2009 and 2010 sample groups that females

performed better than the males in terms of their achievement scores (Fig. 6). This

finding confirms previous findings that females scored significantly higher than males

did (Quimbo, 2003; Wentzel, 1988; Amelink, 2009).


Figure 6. Gender differences in the students’ mean scores in 2009 , 2010 and 2011

sample groups in the five subject areas and in the overall score.


Regional Differences

Seven regions were covered for analysis in 2009 sample group, eight regions in

the 2010 sample group, and four regions in the 2011 sample group. The overall mean

scores refer to the average of students’ scores in all subjects (Table 8 & Fig 7). The

overall score of the recipient group (M=49.78) is significantly higher than that of the

comparison group (M=44.78). All regions have recipient schools that performed higher

than their comparison schools except in regions 4a and NCR. Among the recipient

schools, the highest performing students are in Region 4b (M=57.13) and the lowest

performing students come from Region 4a M=(44.58).

Table 8

Overall Mean Achievement Score per region between RS and CS in the 2009 sample

group.

Figure 7. Students’ Overall Mean Scores between Recipient and Comparison

Schools across regions in 2009.

Variable Mean

Recipient Comparison

Overall Achievement 49.78 44.78

Region 1 53.05 45.34

3 53.89 41.11

4a 44.58 45.73

4b 57.13 48.39

6 44.64 42.05

10 45.16 39.82

NCR 48.41 49.84


In each of the subject areas, the RS performed better than CS. Table 9 shows which

specific regions where KC recipients performed better and whose scores are more

convergent or more consistent across different subject areas.

Table 9

Summary of Regions where RS performed better, and have more convergent scores than

CS in the 2009 sample based on their descriptive statistics.

Subject Area

Regions where recipient

schools performed better than

comparison schools

Regions where recipient schools’

scores are more convergent than

comparison schools

English 1, 3, 4b, 6, and 10

None

Science All 1and 6

Math 1, 3, 4b, 6, and 10

4a

Filipino 1, 3, 4b, 6, and 10

4aand 10

Araling Panlipunan 1, 3, 4a, 4b, and 10

4a, 6, 10 and NCR

Overall 1, 3, 4b, 6 and 10

4a

To see if these differences in their average scores are significant and not due to

sampling error, as well as to determine how much does each factor contribute to the

variation in scores, the 2-way ANOVA test of between subjects effects was conducted.

Students’ achievement scores in each subject area was analyzed using a factorial

analysis of variance (ANOVA) with two between participant factors: Type (RS vs. CS)

and Region (1, 3, 4a, 4b, 6, 10, and NCR). Table 10 shows the test results.


Table 10

Two-way ANOVA test of between Subjects Effects using the 2009 sample group.

Subject

Area

F Sig. Partial Eta Squared

Type

Region Type *

Region

Type Re-

gion

Type

* Re-

gion

Type Re-

gion

Type

* Re-

gion

English 287.284 68.255 33.199 0.000 0.000 0.000 0.022 0.032 0.016

Science 146.953 60.227 13.459 0.000 0.000 0.000 0.012 0.028 0.006

Math 156.882 64.107 35.142 0.000 0.000 0.000 0.012 0.030 0.017

Filipino 115.773 91.856 28.206 0.000 0.000 0.000 0.009 0.042 0.013

Araling

Panlipunan

56.508 41.735 20.372 0.000 0.000 0.000 0.005 0.020 0.010

Overall 259.282 108.31 40.858 0.000 0.000 0.000 0.020 0.049 0.019

In English for instance, the main effects due to type (RS or CS), F(1, 12486) =

3.84< 287.284, p<0.0005), and region factors F(6, 12486) =2.10<68.255, p< 0.05), and

the interaction between these F(6, 12486) = 2.10<33.199, p < 0.05, η2 = 0.016 were

unlikely to have arisen due to sampling error, thus suggesting that those who received

the KC videos perform better than those who did not (means of 53.51 and 46.89,

respectively, partial η2 = 0.022). This implies that 2.2% of the overall variance was

attributable to the influence of the KC videos. The main effect of region suggests that

there are regions that perform significantly higher than the others (two of these regions

take the extremes: Region 4b and 4a with means 60.29 and 47.43, respectively, partial

η2 =0.032) in the English test. Thus, 3.2% of the variance in achievement scores was

due to the region.


In the graph (please see Figure 8), this refers to the vertical distance (red line)

between the highest and the lowest point in the blue line. Finally, the interaction

between the type and the region was considerable and accounts for 1.6% of the overall

variance. This interaction can be further investigated using t-tests and analysis of the

following graphs. These analyses showed that the effects of region on both recipient and

comparison groups were such that they were unlikely to have arisen from sampling

error. Similarly, the effects of being in the recipient or comparison group in any region

were also unlikely to have arisen due to sampling error (all p-values < 0.05).

In all subject areas, the main effect of type and region, and their interaction effects

contribute little to the variation in the achievement scores (at most 2.0%). The greatest

main effect by type is seen in English (partial eta squared value of 0.022) and the least

in Araling Panlipunan (partial eta squared value of 0.005). The greatest main effect by

region is seen in Filipino (partial eta squared is 0.036) and the least in Araling

Panlipunan (partial eta squared is 0.020). The greatest interaction effect is found in

Math (partial eta squared is .017) and the least is in Science (partial eta squared is

0.006). All effects are significant, which means that effect sizes (both main and

interaction effects) attributable to the said factors (i.e. type and region) were unlikely to

have arisen due to sampling error. The main effect of type in the variation in students’

overall achievement score is 2.0%. The main effect of region to the variation of overall

achievement score is 4.9%. While the interaction effect of both type and region to

overall achievement score is 1.9%.

The region factor is presented in the horizontal axis of the graph. The type of respondent

factor is represented as lines. The blue line in Figure 8 represents overall achievement

mean score of students in the recipient schools and the green line is that of the students


in the comparison schools. We can immediately see from this graph that having

received the KC videos contribute to a better performance (in most regions).

Figure 8. Line graph illustrating the interaction between the type of respondents and

Region in English test using the 2009 sample group.

For example, the broken vertical lines connecting between the blue and green lines

in Region 1 refer to the difference in overall achievement scores between RS

respondents (performing better because blue line is on top of the green line) and its

comparison group. So, in most regions (except 4a and NCR), the students in the RS

performed better in English than the students in the CS. The main effect of the second

factor region can be seen in each of the two lines. In the blue line (recipients group), we

can see that it is at its peak in Region 3 and lowest at Region 4a. The difference in the


English scores between these two extreme scores refers to the vertical distance (length

of the red line) between these two extreme points in the blue line. In the green line

(comparison group), students in the NCR scored the highest and students in Region 10

got the lowest score. These differences (between regions in the same type) are found to

be significant (the main effect of region in English has p< 0.0005). Since the lines are

not parallel, there is a disordinal interaction between the type of recipients and region

where they come from. The graph shows that in most cases (Regions 1, 3, 4b, 6, and

10), which received the KC videos have a positive effect (increase in gain scores) on

students’ performance. A similar pattern can be observed in the line graphs of each

subject area test (Fig. 9).


Region in English (E), Science (S), Math (M), Filipino (F), and Araling Panlipunan

(AP) tests in the 2009 sample group.


Post Hoc tests were conducted to determine the regions where performance of the

students in the recipient schools are significantly better than the performance of the

students in the comparison schools. Table 11 provides a summary of these regional

differences.

Table 11

Summary of regions where recipient schools performed significantly better than the

students in the comparison schools in the 2009 sample group.

Subject Area

Regions where recipient schools

performed significantly better than

comparison schools

(p < 0.05 and t > 0)

Regions where performance of the

recipient schools and comparison

schools do not significantly differ

(p > 0.05 )

English 1, 3, 4b,6, and 10

NCR

Science 1, 3, 4a, 4b, 10 6, NCR

Math 1, 3, 4b,6, and 10

NCR

Filipino 1, 3, 4b, 6, and 10

None

A.Panlipunan 1, 3, 4b, and 10

4a

Overall 1, 3,4b,6, and 10

NCR

It can be observed from Table 11 that in English, Math and Araling Panlipunan, in

regions 1, 3, 4b, 6 and 10, RS performed better than the CS. While in Science, scores of

the students in the RS and in the CS do not differ significantly. As for region 4a, it was

only in Science, where the RS performed significantly better than CS. While in the rest

of the subjects, NCR’s RS and CS do not significantly differ.

Overall Achievement score per region in 2010

For the 2010 sample group, the overall score of the recipient group (44.38) is

significantly higher than that of the comparison group (40.31). Table 12 shows that in

terms of overall achievement, the top performing RS students come from Region 1


(59.74) and the lowest performing RS students come from CAR (38.15). For each of the

subject areas, RS performed better than CS in most regions.

Table 12

Overall Mean Achievement Score per region between recipient and comparison schools

in the 2010 sample group.

Variable Mean



Region 1 59.74 39.00

2 44.84 39.93

4a 41.98 39.83

5 40.31 43.40

6 49.66 42.58

12 43.11 34.29

CAR 38.15 42.48

NCR 50.27 42.86

Figure 10. Students’ Overall Mean Scores between Recipient and Comparison Schools

across regions in the 2010 sample group.


Table 13


CS in the 2010 sample group based on their descriptive statistics.

Subject Area


schools performed better than

comparison schools


schools’ scores are more

convergent than

comparison schools

English 1, 2, 4a, 6, 12, and NCR

5

Science 1, 2, 4a, 6, 12, and NCR

5

Math 1, 2, 4a, 6, 12, and NCR

5 and CAR

Filipino 1, 2, 4a, 6, 12, and NCR

5

Araling Panlipunan 1, 2, 4a, 6, 12, and NCR

5 and CAR

Overall 1, 2, 4a, 6, 12, and NCR

5 and CAR

Table 14 reveals students’ achievement scores in each subject area analyzed using a

factorial analysis of variance (ANOVA) with two between participant factors: Type (RS

vs. CS) and Region (1, 2, 4A, 5, 6, 12, CAR and NCR).

In English for instance, the main effects due to type (RS or CS), (F(1, 17,842) =

3.84 < 348.763, p < 0.05), and region factors (F(7, 17,842) = 2.10 < 89.522, p < 0.05),

and the interaction between these (F(7, 17,842) = 2.10 < 128.80, p < 0.05, η2 = 0.048)

were unlikely to have arisen due to sampling error, thus suggesting that those who

received the KC videos perform better than those who did not (means of 50.02 and

42.58, respectively, partial η2 = 0.019). This shows that 1.9% of the overall variance

was attributable to the influence of the KC videos. The main effect of region suggests

that there are regions that perform significantly higher than the others (two of these

regions take the extremes: Region 1 and 6 with means 64.75 and 55.34, respectively,


partial η2 =0.034) in the English test. Thus, 3.4% of the variance in achievement scores

was due to the region.

Table 14

Two-way ANOVA Test of between Subjects Effects using the 2010 sample group.

Subject

Area


Type

Region Type * Region

Type Re-gion

Type * Re-

gion

Type Re-gion

Type * Re-

gion

English 348.763 89.522 128.80 0.000 0.000 0.000 0.019 0.034 0.048

Science 182.229 42.534 49.215 0.000 0.000 0.000 0.010 0.016 0.019

Math 237.859 72.170 68.647 0.000 0.000 0.000 0.013 0.028 0.026

Filipino 79.709 54.855 101.62 0.000 0.000 0.000 0.004 0.021 0.038

Araling

Panlipunan

75.866 54.954 65.232 0.000 0.000 0.000 0.004 0.021 0.025

Overall 300.463 112.26 144.41 0.000 0.000 0.000 0.017 0.042 0.054

Finally, the interaction between the type and the region (Fig. 11) was considerable

and accounts for 5.4% of the overall variance. In all subject areas, the main effect of

type and region, and their interaction effects contribute little to the variation in the

achievement scores (at most 1.7%). The greatest main effect by type is seen in English

(partial eta squared value of 0.019) and the least in Filipino and Araling Panlipunan

(partial eta squared value of 0.004). The greatest main effect by region is seen in

English (partial eta squared is 0.034) and the least in Science (partial eta squared is

0.016). The greatest interaction effect is found in English (partial eta squared is .048)

and the least is in Science (partial eta squared is 0.019). All effects are significant,

which means that effect sizes (both main and interaction effects) attributable to the said

factors (i.e. type and region) were unlikely to have arisen due to sampling error. The

main effect of type in the variation in students’ overall achievement score is 1.7%. The


main effect of region to the variation of overall achievement score is 4.2%. The

interaction effect of both type and region to overall achievement score is 5.4%.

