Name: The Effectiveness of Adult Community Learning Program Implementation in Seremban, Negeri Sembilan, Malaysia
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Author: Aminuddin Adnan
ISBN: 9783389190753

Excerpt

ACKNOWLEDGEMENT

LIST OF TABLES

LIST OF FIGURES

ABBREVIATIONS

CHAPTER 1.0 INTRODUCTION
1.1 Background of Study
1.2 Research Issue
1.3 Statement of Problem
1.4 Research Objectives
1.5 Research Questions
1.6 Scope of the Study
1.7 Significance of the Study
1.8 Definition of Terms
1.8.1 Product Dimension Construct
1.8.2 Student Satisfaction Construct
1.8.3 Learning Achievement Construct
1.8.4 Context Dimension Construct
1.8.5 Input Dimension Construct
1.8.6 Process Dimension Construct
1.9 Summary

CHAPTER 2.0 LITERATURE REVIEW
2.1 Introduction
2.2 Research Gap
2.3 Curriculum Implementation Evaluation Model
2.4 Walberg Education Productivity Theory
2.5 Goal-Free Evaluation Model
2.6 Kirkpatrick’s Four Levels Evaluation Model
2.7 Matching CIPP Model and Walberg Theory
2.8 Conceptual Frameworks Development
2.9 Research Constructs
2.9.1 Context Dimension Construct
2.9.2 Input Dimension Construct
2.9.3 Process Dimension Construct
2.9.4 Product Dimension Construct
2.9.5 Learning Achievement Construct
2.9.6 Student Satisfaction Construct
2.10 Hypotheses Development
2.11 Summary

CHAPTER 3.0 RESEARCH METHODOLOGY
3.1 Introduction
3.2 Research Philosophy
3.3 Research Design
3.4 Population and Unit Analysis
3.5 Data Collection Method
3.6 Data Collection Protocol and Period
3.7 Sample Size
3.8 Sampling Technique
3.9 Research Instrument
3.10 Pre-testing Procedure
3.11 Pilot Test
3.12 Method of Data Analysis
3.13 Summary

CHAPTER 4.0 DATA ANALYSIS AND FINDINGS
4.1 Introduction
4.2 Data Profile
4.3 Respondent Demography
4.4 RO1 - To Determine Effectiveness Level of Learning Program
4.5 RO2 - To Study the Effect of Context, Input and Process Towards Product Dimension Constructs
4.6 RO3 - To Assess the Strength of Context, Input and Process Affecting Product Dimension Constructs
4.7 RO4 - To Assess the Strength of Context, Input And Process Affecting Learning Achievement Constructs
4.8 RO5 - To Assess the Strength of Context, Input, Process and Learning Achievement Affecting Student Satisfaction Constructs
4.9 RO6 - To Analyze Mediating Effect of Learning Achievement Between Context, Input, Process Dimensions and Student Satisfaction Constructs
4.10 R07 - To Compare the Difference in Effectiveness on Learning Achievement and Student Satisfaction Between Students Completed Learning for 30 Chapters and Not Completed
4.11 Summary

CHAPTER 5.0 SUMMARY, RECOMMENDATION AND CONCLUSION
5.1 Introduction
5.2 Research Summary
5.3 Research Recommendation
5.3.1 Recommendation for RO1 - Minimize Score Card Gap
5.3.2 Recommendation for RO2 & RO3
5.3.3 Recommendation for RO4 - Increase Learning Achievement
5.3.4 Recommendation for RO5 - Increase Student Satisfaction
5.3.5 Recommendation for RO7 - State Religious Authority Issues Guideline That Adult Learners Should Learn Recitation Complete 30 Chapters
5.4 Research Contribution
5.5 Research Implication
5.5.1 Theoretical Implication - Extended of CIPP Model
5.5.2 Policy Implication - Supported Recitation Learning for 30 Chapters Launched by MOE in 2004
5.5.3 Practical Implication
5.6 Limitation of Study
5.7 Future Study
5.8 Conclusion

REFERENCES

APPENDICES

Appendix

Appendix A MOE circular dated 2004 on launching of Quranic recitation program in schools