Figure 11 Line graph illustrating the interaction between the type of respondents and

region in Overall Mean scores in 2010.

In most regions (except 5 and CAR), students in the RS group performed better than

students in the CS. The main effect of the second factor region can be seen in each of

the two lines. In the blue line (recipients group), we can see that it is at its peak in

Region 1 and lowest at Region CAR. The difference between these two extreme scores

refers to the vertical distance (length of the red line) between these two extreme points

in the blue line. In the green line (comparison group), Region 5 scores the highest and

Region 12, the lowest. These differences (between regions in the same type) are found


to be significant (the main effect of region in English has p< 0.0005) at 5% level of

significance.

Since the lines are not parallel, there is a disordinal interaction between the type of

recipients and region where they come from. The graph shows that in most cases

(Regions 1, 2, 4A, 6, 12, and NCR), having received the KC videos have a positive

effect (increases) on students’ performance. A similar pattern can be observed in the

remaining subject areas.

Post Hoc tests were conducted to see which regions, recipient schools (1)

statistically performed better than, (2) statistically performed poorer than and (3) do not

significantly differ with those of their comparison schools. Table 14 is then refined into

the following:

Table 15

Summary of Regions where students in recipient schools performed significantly better

than those in the comparison schools in the 2010 sample group.

Subject Area

Regions where recipient schools

performed significantly better

than comparison schools

(p < 0.05 and t > 0)

Regions where performance of the



(p > 0.05 )

English 1, 2, 4a,6, 12 and NCR

None

Science 1, 2, 4a,6, 12 and NCR

5

Math 1, 2, 4a,6, 12 and NCR

One

Filipino 1, 2, 4a,6, and 12

None

A.Panlipunan 1,2, 4a, 6 and 12

NCR

Overall 1, 2,4a,6, 12 and NCR

None

It can be observed from Table 15 that for English, Math and Science, in regions 1,

2, 4A, 6, 12 and NCR students in the recipient schools performed better than the their

comparison schools. Almost in all regions and subjects, the RS performed significantly

better than CS. While in Science, Region 5’s RS and CS scores do not significantly


differ. Except in CAR, all RS in other regions performed better than CS in Science. This

may be due to the regularity in schedule of viewing in Science compared to other

subjects.

Overall mean score per region in 2011

In 2011, the overall mean score of the recipient group (47.21) is significantly higher

than that of the comparison group (41.02) as shown in Table 16 and Fig. 12. The

recipient schools scored significantly higher than comparison schools in all regions

except NCR. Overall, among the recipient schools, the highest performing students

come from CARAGA (51.82) and the lowest come from NCR (41.58). Among the

comparison schools, the highest score is registered for NCR (52.55) while the lowest

score is from Region 1 (38.51).

Table 16

Overall Mean Achievement Score per region between recipient and comparison schools


Variable Mean


Overall

Performance

Region 1 44.16 38.51

Region 3 46.09 42.21

CARAGA 51.82 43.14

NCR 41.58 52.55

TOTAL 47.21 41.02


Figure 12 Students’ Overall Mean Score between Recipient and Comparison Schools

across regions in the 2011 sample group.

In each of the subject area, RS performed better than CS. However, it is noticeable that

not all regions contribute to this effect. Table 17 shows which regions across different

subject areas did KC recipients perform better and whose scores are more convergent.

Table 17


CS based solely on their descriptive statistics in the 2011 sample group.

Subject Area


schools performed better

than comparison schools

Regions where recipient schools’

scores are more convergent than

comparison schools

English All except NCR

None

Science All except NCR

NCR

Math All except NCR

None

Filipino All except NCR

None

AralingPanlipunan All except NCR

NCR

Overall All except NCR

Region 3 and NCR

To see if these differences in their average scores are significant and not due to

sampling error, the 2-way ANOVA test of between subject effects was conducted.


Further, the analysis aimed to find out how much does each factor contribute to the

variation in scores.

Students’ achievement scores in each subject area were analyzed using a

factorial analysis of variance (ANOVA) with two between participant factors: Type (RS

vs. CS) and Region (1, 3, CARAGA, and NCR). Table 18 shows the test results.

Table 18

Two-way ANOVA Test of between Subjects Effects in the 2011 sample group.

Subject

Area


Type

Region Type * Region

Type Re-gion

Type * Re-

gion

Type Re-gion

Type * Re-

gion

English 12.42 106.36 32.72 0.000 0.000 0.000 0.002 0.045 0.014

Science 0.951 70.70 23.96 0.329 0.000 0.000 0.000 0.030 0.010

Math 4.83 132.39 62.71 0.028 0.000 0.000 0.001 0.055 0.027

Filipino 18.54 23.65 31.67 0.000 0.000 0.000 0.003 0.010 0.014

Araling Panlipunan

0.108 36.09 22.97 0.743 0.000 0.000 0.000 0.016 0.010

Overall 14.963 88.93 49.65 0.000 0.000 0.000 0.002 0.039 0.022

In English for instance, the main effects due to type (RS or CS), F(1, 6838) =

12.42, p< 0.0005), and region factors F(3, 6838) = 106.36, p < 0.0005), and the

interaction between these F(3, 6838) = 32.72, p < 0.0005, η2 = 0.014) were unlikely to

have arisen due to sampling error. The effect size (η2 = 0.014) suggests that 1.4% of the

overall variance was attributable to the interaction of regional location and presence of

the KC videos.


The main effect of region suggests that there are regions that perform

significantly higher than the others in the test. Thus, 4.5% of the variance in

achievement scores was due to the difference in regional location.

Finally, the interaction between the type and the region was considerable and

accounts for about 1.4% of the overall variance. This interaction can be further

investigated using t-tests and analysis of the following graphs. These analyses showed

that the effects of region on both recipient and comparison groups were such that they

were unlikely to have arisen from sampling error. Similarly, the effects of being in the

recipient or comparison group in any region were also unlikely to have arisen due to

sampling error (all p-values < 0.05 for type*region interaction).

In all subject areas, the main effect of type and region, and their interaction effects

contribute little to the variation in the achievement scores. The greatest main effect by

type is seen in Filipino and the least in Araling Panlipunan and Science. The greatest

main effect by region is seen in Math (partial eta squared is 0.055) and the least in

Filipino (partial eta squared is 0.010). The greatest interaction effect is found in Math

(partial eta squared is .027) and the least interaction effect is in Filipino and Science

(partial eta squared is 0.010). All type*region interaction effects are significant, which

means that effect sizes (both main and interaction effects) attributable to the two factors

(i.e. type and region) were unlikely to have arisen due to sampling error.



Region in English test in the 2011 sample group.

The blue line in Figure 13 represents English achievement average score of recipient

schools and the green line that of the comparison schools. We can infer from the graph

that having received the KC videos contribute to an overall better performance of RS

samples (except in NCR) compared to their comparison groups. For example, the

broken vertical lines connecting between the blue and green lines in Regions 1 and

CARAGA refer to the difference in overall achievement scores between RS respondents

(performing better because blue is on top of green) and its comparison group. So, in all

regions but NCR, the recipients performed better than their comparison groups (Table

19).


The main effect of the second factor region can be seen in each of the two lines. In

the blue line(recipients group), we can see that it is at its peak in CARAGA and lowest

at Region 1. The difference in the overall scores between these two extreme scores

refers to the vertical distance (length of the red line) between these two extreme points

in the blue line. In the green line (comparison group), NCR scores the highest and

Region 1 the lowest. These differences (between regions in the same type) are found to

be significant (the main effect of region in overall achievement scores has p-value <

0.05) at 5% level of significance. Since the lines are not parallel, there is a disordinal

interaction between the type of recipients and region where they come from. The graph

shows that in most regions, having received the KC videos results to a positive effect

(increases) on students’ performance. A similar pattern can be observed in the line

graphs of each subject area test.

Table 19

Summary of Regions where students in recipient schools performed significantly better

than those in the comparison schools in the 2011 sample group.

Subject

Area

Regions where

recipient schools

performed

significantly better

than comparison

schools

(p < 0.05 and t > 0)

Regions where the

recipient schools

significantly

performed below their

comparison schools

(p < 0.05 and t < 0)

Regions where

performance of the

recipient schools and

comparison schools

do not significantly

differ

(p > 0.05 )

English 1, 2, 4a,6, 12 and NCR

5 and CAR None

Science 1, 2, 4a,6, 12 and NCR

CAR 5

Math 1, 2, 4a,6, 12 and NCR

5 and CAR One

Filipino 1, 2, 4a,6, and 12

5, CAR, and NCR None

Araling

Panlipunan

1, 2, 4a, 6 and 12

5 and CAR NCR

Overall 1, 2,4a,6, 12 and NCR

5 and CAR None


Rural vs. Urban Difference

This study further looked into the achievement scores between RS and CS groups

when respondents are grouped according to the rural-urban classification of provinces

and cities. This analysis was motivated by the result in the previous section which found

that in certain regions, comparison schools scored significantly higher than recipient

schools. We hypothesized that schools in urban areas scored significantly better than

schools in rural areas.

Rural vs. Urban Difference in the 2009 Sample

Based on the 2010 census of population and housing (web0.psa.gov.ph), the 13

provinces and cities involved in 2009 study are classified as presented in Table 20

below.

Table 20

Urban-Rural Classification of Provinces and Cities in the 2009 sample group.

Province/City Classification

Ilocos Sur Rural

La Union Rural

Bataan – Mariveles Urban

Tarlac – Concepcion Rural

Cavite – Naic Rural

Rizal Urban

Occidental Mindoro – San Jose Urban

Oriental Mindoro Rural

Antique Rural

Lanao del Norte – Iligan City Urban

Misamis Oriental – Cagayan de Oro City Urban

NCR – Markina Urban


Urban provinces and cities are areas with urbanization level higher than the national

level. In 2010, the national urbanization level was pegged at 45.3%. This suggests that

respondents coming from Bataan, Rizal, Occidental Mindoro, Iligan City, Cagayan de

Oro City, and Marikina City shall be classified as urban respondents for the purpose of

comparative analysis.

Table 21

Differences in mean percent scores between urban and rural respondents in the 2009

sample group.

Variable N M t P

English 10.2a 0.000

Urban 6869 50.51

Rural 5631 48.05

Science 11.1 0.000

Urban 6869 46.88

Rural 5631 43.72

Math 5.5a 0.072

Urban 6869 49.97

Rural 5631 47.92

Filipino 6.9a 0.000

Urban 6869 48.20

Rural 5631 45.70

Araling Panlipunan 7.2a 0.000

Urban 6869 46.67

Rural 5631 43.60

Overall

Achievement

10.7a 0.000

Urban 6869 48.21

Rural 5631 45.32 aThe t and df were adjusted because variances were not equal.

N = 12,500

As can be gleaned from Table 21, the independent samples t-test revealed that mean

scores of the respondents from urban provinces and cities were significantly higher than

those from the rural areas. This result confirms our hypothesis that students in highly

urbanized areas scored higher than those in the rural areas, which implies that both CS

and RS students have equal access to instructional input through Television and other


media, and may explain why students in comparison schools in Region 4A performed

significantly higher than students in recipient schools.

Rural vs. Urban Difference in the 2010 sample

Based on the 2010 census of population and housing (web0.psa.gov.ph), the

locations involved in the 2010 sample were classified and presented in Table 22.

Table 22



Ilocos Norte Rural

Ilocos Sur Rural

Isabela Rural

Batangas Rural

Cavite Rural

Laguna – Pangil Urban

Laguna – Cavinti, Liliw, Luisiana,

Pagsanjan

Rural

Camarines Norte – Daet Urban

Camarines Norte – Mercedes, Vinsons Rural

Sorsogon Rural

Iloilo Rural

Sarangani Rural

Benguet Rural

Pasig Urban

Urban Provinces and Cities are areas with urbanization level higher than the national

level. In 2010, the national urbanization level was pegged at 45.3%. This suggests that

respondents coming from Pangil, Laguna, Daet, Camarines Norte, and Pasig shall be

classified as urban respondents for the purpose of comparative analysis.


Table 23

Differences in percent mean scores between urban and rural respondents in the 2010

sample group.

Variable N M t p

English 0.762a 0.446

Urban 809 46.23

Rural 17,033 45.73

Science -1.948a 0.051

Urban 809 41.70

Rural 17,033 42.83

Math -1.516a 0.129

Urban 809 42.50

Rural 17,033 43.61

Filipino 0.005a 0.996

Urban 809 41.50

Rural 17,033 41.50

Araling Panlipunan -0.705a 0.481

Urban 809 40.06

Rural 17,033 40.67

Overall

Achievement

-0.899a 0.368

Urban 809 41.98

Rural 17,033 42.45 aThe t and df were adjusted because variances were not equal.

N = 17,842

As can be gleaned from Table 23, the independent samples t-test reveals that the

scores of students from both urban provinces and cities and the rural areas are not

significantly different. This means that as for the 2010 sample group, there is not

enough evidence to support the hypothesis that students in urbanized areas score higher

than those in the rural areas. The findings further revealed that the mean score

differences between urban and rural areas is highest in science and lowest in Filipino.


Rural vs. Urban Difference in the 2011 sample

Based on the 2010 census of population and housing (web0.psa.gov.ph), the 7

provinces and cities involved in the 2011 sample group are presented in Table 24.

Table 24



Butuan Urban

Ilocos Rural

La Union Rural

Nueva Ecija (San Jose City) Urban

Pasig Urban

Surigao del Norte Rural

Zambales Rural

Urban Provinces and Cities are areas with urbanization level higher than the

national level. In 2010, the national urbanization level was pegged at 45.3%. This suggests

that respondents coming from Butuan, Nueva Ecija (San Jose City), Pasig shall be classified

as urban respondents for the purpose of comparative analysis.

As can be gleaned from Table 25, the independent samples t-test revealed that

student respondents from urban provinces and cities consistently scored significantly

higher than those student respondents from the rural areas.


Table 25

Differences in percent mean scores between urban and rural respondents in the 2011

sample group.

Variable N M SD t df P

English 9.84a 4514

a 0.000

Rural 4644 45.70 19.449

Urban 2202 50.50 18.540

Science 7.08 6845 0.000

Rural 4645 40.82 16.473

Urban 2202 43.79 15.563

Math 12.79a 4188

a 0.000

Rural 4645 41.81 20.397

Urban 2202 48.73 21.128

Filipino 10.93 6845 0.000

Rural 4645 42.40 19.582

Urban 2202 47.92 19.405

AralingPanlipunan 10.97a 4358

a 0.000

Rural 4645 39.07 21.124

Urban 2202 44.88 23.419

Overall

Achievement

11.94a 4275

a 0.000

Rural 4429 42.49 13.814

Urban 2202 46.88 14.248 aThet and df were adjusted because variances were not equal.

N = 6846

Synthesis of Comparative Analysis

Comparative analysis of the mean scores across subject areas and in the overall

scores between rural and urban type of provinces and cities in the 2009 and 2011

sample groups revealed interesting findings. Student respondents from schools in the

urban areas performed better than those student respondents in the rural areas.


Grade Level Differences

To see if there are significant differences in mean scores between groups according

to type and grade levels, and to describe how much does each factor contribute to the

variation in scores, the 2-way ANOVA test of between subjects effects was conducted.

Conditions of normality and homogeneity of variance were satisfied, which warranted

the use of ANOVA in overall scores as well as per subject area between groups and

within each of the groups.

Overall Achievement Score per Grade Level in the 2009 sample

A total of 12,787 student respondents constitute the 2009 sample groups included in

the analysis of the differences in mean test scores among grade levels and between

school type (RS and CS). In this 2009 sample group, 43% were students from RS and

the other 57% were students from CS (Table 26).

Table 26

Distribution of participants per type and per grade level in the 2009 sample group.

Grade Number of KC

Recipients

Number of

Participants from

Comparison

Group

Total Participants

from each Grade

Level

3 1,207 1,792 2,999

4 1,374 1,833 3,207

5 1,380 1,792 3,172

6 1,357 1,765 3,122

7 90 50 140

8 88 59 147

Total 5,496 7,291 12,787

Table 27 shows the mean of the overall scores in the 6 grade levels. The overall

mean achievement score is 49.34 which vary by 15.97. Grade 6 respondents have the


highest mean score at 54.66, while the Grade 3 students recorded the lowest mean score

(44.61).

Table 27

Mean achievement percent score per grade level between RS and CS in the 2009 sample

group.

Variable Mean

Achievement Test Score Recipient Comparison


Grade 3 44.61 37.87

4 52.01 47.20

5 47.29 43.91

6 54.66 50.16

7 42.52 39.52

8 40.24 39.48

Overall, mean achievement test score of the students from the recipient schools

(46.89) was significantly higher compared with the mean achievement test score of the

students from the comparison schools (43.02). Across grade levels, mean scores of the

students in the recipient schools were likewise significantly higher than the mean scores

of the students in the comparison schools.

Students’ achievement scores in each subject area was analyzed using a factorial

analysis of variance (ANOVA) with two between-participant factors: type (RS:

recipient of KC vs. non-recipient schools labeled as CS-comparison schools) and grade

levels. Table 28 shows the test results.


Table 28

Two-way ANOVA test of between subjects effects in the 2009 sample group.

Subject

Area


Type

Grade

Level

Type

*

Grade

Level

Type Grade

level

Type

*

Grade

level

Type Grade

level

Type

*

Grade

level

English 384.02 102.76 0.86 0.000 0.000 0.461 0.030 0.024 0.000

Science 270.88 2819.67 3.19 0.000 0.000 0.023 0.021 0.404 0.001

Math 241.40 82.03 11.36 0.000 0.000 0.000 0.019 0.019 0.003

Filipino 173.54 37.77 12.32 0.000 0.000 0.000 0.014 0.009 0.003

Araling

Panlipunan

75.49 1343.95 5.19 0.000 0.000 0.001 0.006 0.244 0.001

Overall 353.47 346.36 7.14 0.000 0.000 0.000 0.028 0.077 0.002

The main effects due to the type are significant in all subject areas: English (F(1,

11492)=3.84 < 384.02, p < 0.05, η2 = 0.030), Science (F(1, 11492)= 3.84 < 270.88, p <

0.05, η2 = 0.021)Math (F(1, 11492)= 3.84 < 241.40, p < 0.05, η

2 = 0.019), Filipino (F(1,

11492)= 3.84 <173.54, p < 0.05, η2 = 0.014) and Araling Panlipunan (F(1, 11492)= 3.84

< 75.49, p < 0.05, η2 = 0.006). This means that there is enough evidence to show that

results were unlikely to have arisen due to sampling error. It is sufficient to conclude

that in all subject areas, those who received the KC videos performed better than their

comparison who did not receive it.

The main effects due to the grade level are all significant in all subject areas (p <

0.05). In Science, about 40% of the variation in scores is attributed to the grade level.

The main effect of grade level suggests that there are grade levels that significantly

performed better than the others.

Interaction effects (Type * Grade level) of these two factors in all subject areas were

found to be significant, therefore unlikely to have arisen from sampling error. Take for

instance the average score in Math and Filipino, which have the greatest effect size


caused by the interaction of type and grade level (η2 = 0.003). This interaction can be

further investigated using t-tests and analysis of the following graphs. These analyses

showed that the effects of grade level on both recipient and comparison schools were

such that they were unlikely to have arisen from sampling error. Similarly, the effects of

being in the recipient or comparison group in any grade level were also unlikely to have

arisen due to sampling error (all p-values < 0.05).

In terms of overall mean scores, the main effects of type and grade level, and their

interaction effects, contributes little to the variation in the achievement scores. Of these

three effects, the greatest was the main effect of grade level (eta squared 0.077). All

effects are significant, which means that effect sizes (both main and interaction effects)

attributed to the two factors (type and grade level) were unlikely to have arisen due to

sampling error.

Table 29

Summary of the test performance of student participants in the Recipient Schools in the

different Grade levels for the 2009 sample group.

Subject Area

Grade levels where recipient schools

performedsignificantly better than

comparison schools

(p < 0.05 and t > 0)

Grade levels where performance of the



(p > 0.05 )

English 3, 4, 5, 6, 7, 8 None Science 3, 4, 5, 6, 7, 8 None

Math 3, 4, 5, 6, 7, 8 None

Filipino 3, 4, 5, 6 , 7, 8 None APanlipunan 3, 4, 5, 6, 7, 8 None

Overall 3, 4, 5, 6, 7, 8 None

Post Hoc tests were conducted to see which grade levels, the recipient schools (1)

statistically performed better than, (2) statistically performed poorer than and (3) do not

significantly differ with those of their comparison schools.


It can be deduced from Table 29, that in all subjects, except in Araling Panlipunan,

RS performed significantly better than CS. In Araling Panlipunan. Mean scores of

Grade 5 students in RS did not significantly differ from the mean scores of the students

in CS.

Overall achievement scores per grade level in the 2010 sample group

Sample sizes in each grade level for RS and CS in the 2010 sample group are

given in Table 30. It can be gathered that 34.97% of the students were from RS and the

remaining 65.03% of the students were from CS (Table 30).

Table 30

Distribution of Participants per Type and per Grade Level in 2010 sample group.

Grade Number of KC

Recipients

Number of

Participants from Comparison Group

Total Participants

from each Grade Level

3 1,163 2,208 3,371

4 1,105 2,055 3,160

5 1,169 2,074 3,243 6 1,113 2,123 3,236

Total 4,550 8,460 13,010

Table 31 shows the overall mean scores across the four grade levels. Average scores

in all grade levels indicate that students are in the beginning level. Grade 6 respondents

have the highest mean score at 49.83and the lowest mean score (41.41) is from grade 3.

It is below half of the total number of items.

Table 31

Overall mean achievement percent score in the 2010 sample group.

Variable Mean



Grade 3 41.41 34.63

4 46.39 42.46

5 41.77 37.88

6 49.83 46.40


The recipient group had an overall mean score (44.78) that is significantly higher

than that of the comparison group (40.28). Across all the four grade levels, the students

in the recipient group have a mean score significantly higher than the comparison group.

To see if these differences in the mean scores between groups according to type and

grade levels are significant and to determine how much each factor contributes to the

variation in scores, the 2-way ANOVA test (Table 32) of between subjects effects was

conducted.

Table 32

Two-way ANOVA Test of between Subjects Effects in the 2009 sample group.

Subject

Area


Type

Grade

level

Type

*

Grade

Level

Type Grade

Level

Type

*

Grade

level

Type Grade

level

Type

*

Grade

level

English 335.67 100.16 11.79 0.000 0.000 0.000 0.025 0.023 0.003

Science 217.79 3,410.58 20.08 0.000 0.000 0.000 0.016 0.440 0.005

Math 145.92 45.69 9.14 0.000 0.000 0.000 0.011 0.010 0.002

Filipino 182.63 28.61 4.37 0.000 0.000 0.004 0.014 0.007 0.001

Araling

Panlipunan

118.71 1,194.98 2.72 0.000 0.000 0.043 0.009 0.216 0.001

Overall 334.82 343.60 9.80 0.000 0.000 0.000 0.025 0.073 0.002

The main effects due to the type are significant in all subject areas: English (F(1,

13010) = 3.84 < 335.67, p < 0.05, η2 = 0.025), Science (F(1, 13010) = 3.84 < 217.78, p

< 0.05, η2 = 0.016) Math (F(1, 13010) = 3.84 < 145.91, p < 0.05, η2 = 0.011), Filipino

(F(1, 13010) = 3.84 < 182.63, p < 0.05, η2 = 0.014) and Araling Panlipunan (F(1,

13010) = 3.84 < 118.71, p < 0.05, η2 = 0.009). This means that there is enough

evidence to conclude that in all subject areas, those who received the KC videos

performed better than the comparison group, who did not receive KC videos.