Appendix B Example of factors with strong effect size

Appendix C WhatsApp communication to learning providers

Appendix D Estimation of average student per learning center and sample size

Appendix E Pre-test feedbacks of the instrument

Appendix F Final copy of instrument in Malay language

LIST OF TABLES

Table 2.1 Previous studies for research gap development

Table 2.2 Components under CIPP dimensions

Table 2.3 Mean scores for CIPP components

Table 2.4 R2 of learning outcome from various factors

Table 3.1 Results of pilot test

Table 3.2 CFA & SEM analysis criteria

Table 4.1 Respondents gender

Table 4.2 Respondents age group

Table 4.3 Programs attended

Table 4.4 Learning providers in Seremban

Table 4.5 Profile of respondents completed learning for 30 chapters

Table 4.6 Implementation score card for context dimension

Table 4.7 Implementation score card for input dimension

Table 4.8 Implementation score card for process dimension

Table 4.9 Implementation score card for product dimension

Table 4.10 CIPP construct items factor loading value

Table 4.11 CFA results for CIPP model

Table 4.12 Discriminant validity test for CIPP model

Table 4.13 Structural model test criteria for CIPP model

Table 4.14 Structural model test results for CIPP model

Table 4.15 Hypotheses results for CIPP model

Table 4.16 Extended CIPP model construct items factor loading value

Table 4.17 CFA results for extended CIPP model

Table 4.18 Discriminant validity test for extended CIPP model

Table 4.19 Structural model test criteria for extended CIPP model

Table 4.20 Structural model test results for extended CIPP model

Table 4.21 Path analysis for indirect relationship

Table 4.22 Independent t test for learning achievement

Table 4.23 Independent t test for student satisfaction

LIST OF FIGURES

Figure 1.1 Profile of poor recitation performers

Figure 2.1 Mean scores CIPP analysis

Figure 2.2 CIPP evaluation model

Figure 2.3 Causal relationship among factors under Walberg theory

Figure 2.4 Framework to choose strong factors

Figure 2.5 Matching CIPP components with Walberg factors

Figure 2.6 Stufflebeam CIPP theoretical model

Figure 2.7 Walberg education productivity theoretical framework

Figure 2.8 Conceptual framework for CIPP model

Figure 2.9 Conceptual framework for CIPP model showing learning achievement & student satisfaction