0.05). In Science, about 44% of the variation in scores can be attributed to the grade

levels. The main effect of grade level suggests that there are grade levels that

significantly performed better than the others.

Interaction effects (Type * Grade level) of these two factors in all subject areas are

significant, therefore unlikely to have arisen from sampling error. For instance: the

mean score in Science, which had the greatest effect size caused by the interaction of

type and grade level (η2 = 0.005). This interaction can be further investigated using t-

tests and analysis of the succeeding graphs. These analyses showed that the effects of

grade level on both recipient and comparison groups were such that they were unlikely

to have arisen from sampling error ( p < 0.0005).

Overall, the main effects of type and grade level, and their interaction effects,

contribute little to the variation in the achievement scores. Of these three effects, the

greatest was the main effect of type and grade level (eta squared 0.005). All effects are

significant, which means that effect sizes (both main and interaction effects) attributable

to the factors type and grade level were unlikely to have arisen due to sampling error.

Post Hoc tests were conducted to better understand the significant differences in the

achievement of students from recipient schools vis-à-vis the achievement of the students

from comparison schools.


Table 33

Summary of performance in achievement tests across grade levels for recipient schools


Subject Area

Grade levels where recipient schools

performedsignificantly better than

comparison schools

(p < 0.05 and t > 0)

Grade levels where performance of

the recipient schools and comparison


(p > 0.05 )

English 3, 4, 5, 6 None

Science 3, 4, 5, 6 None

Math 3, 4, 5, 6 None

Filipino 3, 4, 5, 6 None A.Panlipunan 3, 4, 6 None

Overall 3, 4, 5, 6 None

It can be observed from the Table 33 that across all the 5-subject areas and in all the

four grade levels (Grades 3,4, 5 and 6), students from RS performed significantly better

than students from CS. There was no instance wherein students from CS performed

significantly better than RS.

Overall achievement score per grade level in the 2011 sample group

A total of 5,945 students (Table 34) were included in this analysis, wherein 46.5%

came from KC-recipient schools and the remaining 53.5% were students from the

comparison schools.


Table 34

Distribution of Participants per type and per grade Level in the 2011 sample group.

Grade

Number of KC

Recipients

Number of

Participants from

Comparison

Group

Total Participants

from each Grade

Level

3 554 669 1223

4 546 717 1263

5 563 669 1232

6 572 684 1256

7 253 214 467

8 275 229 504

Total 2763 3182 5945

While Table 35 shows that students in the recipient group had an overall mean score

(46.85) that is significantly greater than the comparison group (mean score 39.76).

Table 35

Overall Mean Achievement Score per grade level between RS and CS in the 2011

sample group.

Variable Mean Recipient Comparison

Overall

Performance

46.85 39.76 Grade 3 46.48 39.07

4 54.55 43.94

5 48.70 41.32

6 55.08 47.39

7 36.56 33.94

8 39.75 36.45

To see if the difference in students’ average scores between groups according to

type and grade levels are significant and and to determine how much each factor

contributes to the variation in scores, the 2-way ANOVA test of between-subjects

effects was conducted.


Table 36

Two-way ANOVA Test of between Subjects Effects for the 2011 sample group.

Subject

Area


Type

Grade

level

Type

*

Grade

Level

Type Grade

level

Type

*

Grade

level

Type Grade

level

Type

*

Grade

level

English 190.375 280.984 19.315 0.000 0.000 0.000 0.027 0.224 0.019

Science 134.614 600.607 12.323 0.000 0.000 0.000 0.019 0.381 0.012

Math 208.967 190.603 10.865 0.000 0.000 0.000 0.030 0.163 0.011

Filipino 206.308 26.672 6.211 0.000 0.000 0.000 0.029 0.027 0.006

Araling

Panlipunan

42.789 328.581 23.143 0.000 0.000 0.000 0.006 0.252 0.023

Overall 253.735 160.057 13.34 0.000 0.000 0.000 0.037 0.145 0.014

Table 36 shows the ANOVA test results of between subjects effects for the 2011

sample group. The main effects due to the type are significant in all subject areas:

English (F(1, 6830)=3.84 < 190.375, p < 0.05, η2 = 0.027), Science (F(1, 6831)= 3.84 <

134.614, p < 0.05, η2 = 0.019)Math (F(1, 6831)= 3.84 < 208.967, p < 0.05, η

2 = 0.030),

Filipino (F(1, 6831)= 3.84 <206.308, p < 0.05, η2 = 0.029) and Araling Panlipunan (F(1,

6831)= 3.84 < 42.789, p < 0.05, η2 = 0.006). This means that there are enough

evidences to show that results were unlikely to have arisen due to sampling error.

Therefore it is sufficient to conclude that in all subject areas, those students who

received the KC videos performed better than their counterparts who did not receive it.


0.05). In Science, about 38% of the variation in scores is caused by grade level. The

main effect of grade level suggests that there are significant differences in the

achievement of students across the 8 grade levels .that significantly performed better

than the others. The interaction effects (Type * Grade level) of these two factors in all

subject areas are significant, therefore unlikely to have arisen from sampling error. For


example, the average score in Araling Panlipunan returned the greatest effect size

caused by the interaction of type and grade level (η2 = 0.023). This interaction can be

further investigated using t-tests and analysis of the following graphs. These analyses

showed that the effects of grade level on both recipient and comparison groups were

such that they were unlikely to have arisen from sampling error. Similarly, the effects of

being in the recipient or comparison group in any grade level were also unlikely to have

arisen due to sampling error (all p-values < 0.0005).

Since all effects were significant, the effect sizes (both main and interaction effects)

attributable to the two factors (type and grade level) were unlikely to have arisen due to

sampling error.

Table 37

Summary of performance in the achievements tests across 6 grade levels.

Subject Area

Grade levels where recipient

schools performed

significantly better than

comparison schools

(p < 0.05 and t > 0)

Grade levels where

performance of the recipient

schools and comparison

schools do not significantly

differ (p > 0.05 )

English 4,5,6,7,8 None

Science 4,5,6,7,8 None

Math 3,4,5,6,7,8 None

Filipino 3,4,5,6,7,8 None APanlipunan 3,4,5,6,8 None

Overall 3,4,5,6,7,8 None

It can be gathered from Table 37, that for the 2011 sample group, students from the

recipient schools performed better than students from the comparison schools in 6 grade

levels (Grades 4, 5, 6, 7, 8 and 9). Students from RS also performed higher than their

CS counterparts in all the 5-subject areas.


Conclusion

Study 1 compared the achievement in the five subject areas between two groups of

students: students from schools that integrated Knowledge Channel Shows (RS) in their

curriculum and students from schools with no Knowledge Channel shows, referred to as

comparison schools (CS). The considerably large sample size (N=32,368 students)

included in the analysis provided sufficient evidence that led to the following significant

findings:

1. Students from KC recipient schools (RS) have significantly higher scores than those

from comparison schools (CS). Further investigation on the effect of exposure to KC

shows suggests that students in the RS performed 33% better than the students in the CS

for the 2009 sample group, 31% for the 2010 sample group and 45% fo the 2011 sample

group. Interestingly, highest effects were noted in English (in 2009 and in 2010) and in

Math (in 2011).

2. Across various regions in the country, the RS respondents generally performed better

than the CS respondents.

3. Female students in both 2009 and 2010 sample groups outperformed the males in

almost all subjects.

4. Overall, students from RS performed better across all subjects in all grade levels

compared with students from CS. Interestingly, grade 6 students were found to

perform significantly higher than students from other grade levels.


Study 2 The Effect of Knowledge Channel Shows on Academic

Achievement of Students from Various Learning Groups

Methods

Research Design

Study 2 utilized an experimental research design to investigate whether students

from various learning groups (i.e. Passive, Active, Lecture) will have significantly

different gains in terms of achievement scores. Students in the passive learning group

were simply made to watch Knowledge Channel (KC) videos. Students in the active

learning group watched KC videos with the teacher facilitating the processing of the

contents of the video. Students in the lecture group were taught using the lecture type of

instruction.

Participants

A total of 926 students were involved in this study and these students were

randomly assigned to each of the three learning groups. The experiment was conducted

in four recipient schools located in Luzon and Mindanao. The first experiment was

implemented in Don Mariano Matugas NHS and Don Enriquez Navarro Elementary

School in Siargao, Mindanao; the second experiment was conducted in NAIC National

High School of Cavite and Pineda Elementary School of Pasig.

Instruments.

A 15-item test was developed for Science, Mathematics, English, and

AralingPanlipunan. For each subject area, the researchers selected three topics with a

corresponding Knowledge Channel video. These tests were subjected to content


validation by subject matter experts from the Br Andrew Gonzalez College of

Education. A lesson plan was developed for the teachers who were assigned to the

Active Group. The contents in the videos were incorporated into the lesson plan.

Specific instructions were provided for teachers regarding the processing activities that

they should do before, during, and after viewing the shows. A similar lesson plan was

given to the teachers who were assigned to the Lecture Group.

Procedure

Prior to the experiment, teachers who were purposively selected to participate were

given an orientation, copies of the videotapes of lessons selected for the study, and the

lesson plan. The list of students randomly assigned for each learning group were also

prepared by the participating teachers.

During the experiment, the Active Group went through several procedures. At the

start of the experiment, the objectives and the procedures of the experiment were briefly

discussed. The students then took a multiple-choice test which required them to use

their recall, comprehension, analytic, and evaluative skills in test taking. Questions were

based on the selected videos of Knowledge Channel. The teacher spent 3-5 minutes

asking questions about the topic. Its purpose was to determine whether or not the

students had prior knowledge about the topic. Then the teacher showed the videotape

for the first topic. The teacher paused the videotape then asked students some questions

about the lesson. The teachers explained some difficult concepts that students did not

understand. The rest of the topic was viewed by the students. The teacher asked

questions to test how much information was understood and retained by the students.

Difficult concepts were explained by the teacher. These steps were repeated for the two


other topics selected for the study. Finally, the students took the test after having viewed

all the videos. The questions in the pre-test and post-test are the same.

The Passive Group went through the same procedure except that the teacher did not

explain the topic to the students and did not ask questions before, during, and after

viewing the videotapes. In the Lecture Group, the students were tested before and after

the lecture instruction. The teachers directly taught the three topics discussed in the

videos using the transmissivel way (asking questions, explaining concepts, showing

visual aids, writing on the board).

Data Analysis

Paired Samples t-test was used to determine if there is difference between the

pre-test and posttest scores for all student participants, in each learning group (passive,

active and traditional lecture), and in each area (Luzon and Mindanao). Cohen’s d was

used to determine the effect size of the instruction type in each learning group and area

in their gain scores. A student’s gain score is determined by the difference between the

pretest and posttest scores. In order to investigate further the difference in scores across

the three learning groups, two-way Analysis of Variance (ANOVA) was used.

Study 2 Results

About 70% of the participants came from Luzon (n= 626) and the other 30% were from

Mindanao (n=293). As regards gender, 57.79% were females and the remaining 42.21%

of the student participants were males (Fig. 14).


The participants in Study 2 were distributed among the three learning groups in

about the same number. The lecture group was composed of 343 students (37%), 314

students in the passive (34%) and 262 students in the active group (29%).

Comparative Analysis

In order to determine whether there is significant difference in the pretest and

posttest mean percent scores in each learning group and their effect sizes, an

independent samples t-test was used. The same statistical test was used to determine the

significant differences between pretest and posttest in each location.

Mean scores across learning groups and their effect sizes

The three learning groups posted gains in mean percent scores (posttest > pretest)

as differences were all in positive values. Across the three learning groups, t-tests were

found to be significant, where t(313)=5.714, p<0.0005, d=0.2316 for the Passive group,

t(342)=8.605, p<0.0005, d=0.4088 for the Lecture group, and t(261)=13.976, p<0.0005,

d=0.7415 for the Active group. Overall, across the three learning groups, t(918)=15.697,

p<0.0005, d=0.4273.

Figure 14 Profile of the student participants in study 2 in terms of gender, location, and

grade level.


Table 38

Mean scores across learning groups and locations in study 2.

Learning

Group

Mean Percent

Scores Mean

Difference

Standard

Deviation Paired Samples

Effect Size

Pre-

test

Post-

test

Pre-

test

Post-

test t Df

Sig.

(2-tailed)

Cohen’s

d

Passive 39.39 43.61 4.22 17.53 18.89 5.714 313 0.000 0.2316

Lecture 38.13 44.66 6.53 15.94 16.01 8.605 342 0.000 0.4088 Active 36.56 48.15 11.45 14.82 16.40 13.976 261 0.000 0.7415

Overall 38.11 45.26 7.15 16.22 17.23 15.697 918 0.000 0.4273

Location

Mean Percent

Scores Mean

Difference

Standard

Deviation Paired Samples

Effect

Size

Pre-test

Post-test

Pre-test

Post-test

t df

Sig.

(2-

tailed)

Cohen’s

d

Luzon 40.26 49.22 8.97 16.56 16.58 16.974 625 0.000 0.5407

Mindanao 33.53 36.79 3.26 14.48 15.45 3.918 292 0.000 0.2177

Mean scores between location and their effect sizes

Table 38 also shows the pretest and posttest mean percent scores per location and

their corresponding standard deviations. Both Luzon and Mindanao means show gains

in scores (post-test > pre-test), as mean differences are both positive values (3.26 and

8.97). Results revelaed significant difference between the posttest and pretest for Luzon

where t (625)=16.97, p < 0.0005 and for Mindanao where t (292)=3.918, p < 0.0005.

Learning Group and Location Effects on Gain Score

Students’ gains scores in each of the three learning groups and two locations were

analyzed using a factorial analysis of variance (ANOVA) with two between participant

factors: learning group (passive, lecture and active) and location (Luzon vs. Mindanao).

Table 39 shows the cross-tabulation of the explanatory variables (learning group and

location).


Table 39

The 3 x 2 contingency table of mean gain scores in study 2.

Location Combined mean

gain scores Luzon Mindanao

Passive 5.96 1.17 4.22

Lecture 7.39 5.02 6.53

Active 13.53 3.64 11.45

Total 8.97 3.26 7.15

Inspection of the contingency table showing mean gain scores, the Active group in

Luzon garnered the highest (13.53), followed by the lecture group (7.39), then the

passive group last (5.96). This, however, is not the case in Mindanao where the lecture

group got the highest (5.02), followed by the active group (3.64), then the passive group

last (1.17).

The combined scores of both areas show that the active group performed the

highest (11.45), followed by the lecture group (6.53) and the passive group (4.22).

Combining all scores from the three learning groups but comparing these per area, the

Luzon group has gained higher percent scores than the Mindanao group. This can be

further clarified through the line graph (Fig. 15).


Figure 15. Estimated marginal means of post-test scores by learning group and location.

The lines in the graph intersect. This means that there is interaction between the

two factors (learning group and location) affecting students’ posttest scores. The gain

scores due to learning group were moderated by the location where the participants

came from. Teacher’s processing skills vis-à-vis their lecture skills can be a plausible

explanation for this finding. To determine if the differences in mean gain scores by

learning group and location, as well as their interaction are significant, the 2-way

ANOVA test of between-subjects effects was conducted. Normality and homogeneity of

variances were tested and satisfied warranting the use of the two-way ANOVA.


Table 40

ANOVA test of Between-Subjects Effects for Learning Group and Location.

Source Sum of

Squares

Df Mean

Square

Fcomputed Fcritical p-

value

Effect

Size

(2)

Between 14049.689a

Learning Group 2849.005 2 1424.502 8.088 F(2, 913)

= 3.01

0.000 0.016293

Location 5894.295 1 5894.295 33.466 F(1, 913) = 3.85

0.000 0.033709

Learning

Group*Location

1588.669 2 794.335 4.510 F(6,913)

= 2.11

0.011 0.009086

Within 160806.120 913 176.129

Corrected Total 174855.808 918 a squared = 0.060 Computed using alpha = 0.05

Table 40 shows the 2-way analysis of variance (ANOVA) with two between

participant factors: learning group (passive, lecture and active) and location (Luzon and

Mindanao) and their effects on students’ gain scores. The main effect due to learning

group is significant since the computed value 8.088 is greater than the critical value F(1,

913) = 3.01, and the p< 0.0005. This means there is evidence to support that learning

group can improve students’ academic performance. Specifically, if we discount the

location, the active group performed better than the other two learning groups.

Table 41

Learning Group Pairwise Comparison.

Learning Group Pair Mean Difference

(Gain Scores)

p-value (1-tailed)

Active vs. Lecture 4.9207* 0.000

Active vs. Passive 7.2334* 0.000

Lecture vs. Passive 2.3127 0.067

*The mean difference is significant at the 0.05 level.

Post-Hoc Analysis (Tukey HSD) of pairwise comparisons (Table 41) shows that

there is no significant difference between the lecture and the passive groups. However,

the active group is significantly better than either the lecture or the passive group.


Hence, hypotheses 1 and 2 were substantiated by this result. This finding corroborates

Zhang et al.’s (2006) finding that students with interactive video that utilizes interaction

performed significantly better than the students who belong to the non-interactive or

passive group. This implies that simply using the technology in the classroom may not

be enough to improve student’ achievement. Therefore, the teacher remains to be an

important partner in the effective utilization of KC videos.

Moreover, there exists significant main effect due to location since the computed

F=33.466 > critical value F(1, 913)=3.85, p< 0.0005). A bigger percentage of students’

gain score is accounted for by this factor (effect size = 3.37%). This means that student

participants in Luzon performed better than those in Mindanao.

The interaction effect of these two factors is significant (computed F-value = 4.510 is

greater than the critical value F(6, 913) = 2.11, p< 0.011). This means that all effects

due to learning group, location and their interaction - on gain scores are significant.

Thus, there is evidence to suggest that students’ gain scores due to learning group were

moderated by the location.

Conclusion

Results obtained in this experimental research elucidated why students from three

learning groups (Passive, Active, Lecture) had significantly different gains in terms of

achievement scores. Specifically the following significant findings are advanced:

1. The mean gain scores for each of the learning groups yielded significantly

positive results, with posttest scores > pretest scores. Students in the Active


group had the highest gain score. Therefore the use of KC shows with teachers’

processing accounted for the 74.15% difference in the students’ posttest scores.

2. There are significant differences between the gain scores of the students in the

Active group and the gain scores of the students in either the Passive group or

the Lecture group. Results further revealed that the gain scores of the students in

the Passive group is comparable with the gain scores of the students in the

Lecture group.

3. There is enough evidence to support that learning group may account for

differences in students’ achievement.


Study 3 Students’ Attitude Towards KC Shows, Students’ Achievement and

Teacher’s Ability to Integrate KC Shows

Methods

Research Design

Study 3 utilized a descriptive correlational design to investigate the relationship

between students’ attitude towards knowledge channel shows and students’

achievement with the teachers’ ability to integrate channel shows as a mediating

variable on students’ achievement.

Participants

The participants in study 3 comprised of 143 high school teachers across grade

levels and disciplines and 950 high school students in 8 public schools in the Division

of La Union (Table 42). During the conceptualization of study 3, the research team has

identified the Division of La Union as the study setting since there were several public

high schools in this division, which were recipients of Knowledge Channel (KC) shows

package.

Table 42

Number of teacher participants by school.

School Number of

Teachers

Aringay NHS 14

Damortis NHS 18

Don Eulogio De Guzman Memorial NHS 19

President Elpidio Quirino NHS 18

Pugo Central NHS 19

Rosario Integrated School 17

Southern Naguilian NHS 19

Tubao NHS 19

Total 143


Efforts were made to equalize the number of teacher participants in the study, hence

almost equivalent percentages of teachers in the four grade levels were chosen by the

division Superintendent (Table 43). In terms of subject taught during observation,

highest number of teacher participants were Araling Panlipunan teachers (n=29

teachers) and the least number were English teachers (n=17 teachers).

Table 43

Profile of teacher participants by grade level and subject taught.

Grade Level Frequency Percent

Grade 7 39 27.3

Grade 8 36 25.2

Grade 9 32 22.4

Grade 10 36 25.2

Total 143 100

Subject Taught During Observation Frequency Percent

Araling Panlipunan 29 20.28

English 17 11.89

Values Education 20 13.99

Filipino 24 16.78

Math 26 18.18

Science 27 18.88

Total 143 100

As indicated in Table 44, among the components of teachers’ ability to integrate KC

shows in their lessons, the highest is content knowledge (3.35) using a scale of 1 to 4.

Table 44

Mean scores of the teachers’ ability to Integrate KC Shows.

Skills Mean

Technological Knowledge (TK) 3.05

Pedagogical Knowledge (PK) 3.08

Content Knowledge (CK) 3.35

As can be seen from the profile of student respondents (Table 45) for study 3, each

school and each grade level has similar number of students. Of the 950 student


respondents, the total number for each school ranges from 116-123 (12.1 % to 12.9%).

In terms of gender profile, 66% of the student respondents were females and only 34%

were males, with ages ranging from 10 -18 years old.

Table 45

Student respondents’ profile by school, grade level, and age.

School Frequency Percent

Aringay National High School 119 12.5

Damortis National High School 120 12.6

Don Eulogio de Guzman Mem. Natl High School 123 12.9

President Elpidio Quirino National High School 119 12.5

Pugo Central National High School 118 12.4

Rosario Integrated School 116 12.2

Southern Naguilian National High School 120 12.6

Tubao National High School 115 12.1

Grade Level Frequency Percent

Grade 7 238 25.1

Grade 8 239 25.2

Grade 9 246 25.9

Grade 10 227 23.9

Age Frequency Percent

10 1 0.1

11 13 1.4

12 176 18.5

13 232 24.4

14 218 22.9

15 220 23.2

16 73 7.7

17 5 0.5

18 2 0.2

To find out the teachers’ ability to integrate knowledge channel shows in the

lessons, 8 education supervisors from the Division of La Union observed the 143

teachers from November 3, 2014 to February 13, 2015.


Instruments

To gather pertinent data for study 3, three instruments were utilized in this study

such as: (1)assessment package, (2) observation protocol (Teaching Observation

Protocol on Use of KC Shows–TOP-KCS), and (3) attitude checklist (Attitude Towards

Knowledge Channel Shows- ATKCS).

An assessment package was developed for grades 7-10 covering the subject areas

in Science, Mathematics, Filipino, English, and Araling Panlipunan. The reliability of

the test instruments was determined using Cronbach alpha.

Table 46

Reliability of achievement test instruments used.

Level Subject No. of items Cronbach's Alpha

Gra

de

7

English 30 0.724

Science 25 0.774

Math 27 0.714

Filipino 30 0.734

Araling 30 0.772

Gra

de

8

English 30 0.720

Science 25 0.768

Math 25 0.664

Filipino 30 0.706

Araling 30 0.623

Gra

de

9

English 30 0.633

Science 25 0.847

Math 25 0.677

Filipino 30 0.825

Araling 30 0.638

Gra

de

10

English 30 0.627

Science 25 0.675

Math 15 0.668

Filipino 30 0.701

Araling 30 0.721

Teaching Observation Protocol on the Use of KC Shows –TOP-KCS is a 25-item

observation checklist that is divided into three components such as teachers' technical,


pedagogical, and integration skills. This instrument was drawn from McGrath, Karabas

& Willis (2011) TPACK framework. The first part of the checklist aims to measure the

teachers' technological knowledge in operating the video. The second part, the

pedagogical knowledge, intends to measure the teachers' ability to elicit students' prior

knowledge about the lesson prior to viewing knowledge channel shows, their ability to

ask questions, developing students' critical thinking skills during and after the viewing

of knowledge channel shows. The third part, content knowledge intends to assess the

teachers’ mastery of learning content. TOP-KCS was designed by the research team and

validated by two experts in the field. Revision of TOP-KCS was done according to the

experts’ comments.