Figure 4.1 Profile of data after manual cleaning exercise

Figure 4.2 Summary for implementation score card of all CIPP dimensions

Figure 4.3 PLS-SEM for CIPP model

Figure 4.4 PLS-SEM for extended CIPP model

Figure 4.5 Alternative PLS-SEM for extended CIPP model

Figure 5.1 Indicators gap statistics

Figure 5.2 Diagram for criteria 2,4 & 5 recitation criteria

Figure 5.3 Improvement on learning achievement

Figure 5.4 Predicting factor for student satisfaction

Figure 5.5 Causal relationships among CIPP model constructs

Figure 5.6 Model to predict learning achievement & student satisfaction

ABBREVIATIONS

AMOS Analysis of Moment Structures

AVE Average variance extracted

CB-SEM Covariance-Based Structural Equation Modelling

CFA Confirmatory factor analysis

CIPP Context, input, process and product

EFA Exploratory factor analysis

H Hypothesis

IBM SPSS V31 Statistical Package for the Social Sciences

JQAF Jawi, Quran, Arabic and Fardu ain

KMO Kaiser-Meyer-Olkin

MOE Ministry of Education, Malaysia

PLS-SEM Partial least square - structural equation modelling

RG Research gap

RO Research objective

RQ Research question

TVE Total variance explained

UniSZA Universiti Sultan Zainal Abidin

VIF Variance inflation factor

Abstract

Research was carried out to evaluate the effectiveness of adult community learning program implementation in Seremban focusing on Quranic recitation program. The research problem is about difficulties faced by learners to know the effectiveness of learning program for adult to choose. Based on the problem, seven research objectives (ROs) had been developed consists of fifteen hypotheses (Hs). Hypotheses support two conceptual frameworks developed using Stufflebeam CIPP model, Walberg education productivity theory and student satisfaction concept. Research adopted exploratory research design. Sample of 163 data were collected using google form questionnaires and analyzed using quantitative method. Both conceptual frameworks had met measurement model and structural model tests of PLS-SEM. RO1 was analyzed using mean score of IBM SPSS V31 and factor loadings of SMART PLS4. RO2, RO3, RO4 and RO5 were analyzed using path analysis (beta value, VIF, t value and p value) of SMART PLS4. RO3, RO4 and RO5 were subsequently analyzed using R[2] of SMART PLS4. RO6 was analyzed using path analysis of indirect effect. RO7 was analyzed using t test of IBM SPSS V31 software. RO1 found average implementation score card gap of 44 % between expected and actual implementation of 42 item indicators under CIPP model. RO2 found significant causal relationship between context and process dimension constructs toward product dimension construct within CIPP model with moderate beta value (0.431 & 0.333) respectively. RO3, RO4 and RO5 found model fit (R2 value) which become predictors for learning achievement (moderate 43.4 %), product dimension (substantial 68.3 %) and student satisfaction (substantial 71.3 %) constructs respectively. RO6 found that there was no significant mediating effect between context, input and process dimension constructs towards student satisfaction construct through learning achievement construct. RO7 found significant different (t test equal variance assumed = 0.012) between students completed recitation learning program for 30 chapters and not completed. RO2 finding established causal relationship between context and process dimensions towards product dimension constructs under CIPP model. RO3, RO4 and RO5 findings established model fit for conceptual frameworks to predict learning outcomes with moderate to substantial accuracy. RO7 finding supported initiative by MOE in launching recitation program until completion of 30 chapters under JQAF curriculum in 2004. RO1 finding established implementation score card which identifies specific item indicator gaps under CIPP model checklist for program improvement. RO4 and RO5 findings detailed out which item indicators should be prioritized in order to optimize the effect on learning achievement and student satisfaction for program improvement. This study has limitation where individual learning providers represented large percentage (68 %) of respondents compared to others. Therefore, findings of this study reflected more towards effectiveness of learning from individual learning providers compared to others. It is recommended to carry out study on larger population such as Al Baghdadi Center, Azdan Center and Rumah Ngaji Center separately at national level to make comparison for program effectiveness. As conclusion the findings could provide empirical guidance for learners to assess recitation learning program using empirically tested evaluation model. This guidance could be shared with Muslim community by religious state authority (State Mufti) through its regular social media engagement.

Acknowledgement

I would like to express my sincere thanks to all the people who have supported and guided me towards completion of this executive doctorate project. My deepest gratitude to my supervisor, Professor Dr Asif Mahbub Karim for his guidance, invaluable advice and support throughout this project.

My sincere thanks to all Universal Business Academy team members headed by Dr Shirley for being enabler to the accomplishment of this journey. I also wish to thank my fellow course mates for their moral support and enriching discussions during online class especially.

I would like to express my gratitude to my family especially my spouse and children. Your belief in me kept my spirit and motivation high during this tough journey.

Finally, I thank everyone who directly or indirectly contributed to the completion of this project. Your help and support have been invaluable.

CHAPTER 1.0 INTRODUCTION

1.1 Background of Study

The research is to study the effectiveness of Quranic recitation program among adult learners in Seremban. Adult learners are learners between those who have completed secondary schools until learners at the age of 70.

Effectiveness is selected as research issue because many learning providers had claimed that their centres offered effective learning. While their claim was consistent with previous studies which found that the satisfaction level of adult programs was high, it contradicted with other previous studies on recitation performance which found that adult learners represented highest percentage among low performers.

Having identified research issue, research problem is related to difficulties of learners to know which learning programs are effective especially those advertised on internet. New research carried out to study how to measure effectiveness in term of student satisfaction and learning achievement quantitatively.

There was previous study to assess program effectiveness using curriculum evaluation model (CIPP model). However, this model had limitations in performing causal relationship analysis beside the model itself does not analyse learning achievement and student satisfaction under separate variable.

Two new conceptual frameworks were developed by adopting Walberg education productivity theory and student satisfaction concept on top of CIPP model.

Questionnaires had been distributed to Al Baghdadi, Rumah Ngaji, Azdan learning centres and individual learning providers within Seremban. Data were analysed using partial least square of structural equation modelling (PLS-SEM) method.

Main findings from this study are descriptive analysis of learning program effectiveness using CIPP model, causal relationship between variables under CIPP model, R square of CIPP product dimension, learning achievement and student satisfaction. The findings are expected to contribute new knowledge in research relating to causal relationship among CIPP model constructs as well as to improve adult recitation program.

1.2 Research Issue

Learning program advertisements on social media appeared very promising for example advertisements by (kaedahharaki, 2025; Al-Hira, 2025; DigyVersity, 2025) using phrases such as “ Able to recite Quran within 7 hours? Join our live class”, “Learn Quranic recitation within 24 hours ” and “ Learn basic recitation within 3 hours ”.

The programs offered were claimed very effective. It was consistent with previous qualitative studies in evaluating adult recitation program. Study found that effectiveness of learning program in term of learning achievement and student satisfaction was generally high (Zulkifli, n.d.; Sharifah et al., 2018).