An attitude checklist, ATKCS, was also developed by the research team and

validated by the two experts in the field. The instrument was revised following the

expert’s comments. The checklist contains items that measure the components of

attitude such as students’ feelings and attentiveness while watching KC shows, their

perceived importance and usefulness of KC shows in understanding concepts in five

subjects covered in this study and in the application of these KC shows to life, and their

personal evaluation about their learning through KC shows.

Table 47 shows the indicator loadings and reliability coefficients of the variables

under study such as students’ attitude towards KC shows and teachers’ ability to

integrate KC shows in their lessons.


Table 47

Indicator loadings, average variance extracted, and reliability coefficients of the

variables in the study.

Indicator/Item

Loading

AVE Composite

Reliability

Cronbach's

Alpha

A. Attitude .581 .890 .849

Feels happy while watching

KC shows in five subject areas 0.574

Focused while watching KC

shows 0.848

Learned many things from KC

shows in five subject areas 0.855

KC shows helped remember

lessons in five subject areas 0.903

Encouraged others to watch KC

shows 0.656

KC shows are useful in

everyday life 0.680

B. Teacher Achievement .840 .954 .936

Technological Knowledge 0.808

Pedagogical Knowledge 0.948

Content Knowledge 0.956

Combination of Technological,

Pedagogical, and Content

Knowledge

0.946

Note: All indicators/item loadings are statistically significant (p <.001)

Analysis of the results of the measurement model reveals that the loadings of the

items for both attitude and teacher achievement constructs are statistically significant

and greater than the 0.5 threshold (Hair et al., 1987 & 2009 cited in Kock, 2013); the

average variance extracted (AVE) for each construct is greater than the .5 cut-off

(Fornell and Larker, 1981) and the composite reliability and Cronbach’s alpha are

greater than the .7 cut-off (Fornell & Larcker, 1981; Nunnaly, 1978; Nunnally &

Bernstein, 1994), indicating that the constructs have convergent validity. Moreover, the

square roots of the AVE (diagonal elements in Table 48) are larger than the correlations

of the constructs (off-diagonal elements in the same table), indicating that each

construct has discriminant validity based on Fornell & Larker (1981) criterion.


Table 48

Average Variance Extracted and correlation coefficients among constructs.

1 2

1. Attitude (0.917) -0.009

2. Teacher Achievement -0.009 (0.763)

Note: Diagonal elements are the square root of AVE between constructs. For

discriminant validity, the diagonal elements should be larger than the off-diagonal

elements.

Procedure

LIDER sent a communication to the Schools Division Superintendent (SDS) of

DepEd La Union on August 8, 2014, indicating the list of recipient schools where study

3 would be conducted. Upon the approval of the Schools Division Superintendent

(SDS), the assessment package in five subject areas were administered to 950 student

respondents in eight schools in La Union.

To measure students’ ability to integrate KC shows in their lessons, LIDER

communicated with the KC coordinator and made arrangements with DepEd Division

supervisors in La Union to conduct observation of teacher respondents in eight schools

using TOP-KCS, an observation protocol designed by the research team. Prior to their

observation, the research team went to DepEd Division of La Union Office and oriented

the supervisors on the process of rating the teachers’ ability to integrate KC shows in

their lessons using TOP-KCS. They were also given a rubric aside from the observation

protocol sheet to calibrate their ratings. During the orientation, the research team found

out that the target respondents of 320 teachers was impossible to meet because many

teachers were newly hired in eight schools covered in the study. Hence, to get the

novice teachers to be matched with seasoned teachers trained by Knowledge Channel in


integrating KC shows in lesson presentation and discussion may not be feasible either

because results may be biased and therefore not valid. Hence, the original question of

identifying the significant difference between the achievements of teachers trained and

those not trained in integrating KC shows in their lessons was not considered in this

study. This is the reason why the target respondents of N= 320 was narrowed down to

143 trained teachers.

Supervisors’ observations of classes were completed in two months. Field

researchers went with the supervisors to administer the attitude checklist, ATKCS, to

the student respondents who took the assessment package test.

Data Analysis

Path Analysis was used to depict the hypothesized causal paths of variables in this

study. Specifically, the Maximum Likelihood (ML) estimation method in AMOS was

used to estimate the path coefficients. Maximum likelihood was preferred because of its

ability to run simultaneous and iterative estimation of coefficients. Furthermore, the ML

estimation accounts for disturbances or “error terms” caused by other exogenous

variables.

The path model of this study was hypothesized such that teacher’s ability to

integrate technology in teaching mediates the effect of students’ attitude towards

technology on their academic achievement. Fig. 16 presents the hypothesized path of

these variables.


Figure 16. Hypothesized path diagram of the effect of students’ attitude towards

Knowledge Channel shows on their academic achievement.

Results

Teachers’ Performance

Using the classroom observation protocol, supervisors evaluated the teacher

participants in terms of their proficiency skills. Proficiency skills in this study is

described in terms of the following components: technological knowledge,

pedagogical knowledge, and content knowledge. As can be gleaned from the Table 49,

the teachers were highly rated. Teachers’ mean ratings ranged between 3.27-3.57,

depicting very good performance. Out of a perfect score of 4, the highest rating was on

Content knowledge (3.57), then followed by technological knowledge (3.30) and

pedagogical knowledge (3.27).


Table 49

Teachers' Mean Ratings in ability to integrate KC shows.

Student’ Attitude towards KC shows

Students were asked to respond to a symmetric agree-disagree scale, where 1-

strongly disagree, 2 – disagree, 3 – agree and 4 – strongly agree. A response of 3 or 4

would mean a positive attitude towards KC videos. The attitudinnaire would indicate

whether - KC helps them to understand better their lessons, information in the videos

are credible or factual, the material provide them knowledge, skills, values and

inspiration, etc.

Generally, the students’ mean attitude rating ranges from 3.01 to 3.57, indicative of

a positive attitude towards the KC videos. Table 50 presents the mean rating and

standard deviation of each of the items in the attitudinnaire.

Teacher’s Proficiency Skills Mean Std. Dev.

Technological Knowledge (TP1)

The teacher has the necessary technical skills to use the video,

in terms of setting it up, pausing/resuming the video, and

trouble shooting.

3.30 0.84

Pedagogical Knowledge (TP2)

The teacher has ability to

Pre-viewing : activate prior knowledge effectively, motivates

students, elicits predictions about the video;

While watching: take note of portions in the video that elicit

strong reactions from the students, elicits predictions and

inferences about specific portions in the video;

Post-viewing: Asks specific questions/provides learning

activities that require simple recall; understands of concepts

presented in the video and to real life situations, analyze,

evaluate, synthesize and create ideas; monitors students’

progress by giving feedback and scaffolding;

3.27 0.43

Content Knowledge (TP3)

The teacher was able to clearly explain/expound the topic,

correct students’ answers and answer student’s questions,

clarify information in the videos.

3.57 0.54


Table 50

Mean ratings of students’ attitude towards KC shows.

*inversely coded

Students’ Achievement per Subject

The student participants of this study were administered an achievement test in five

subject areas. Fig. 17 shows the mean scores of these students in English, Math,

Science, Filipino, and Araling Panlipunan. As can be gleaned from the graph, students

garnered the highest score in Filipino (M=43.28) and lowest in Araling Panlipunan

(M=33.18).

Mean SD

At1 I believe that knowledge channel shows can help me

understand easily my lessons. 3.40 0.725

At2 believe that the lessons discussed in knowledge channel

shows are credible or based on facts because they were

prepared by experts in the fields.

3.39 0.918

At3 I believe that knowledge channel shows can help me

appreciate lessons. 3.37 0.771

At4 I believe that knowledge channel shows will provide me

knowledge. 3.57 0.764


skilss. 3.56 0.733


values. 3.57 0.766


inspiration. 3.51 0.740

At8 I feel sad when I miss watching knowledge channel shows*. 3.01 0.879

At9 I tell others to watch knowledge channel shows 3.38 0.703


Figure 17. Students’ achievement scores in the 5 subject areas.

The succeeding section elucidates whether the students’ attitude towards KC shows

has an effect on their achievement scores. It also seeks to know if the ability of teachers

to integrate KC shows in teaching mediates such effect.

Effect of Student’ Attitude towards KC Shows on Student Achievement

IBM®SPSS®AMOS version 6 was used in testing the path relationship

hypothesized in this study employing the Maximum Likelihood estimation. The direct

relationship between students’ attitude towards KC shows and students’ achievement

was first conducted in order to establish the baseline estimates.

It was revealed that Attitude has a positive significant direct effect on students’

achievement (β=41.8unstandardized). The beta estimates suggest that a unit increase in

students’ attitude would translate to a 41.804 units increase on students’ achievement

scores.


Table 51

Estimates for the relationship between Students’ Attitude towards KC shows and

Students’ Achievement when Teacher Performance is introduced as mediating variable.

Estimate

Achievement Attitude 41.804

After Mediation

Teacher Performance Attitude 0.053

Achievement Attitude 40.086

Achievement Teacher Performance 2.191

When teacher performance was introduced into the model, it was revealed that attitude

has a positive effect on teacher performance (Table 51) . Teacher performance also

returned a positive effect towards achievement. Further examination of other causal

relationships in the path diagram revealed that Teacher’s Performance (TP) positively

affects students’ achievement. Table 51 shows that every unit increase in the rating of

teachers’ ability to integrate KC shows translates to about 2.2 units increase in students’

achievement. Figure 18 reveals the path diagram of the mediation model showing the

estimates.


Figure 18. Path diagram when Teacher’s performance is introduced as a mediating

variable on the relationship between students’ attitude towards KC shows and students’

achievement.

Conclusion

Analysis of quantitative data using the maximum likelihood (ML) estimation

method of Path Analysis depicted causal paths of the variables in this study that led to

the following conclusions:

1. The relatively high mean ratings of students’ attitude towards Knowledge

Channel shows indicate a positive attitude among the student respondents.

2. Students’ attitude towards KC shows has a significant direct positive effect on

students’ achievement.


3. The model revealed that teachers’ ability to integrate KC shows in instruction

partially mediate the effect of students’ attitude on achievement since students’

attitude remained to have a positive significant effect on students’ achievement

even after introducing the mediation variable.


Study 4 Teachers’ Lived Experiences in Integrating Knowledge Channel Videos

in Instruction

Methods

Research Design

Study 4 is a qualitative research designed to describe the lived experiences of

teachers in integrating Knowledge Channel videos in instruction. The study made use of

a written questionnaire and focus group discussions (FGD) in obtaining data. Written

questionnaire was used to profile the teacher participants and to determine how frequent

the KC videos are viewed, how the the KC videos are used in the lesson, and how the

KC videos enhance the teaching and learning experience. Focus group discussions

(FGD) probed on the different teaching strategies and methods that the teachers employ

when integrating KC videos into the lesson and how the teachers use KC videos in

teaching. For qualitative content analysis of the data from the FGD, the framework

method was used. This methodological approach examines the content of the FGD in

order to derive meaning and particular implications for describing the lived experiences

of the teachers. The approach involved identifying commonalities and differences in the


thereby seeking to draw descriptive and/or explanatory conclusions clustered around

themes.


Participants

Participants of study 4 included 30 teachers from three schools located in the three

major islands (Luzon, Visayas and Mindanao). Profile of the teacher participants is

presented in Figure 19.

As can be gleaned from Figure 19, 10 teachers each come from a school in Luzon,

Visayas and Mindanao. There are more female teachers (n=26) compared to their male

counterparts.

Figure 19. Profile of teacher participants included in Study 4 (N=30).


Instruments

The study made use of a written questionnaire and focus group discussions (FGD) in

obtaining data. Written questionnaire was used to profile the teacher participants and to

determine how frequent the KC videos are viewed, how the the KC videos are used in

the lesson, and how the KC videos enhance the teaching and learning experience. Focus

group discussions (FGD) probed on the different teaching strategies and methods that

the teachers employ when integrating KC videos into the lesson and how the teachers

use KC videos in teaching.

The written questionnaire has three parts: Teachers’ Profile, Showing Knowledge

Channel Shows in School, and Watching Knowledge Channel Shows Outside of

School. A total of 22 items were included in the questionnaire: 10 items for part 1, 8

items for part 2, and 4 items for part 3. Completion of the questionnaire lasted for about

an hour. While focus group discussions were conducted to probe on the teachers’

beliefs, attitudes, feelings, experiences, and reactions (Gibbs, 1997). FGD was done

thrice (once per loction) for about an hour using an FGD protocol. The FGD protocol

(i.e., 12 items) were aligned to the survey questionnaire. The FGDs were audio-recorded

to preserve the actual and natural language and to record data with utmost objectivity

and accuracy.

Procedure

Prior the actual data gathering, instruments were prepared, pilot tested and validated.

Letters of requests were sent to participating schools for the conduct of data gathering.

As soon as the schools principals approved the request, three groups of researchers were

sent to the three different locations namely: Cavite, Iloilo and Siargao. Prior to actual


data-gathering, a briefing session was conducted to orient the participants about the

rationale of the study. The participants were able to finish the survey within the allotted

time. Thereafter, the FGD was conducted. Twelve questions were raised duing the FGD

and it delved on teacher’s experiences related to integrating KC videos.

Data Analysis

For qualitative content analysis of the data from the FGD, the framework method

was used. This methodological approach examines the content of the FGD in order to

derive meaning and particular implications for describing the lived experiences of the

teachers. The approach involved identifying commonalities and differences in the


thereby seeking to draw descriptive and explanatory conclusions clustered around

themes.

The qualitative responses of this study were analyzed using the 3E framework of

technology-enhanced learning (TESEP, 2007). This framework consists of three broad

iterative stages of learning transformation referred to as the “3E continuum” or simply

“continuum” in this study. Despite being iterative, it can be noted that each stage of the

continuum represents an increasing level of learner ownership and control. Although the

3 stages can be seen as a continuum of change in teaching practice, they should not be

viewed as mutually exclusive (TESEP, 2007). The analysis proceeded with these 3

stages set as primary categories in the coding process. The coding process was

conducted utilizing MaxQDA10™.


Study 4 Results

As can be seen from Fig. 20, there were three documents loaded into the software

for analysis. These are the transcripts of the FGDs conducted in Luzon (NAIC Coastal

National High School), Visayas (Janiuay Elementary School), and Mindanao (Don

Enrique Navarro Memorial School). The Code system window shows the preset

categories Enhance, Extend, Empower. A new code was also introduced for the

emerging codes that were deemed crucial in validating the analysis. It can be gleaned

further that a total of 132 segments of the three transcripts were coded, where most of

the codes are on describing the typical scenario of KC integration in teaching. The

document browser shows the coded segments in the active transcript. The code

summary is presented (Fig. 21).

Figure 20. Screenshot of the MaxQDA10 project illustrating the document,

browser and code system.


Figure 21. Screenshot of the MaxQDA10 document browser showing the coded

segments in the active transcript.

As can be noticed from Fig. 21, the empower code did not yield any coded segment

in any of the three transcripts. This suggests that these codes did not emerge during the

FGDs. This led the researchers to further examine the teachers’ responses in the

questionnaire.


Table 52

Summary of codes and the coder’s memos.

Code Source Memos

Enhance

FGD

Transcript and

Questionnaire

Main Lesson – Introduction of the lesson is given in

respective classrooms before going to the viewing

room. Processing is conducted inside their

respective classrooms. Mode: 20 minutes viewing time. Range 15-30 minutes viewing time.

Enrichment – Supplementary material. Conducted

as an activity (film viewing). Sometimes not in lesson plan of teacher.

Motivation Activity – Discussion is conducted

immediately. Students can relate to the video. Or a

preview of main lesson. Students are really interested in viewing videos.

Principal’s Role – Encourage/Require integration of

KC shows

Choosing topics. Not all topics of KC can be used. The teacher must pick only the suitable to the

current topic.

Technology is available but limited. Need to

maximize the use of what are available.

Extend Questionnaire

Descriptives

Teachers give assignments based on KC Video.

Teachers assign projects emerging from the lesson

and inspired by KC videos.

Empower Questionnaire Descriptives

Teachers may give assignments which students need to watch KC videos and other resources

outside the school.

Teachers tell the students to watch KC shows outside the school.

Teachers tell students to watch other videos and

materials outside the school. The purpose is for students to gain more knowledge.

Effectiveness FGD

Transcript

Students score higher when aided with KC videos

than in topics without KC videos. Students are

excited to learn. Note that learners today are mostly visual learners.

Teachers observed their students gain broader

understanding.

Issues FGD Transcript

Schedule does not match with current topic. Interval with the next lesson is long.

Pacing of students is also a problem. Some low

performing students delay the lessons and the more that lessons do not match with KC.

Only one viewing room.

Recommendations FGD

Transcript

Coordinate well on schedule and topics. Add more

topics and videos Add more TV sets (if possible one in every

classroom) equipped with cable connection. Add

internet connection too.


Enhance generally involves a straightforward and effective use of technology and

structured peer support opportunities (Table 52). At this level, the students are viewed

as being more actively involved in their learning experience and having greater degree

of responsibility for their learning than traditional classroom lecture.

This study looked into the available learning technology in schools that facilitated

technology integration and structured peer learning. The questionnaire part of this study

revealed that desktop computer is the most prevailing technology where 83% (n=25) of

the teacher respondents reported to have been using in classroom teaching. This is

followed by television set (TV) at 80% and CD and DVD players at 60%. Other

learning technologies that teachers use in classroom teaching include cellphones (56%)

and tablets (24%). It is noteworthy to mention that despite having computer and TV sets

in classrooms, only few of these facilities are hooked to the internet and cable

connection.

The teacher respondents in this study were also probed on the various learning

activities they introduce in classroom teaching which are aided by the Knowledge

Channel (KC) shows. It was found out that KC shows are primarily used for activities

identified at the “enhance” level of the 3E continuum. These activities include using KC

shows as both an opening activity for the lesson or as the main lesson. When asked on

which part of the classroom teaching the video is shown, survey revealed that majority

(62%) of the teacher respondents shows the videos in the middle of a classroom

discussion. The teachers were probed further on the specific activities introduced

together with the KC shows. Teachers were asked how frequently they ask questions

about the topic before showing the video, pause the video and clarify some concepts,


ask questions to check their comprehension, conduct post-viewing discussion, and

assign group activities based on the video shown. It was found out that although they

acknowledged that at some point they introduced these activities to supplement the

content of the KC shows, the frequency of doing so is very seldom. Given the scale of 1

to 4 to examine the frequency of introducing these activities, where 1 represents “not

even once” and 4 represents “very frequent”, the teachers rated these activities at an

average ranging from 1.5 to 1.96 signifying that they seldom introduce these activities.

Teachers were probed further on the typical scenarios that the KC shows were

utilized in teaching. It was revealed that the lesson is being introduced by the teacher in

their respective classrooms while they queue for the KC schedule. The teacher then

brings his class to the KC viewing room on the timeslot of the specific KC video to

watch, and back to their classroom for post-viewing processing. Teachers reported that

they conduct post-viewing processing in order to discuss thoroughly the concepts in the

video. “Most of the times after viewing, we use the video as a point of discussion”. The

time allotted to discuss the content of the KC shows ranges from 15 to 30 minutes, with

50% of the respondents declared doing it in an average of 20 minutes. This signifies that

teachers give an ample time to process the video in order to build and develop the

content lesson at hand. This is indicative that the teachers are cognizant of their crucial

role in a technology-enhanced learning activity.

There are some teachers who declared having obtained copies of specific KC videos

in CDs and DVDs. When these teachers use the KC shows as the main lesson, they

employ the “pause-play” technique, where they pause the video for a while to throw

questions, and emphasize the important concepts.


In order to maximize the utility of the available learning technologies in schools,

this study sought to know how the school principals encourage their teachers to

integrate technology in their teaching. It was revealed by the 72% (n=22) of the

respondents that their respective school principals require them to integrate KC shows

in their lessons. It was further found out that 59% (n=18) were required by the principal

to include viewing of KC shows in their lesson plan. Further examination during focus

group discussions revealed that the teacher respondents from Visayas and Mindanao

schools were the ones required by their principals to include viewing of KC shows in

their teaching.

Extend engages the students in collaborative or individual tasks which offer new

opportunities to extend the classroom activities in ways that provide choice and control

in what, when, and how students learn. At the extend level, students move from an

active to pro-active modes which require them to make some key decisions about the

activities they undertake.

The activities that the teacher respondents indicated in this study that extend the

classroom learning encounter included assignments and projects. Again, although they

acknowledged that at some point they have given assignments and projects related to or

based on the KC shows, they seldom do so. Given the scale of 1 to 4 where 4 represents

“very frequent” and 1 “not even once”, it was found out that teachers rated both “giving

assignments” and “assigning projects” at an average of 2.38 (seldom). The

questionnaire part of this study further revealed that respondents from Luzon give

assignments more frequently than those from Visayas and Mindanao. On the other hand,


respondents from Visayas assign projects more often than those from Luzon and

Mindanao.

Empower drives the students to seek learning independently. This stage of the

continuum represents that “ideal” technology-enhanced learning where students take

full control of their learning.

In terms of empowering the students to seek learning independently, this study

simply looked into how the teachers moved their students to watch videos and seek

other resources to further enhance their learning. It was found out that what the teachers

did to encourage independent learning was simply to pose assignment questions at the

end of the discussion. Teachers encourage their students to answer the questions by

watching other KC videos and other related materials that could help them answer the

question. All the teacher respondents declared that they instruct their students to watch

KC videos and other related materials outside the school. When probed on their reasons

for doing so, the most common answer was - for students to gain more knowledge about

the topic from “other sources”. This strategy is apparently inclined towards empowering

the students to exercise responsibility and control of their own learning endeavor. The

fact that questions were given as an assignment, teachers may be able to monitor the

learning progress of his students. This suggests that a mechanism to monitor students’

progress in independent learning may be adapted.

Implementation Issues

This study also investigated the various issues that the teachers encountered in

implementing KC integration in teaching. Common among the responses is the timing

and scheduling of KC shows. Recalling the typical scenario where KC shows are


integrated in teaching, the teachers have to queue their classes for the timeslot of the

specific video they need to watch. This entails adjusting to the schedule of the KC

shows. While it may work for some, most of the teachers find this difficult because

most of the times, the scheduled KC shows do not coincide with the topic at hand. One

teacher disclosed “Halimbawa, iba yung topic kapag ipapadala mo sila sa knowledge

channel (viewing room)… hindi po magkatugma” (For example- the topic at the

Knowledge Channel (viewing room) does not coincide with topic (in class)). In some

cases, teachers simply bring their class to the viewing room and discuss whatever video

is being shown. “Kung ano man yung pinanonood namin, yun yung ididiscuss namin”

(we just discuss whatever video is being shown). Some teachers have to swap schedules

with other teachers in order to catch the scheduled KC shows. “Ginagawa po namin is

hinihiram po namin yung time nang ibang guro para sa viewing maam tapos

kinabukasan yung time namin ang gamitin nila” (What we do is we exchange class

schedule with our co-teachers for our KC viewing). Other teachers have to skip some

topics in order to catch the schedule KC shows. It was also revealed that the teachers are

not receiving regularly the KC schedule of shows. “Dati po nagkocoincide yung topic at

alam namin ang I view for the whole semester” (Before the topics coincide and we

know what (videos) to view for the whole semester). Hence, they cannot determine

when to bring their classes to the KC viewing room. These scenarios are deemed

significant contributory factors why teachers seldom integrate the KC shows in their

teaching. However, it is also important to note that the pacing of lessons is another

problem. There are instances that despite having a schedule of KC shows, some topics

lag behind as teachers have to teach the topic again for the low-performing students.

“Depende po sa IQ nang mga bata kasi minsan kailangan namin e reteach yung


lesson” (It depends on the students’ IQ because sometimes we need to reteach the

lesson).

However, despite these issues, teacher respondents declared that they are convinced

that integrating KC shows in the curriculum effectively enhances learning. Given a

scale of 1 to 5 where 5 represents the highest perceived effectiveness of KC shows in

helping students learn the lessons, results revealed that all teachers gave a rating of 4.