However, the above qualitative finding contradicted with statistic of poor recitation performers in Quranic recitation from various studies as shown in Figure 1.1 below.

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Figure 1.1 - Profile of poor recitation performers

Source: (Azarudin et al., 2011; Mohd et al., 2018; Zarif et al., 2014; Nor and Nurul, 2018)

Qualitative findings by ((Zulkifli, n.d.; Sharifah et al., 2018) contradicted with the profile of poor recitation proficiency statistics in studies by (Azarudin et al., 2011; Mohd et al., 2018; Zarif et al., 2014; Nor and Nurul, 2018). Statistics showed that poor recitation proficiency among adult students represented highest percentage (69 %) compared with primary school students (61 %) and Islamic school students (60 %).

1.3 Statement of Problem

Research problem is related to difficulties of learners to know which recitation learning programs are effective especially the programs advertised on internet. According to Karim and Hazmi (2025), Muslim learners faced difficulties to verify the accuracy of learning information on internet.

Therefore, this research is carried out to measure effectiveness in term of learning achievement and student satisfaction quantitatively. The rational is previous studies for adult learning by (Zulkifli, n.d.; Sharifah et al., 2018) adopted qualitative analysis in measuring learning achievement and student satisfaction thus lacking in empirical finding to measure program effectiveness.

Measuring effectiveness with quantitative indicators had been carried out by Sapie et al. (2018) to evaluate effectiveness of recitation program using CIPP curriculum evaluation model in government schools. Despite quantitative analysis, the study lacked in causal relationships analysis among variables within CIPP model particularly causal relationship towards learning achievement and student satisfaction.

Among effectiveness indicators which had been used in recitation learning studies were correlation (r) and R square (R[2]). (Norhisham et al., 2019; Azmil et al., 2014) had studied to measure correlation between Quranic related learning programs and learning achievement. Studies found r value were (r = 0.514 & r = 0.486) respectively. According to Kumar, Talib & Ramayah (2013), correlation value between (0.31 - 0.60) was categorised as moderate relationship.

Awang (2015) described that causal relationship is more suitable for relationship between learning program towards outcome of learning program because outcome of learning occurs after the process of learning takes place. Therefore, R[2] is more suitable as effectiveness quantitative indicator compared to r value. Previous studies which had been using R2 were by (Surendran & Norazlina, 2019; Hanita & Norzaini, 2018) which found R2 (78.1 % & 12.6 %) respectively.

According to Awang et al. (2018) (R2 > 50%) is deemed to have strong causal relationship. However, studies using R2 above did not adopt evaluation models in evaluating effectiveness of curriculum implementation.

1.4 Research Objectives

Research objectives (RO) are listed below.

RO1 - To determine effectiveness level of learning program implementation in term of mean score and factor loading for items under context, input, process and product dimension constructs.

RO2 - To study the effect of context, input and process towards product dimension constructs.

RO3 - To assess the strength of context, input and process affecting product dimension constructs with R[2] value as effectiveness indicator for learning outcome.

RO4 - To assess the strength of context, input and process dimensions affecting learning achievement constructs with R[2] value as effectiveness indicator for learning achievement.

RO5 - To assess the strength of context, input, process dimensions and learning achievement affecting student satisfaction constructs with R2 value as effectiveness indicator for student satisfaction.

RO6 - To analyse mediating effect of learning achievement between context, input, process dimensions and student satisfaction constructs.

RO7 - To compare the difference in effectiveness on learning achievement and student satisfaction between students who completed learning for 30 chapters and not completed 30 chapters.

1.5 Research Questions

The research questions (RQ) are listed below.

RQ1 - What is the effectiveness level of learning program implementation in term of mean score and factor loadings for context, input, process and product dimension constructs?

RQ2 - What is the effect of context, input and process towards product dimension constructs?

RQ3 - What is the strength of context, input and process affecting product dimension constructs with R[2] value as effectiveness indicator for learning outcome?

RQ4 - What is the strength of context, input and process dimensions affecting learning achievement constructs with R2 value as effectiveness indicator for learning achievement?

RQ5 - What is the strength of context, input, process dimensions and learning achievement affecting student satisfaction constructs with R2 value as effectiveness indicator for student satisfaction?

RQ6 - Is there any mediating effect of learning achievement between the relationship of context, input, process dimensions and student satisfaction constructs?

RQ7 - Is there any different in effectiveness on learning achievement and student satisfaction between students who completed learning for 30 chapters and not completed 30 chapters?

1.6 Scope of the Study

Research scopes and limitations are listed below.