When probed further, these answers are based on teachers’ observation of their

students’ scores during examinations. It was revealed that students score higher when

KC shows are integrated compared to classes/topics without KC shows. “After nila

nanood nagbigay ako nang quiz then nakakuha sila nang matataas na score compare

yung nag lecture lang ako ng lecture” (When I give a quiz after viewing KC videos, the

students get higher scores than when I just give a lecture”. Some teachers are even

surprised to see that their students are able to answer even the seemingly difficult

questions. “Yung hindi mo na expect na makakuha sila nang tama o makasagot sila”

(that times when you do not expect your students to get the right answer).

Teacher respondents were then asked what they can recommend to improve the

implementation of the integration of KC shows in teaching. Apparently, all teachers

recommended that the schedule for the entire school year be set. Further, the teachers

must be informed of this schedule so they can also plot their classes based on the given

KC schedule. Moreover, teachers also recommended that the cable subscription and

access to KC shows be brought to their individual classrooms so they need not queue at

the viewing room and students need not transfer from their respective classrooms to the

viewing room.


Conclusion

The use of the 3E framework of technology integration in interpreting and analyzing

the lived experiences of teachers was found to be appropriate. Reflecting carefully on

these lived experiences, it can be deduced that the teachers’ techniques in integrating

Knowledge Channel videos fall primarily within the enhance level of the 3E continuum.

This suggests that teacher training is needed to further develop their pedagogy in

integrating technology in order to achieve the stage where students are self-motivated to

learn. Following the social constructivist lens of the framework, results of this study

maintain that teachers must be cognizant of their primary role as facilitators of learning.

Through the lived experiences of the teacher participants, the social constructivist roles

of teachers at the enhance level is recognized. There are some activities that the teachers

employ in teaching that are reflective of the extend level of technology integration. The

empower level manifests in the process of giving assignment questions which calls for

students to watch other KC shows and related learning materials.


References

Adoguzel & Timcin, 2010. The effect of creative drama on student achievement in the

instruction of some development and learning theories. Procedia Social and

Behavioral Sciences 9 (2010) 1741–1746.

Aliasgari, Riahinia, Mojdehavar, (2010) "Computer‐assisted instruction and student

attitudes towards learning mathematics",Education, Business and Society:

Contemporary Middle Eastern Issues, Vol. 3 Iss: 1, pp.6 – 14.

Amelink, C (2009). Gender Differences in Math Performance. SWE-AWE CASEE

Overviews. Retrieved from http://www.AWEonline.org.

Anderson, D. R., Huston, A. C., Schmitt, K.L., Linebarger, D.L., & Wright, J.C. (2001).

Early childhood television viewing and adolescent behavior: The recontact study.

Monographs of the Society for Research in Child Development, 66 (1), vii-147.

Ayers JB (1972). Elementary Teachers’ Attitudes towards Instructional Television. The

Journal of Experimental Education. Taylor & Francis, Ltd.

Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in

social psychological research: Conceptual, strategic, and statistical considerations.

Journal of Personality and Social Psychology, 51, 1173-1182.

Bryant, J., Mullikin, L., McCollum, J., Ralastin, L., Raney, A., Miron, D., Thompson,

S., Stuart, Y., Mundorf, N., Mundorf, J., Wilson, B., and Smith, S. (1998). Effects of

one year’s viewing of Blues Clues. Tuscaloosa, AL: Institute for Communication

Research, The University of Alabama. Baydar, N., Kagitcibasi, C., Kuntay, A., &

Goksen, F. (2008). Journal of Applied Developmental Psychology, 29, 349-360.

Chen, M., & Hodder, L. (1997). A research overview of FASE productions' TV series on

science, mathematics, and technology education. Unpublished manuscript.

Christakis, D.A. et al.. “Early Television Exposure and Subsequent Attentional

Problems in Children,” Pediatrics 113 (2004): 708–13.

Cruz, M. (August, 2012). Children’s TV Act given ‘real teeth’ with IRR. Philippine

Daily Inquirer.

Eckenrod, J, Rockman, S. (1988). Connections between Television and the Social

Studies Curriculum. Social Education, v52 n5 p357-62 Sep 1988.

Foundation for Advancements in Science and Education (1997). Classroom television:

A useful resource for mathematics and science education. Evaluation of The Eddie

Files. Los Angeles: FASE.

Gardner, H. (1985). The Mind’s New Science: A History of the Cognitive Revolution.

New York, NY; Basic Books Publishing.

Harwood, W.S. McMahon, M.M. (1997). Effects of Integrated Video Media on Student

Achievement and Attitudes in High School Chemistry. Journal of Research in

Science Teaching,34 (6), 617–631.

Hattie, J. (2011) Ranking: Influences and Effect Sizes Related To Student Achievement.

Visible Learning for Teachers: Maximizing Impact on Learning. ISBN-

13: 8601401316398 ISBN-10: 0415690153 Edition: 1st

Hendry , D. Instructional Television's Changing Role in the Classroom. The

Technology Source, May/June 2001.

http://www.emeraldinsight.com/action/doSearch?ContribStored=Aliasgari%2C+M

http://www.emeraldinsight.com/action/doSearch?ContribStored=Riahinia%2C+N

http://www.emeraldinsight.com/action/doSearch?ContribStored=Mojdehavar%2C+F

http://www.aweonline.org/

http://www.jstor.org/action/showPublisher?publisherCode=taylorfrancis

http://www.amazon.com/dp/B007M9E88C/ref=rdr_kindle_ext_tmb


Hofer, M; Grandgenett, N; Harris, J..; and Swan, K. (2011). , "Testing a TPACK-Based

Technology Integration Observation Instrument". Teacher Education Faculty

Proceedings & Presentations. Paper 19.

Kaya, Z., Kaya, O., Emre, I. (2013) Adaptation of Technological Pedagogical Content

Knowledge Scale to Turkish Educational Sciences: Theory and Practice, v13 n4

p2367-2377

Koehler, M. J., & Mishra, P. (2008). Introducing TPCK. In J. A. Colbert, K. E. Boyd,

K. A. Clark, S. Guan, J. B. Harris, M. A. Kelly & A. D. Thompson (Eds.),

Handbook of Technological Pedagogical Content Knowledge for Educators (pp. 1–

29). New York: Routledge.

Knowledge Channel Foundation, Inc. (2009). Knowledge Channel Foundation, Inc.:

an overview. Retrieved from

https://www.ammado.com/nonprofit/48611/articles/8868

Linebarger, D. L.,Kosanic, A.Z.,& Greenwood, C.R. (2004). Effects of viewing the

television program Between the Lions on the emergent literacy skills of young

children. Journal of Educational Psychology, 96(2), 297-308.

Mapa, D. (2007). Impact Evaluation of the Programs of Knowledge Channel on the

Performance of the Schools in the National Achievement Test (NAT). Final

Report.UP Statistical Research Foundation, Inc.

Mapa, D. (2009). Proficient Measures for Quality Education (PMQE). Final Report.UP

Statistical Research Foundation, Inc.

Marshall, J. (2002) Learning with Technology Evidence that technology can, and does,

support learning. San Diego State University.

Mishra, P and Koehler, M. (2006). Technological Pedagogical Content Knowledge: A

Framework for Teacher Knowledge. Michigan State University.

Moeller, B. (1996). Learning from television: A research review (Reports Issue No.11).

Center for Computation and Technology.Nielsen Report on Television . Northbrook,

IL: A.C. Nielson Co.

Nicolaidou, M. and Philippou, G. 1999. Attitudes towards mathematics , self-efficacy

and achievement in problem-solving. Retrieved from

www.dm.unipi.it/~didattica/CERME3/proceedings/Groups/TG2/TG2_

nicolaidou_cerme3.pdf

Pelletier, J. (2011). The effects of educational television viewing in an early childhood

family literacy intervention. Retrieved from

http://www.tvo.org/learnwithtvo/resources/PelletierTVO.pdf.

Rhema, A., and Miliszewska, I. (2012). “The potential of e-learning in assisting post-

crisis countries in re-building their higher education systems: the case of Libya,”

Issues in informing Science and information technology, 2012, vol. 9, pp. 149-160.

Register, D. (2004).The effects of live music groups versus an educational children’s

television program on the emergent literacy of young children. Journal of Music

Therapy, 41, 2.

Savenye, Wilhelmina, Davidson, and Smith. (1991). "Teaching Instructional Design in a

Computer Literacy Course." Educational Technology Research and

Development, 39(3), 49-58.

Schmidt, D. A., Baran, E., Thompson, A. D., Mishra, P., Koehler, M. J., & Shin, T. S.

(2009). Technological pedagogical content knowledge (tpack): The development

and validation of an assessment instrument for preservice teachers. Journal of

Research on Technology in Education, 42(2), 123-149.

https://www.ammado.com/nonprofit/48611/articles/8868

http://www.dm.unipi.it/~didattica/CERME3/proceedings/Groups/TG2/TG2_%20nicolaidou_cerme3.pdf

http://www.dm.unipi.it/~didattica/CERME3/proceedings/Groups/TG2/TG2_%20nicolaidou_cerme3.pdf

http://www.tvo.org/learnwithtvo/resources/PelletierTVO.pdf


Schmidt, M E and Anderson, D (2007) “The Impact of Television on Cognitive

Development and Educational Achievement” in Children and Television: Fifty Years

of Research, Norma Pecora, John P. Murray, and Ellen Ann Wartella, eds., Mahwah,

NJ: Lawrence Erlbaum, pp. 65-84

Seels, B., Fullerton, B., Berry, L., & Horn, L. (2008).Research on learning from

television. Retrieved from

http:///www.coe.csusb.edu/faculty/newberry/classes/archive/fall2008

Shoemaker, A. (2011). Do educational TV programs have positive effects on children

learning? Online [Available]

http://www.associatedcontent.com/shared.shtml?content_type=article&content_type

_id=5630635

Skolnik, R., & Smith, C. (1993, February). Educational technology: Redefining the

American classroom. Network News and Views, 12 (3), 79-83.

TESEP. (2007). TESEP in Practice: The 3E Approach. Retrieved from Edinburg Napier

University Transforming and Enhancing the Student Experience through Pedagogy:

Retrieved from: http://www2.napier.ac.uk/transform/TESEP_3E_Approach.pdf

Tips for Using Instructional Video and Public Television Programming in the

Classroom. The Technology Source Archives at the University of North Carolina.

Retrieved from http://www.thirteen.org/edonline/ntti/formanagers/02Media.pdf on

February 2015.

Wentzel, K. (1988). Gender differences in Math and English Achievement: A

longitudinal study. Sex Roles, 18(11-12), 691-701.

Wilson, B.J., Linz, D., Federman, J., Smith, S., Paul, B., Nathanson, A., Donnerstein,

E., and Lingsweiler, R. (1999). The Choices and Consequences evaluation: A study

of Court T V’s anti-violence curriculum. Santa Barbara : University of California,

Santa Barbara, Center for Communication and Social Policy

Wright, J.C., Huston, A.C., and Kotler, J. (2001). Chapter 6, The early window project:

Sesame Street prepares children for school. In S.M. Fisch and R.T. Truglio (Eds.),

“G” is for growing: Thirty years of research on children and Sesame Street ( p p. 97–

114). Hillsdale, NJ: Erlbaum.

Yates, Bradford L. "Modeling Strategies for Prosocial Television: A Review". Paper

presented to the Open Paper Competition, AEJMC Southeast Colloquium;

Lexington, KY, 4–6 March 1999.

Zhang, D., Zhou, L., Briggs, R., &Nunamaker, J. (2006). Instructional video in e-

learning: Assessing the impact of interactive video on learning effectives.

Information & Management, 43, 15-27.

http://www.coe.csusb.edu/faculty/newberry/classes/archive/fall2008

http://www.associatedcontent.com/shared.shtml?content_type=article&content_type_id=5630635

http://www.associatedcontent.com/shared.shtml?content_type=article&content_type_id=5630635

http://www2.napier.ac.uk/transform/TESEP_3E_Approach.pdf

http://www.thirteen.org/edonline/ntti/formanagers/02Media.pdf

http://www.westga.edu/~byates/prosocia.htm

http://en.wikipedia.org/wiki/Association_for_Education_in_Journalism_and_Mass_Communication

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