1.6.1 Research focuses on the population of adult learners who learn Quranic recitation program as lifelong learning. Adult learners undergoing Quranic recitation study in university or college are excluded.

1.6.2 Adult Quranic recitation programs are limited to those conducted by private entities or non-government entities within Seremban. These are categorized as community learning centres which provide continuous learning for adult learners. Programs conducted by universities or government institutions including mosques are excluded.

1.6.3 The research adopted quantitative research approach. Therefore, data had been collected and analysed using quantitative analysis only.

1.7 Significance of the Study

The followings are the significance of the findings.

1.7.1 Research findings are expected to contribute knowledge on adult Quranic recitation program improvement for learning providers.

1.7.2 Research findings are expected to contribute new knowledge in educational research for future researchers to conduct research in curriculum implementation evaluation effectiveness.

1.8 Definition of Terms

Definition, function and relationship of constructs used in the conceptual frameworks are explained below.

1.8.1 Product Dimension Construct

Product dimension construct in this research is an endogenous construct for context, input and process dimension constructs. The function is to evaluate learning outcome of curriculum implementation. It covers cognitive and affective learning outcomes (Walberg, 1984).

Under CIPP model, it facilitates curriculum implementation evaluation from the perspective of context, input and process dimensions (Stufflebeam, 1971, 2003). However, this model does not specify causal relationship among constructs because CIPP model is a descriptive evaluation tool.

The expected causal relationship of context, input and process towards product dimension constructs are developed based on Walberg theory of education productivity (Walberg, 1984).

1.8.2 Student Satisfaction Construct

Student satisfaction construct in this research is an endogenous construct for context dimension, input dimension, process dimension and learning achievement constructs to evaluate learning outcome from affective perspective (Walberg, 1984). Under CIPP model, it is a component of product dimension together with another component namely learning achievement (Sapie et al, 2018).

Student satisfaction is derived from comparing actual experience in learning with expectation prior to learning taken place as found in previous study that e-learning program affected student satisfaction (Calli et al., 2013). The rational to split the construct as endogenous construct for learning achievement construct from originally being component of product dimension construct is based on description of causal relation in learning by (Awang, 2015).

According to Awang (2015) causal relationship is suitable in learning program because activity of learning must occur first before learning outcome occurs. Based on this argument, student satisfaction occurs after the learning process takes place. Therefore, student satisfaction becomes endogenous for learning achievement.

1.8.3 Learning Achievement Construct

Learning achievement construct in this research is an endogenous construct for context, input and process dimension constructs. Beside this, the construct is also an exogenous construct for student satisfaction construct.

Learning achievement construct is to measure learning outcome of learning under cognitive perspective (Walberg, 1984). Under CIPP model, learning achievement is one of the components under product dimension together with another component namely student satisfaction (Sapie et al., 2018).

The rational for using causal relationship for relationship of context, input and process dimensions constructs towards learning achievement construct is based on (Awang, 2015) which describe that learning process must occur before learning outcome occurs. Subsequently, the rational for learning achievement construct becomes exogenous construct for student satisfaction construct is based on (Calli et al., 2013) which found that e-learning program affected student satisfaction.

Having discussed the above, this construct will potentially function as mediator between relationship of context, input and process dimensions towards student satisfaction constructs.

1.8.4 Context Dimension Construct

Context dimension construct in this research is an exogenous construct for product dimension, learning achievement and student satisfaction constructs as shown in two conceptual frameworks.

The function of this construct is to facilitate evaluation on learning program objectives based on CIPP model.

Causal relationship of this construct towards product dimension, learning achievement and student satisfaction constructs is based on (Awang, 2015; Walberg, 1984).

1.8.5 Input Dimension Construct

Input dimension construct in this research is an exogenous construct for product dimension, learning achievement and student satisfaction constructs as shown in two conceptual frameworks.

The function of this construct is to facilitate evaluation on learning program planning such as resources used in learning program based on CIPP model.

Causal relationship of this construct towards product dimension, learning achievement and student satisfaction constructs is based on (Awang, 2015; Walberg, 1984).

1.8.6 Process Dimension Construct

Process dimension construct in this research is an exogenous construct for product dimension, learning achievement and student satisfaction constructs as shown in two conceptual frameworks.

The function of this construct is to facilitate evaluation on actual program implementation such as teaching strategy used in learning program based on CIPP model.

Causal relationship of this construct towards product dimension, learning achievement and student satisfaction constructs is based on (Awang, 2015; Walberg, 1984).

1.9 Summary

Research issue is the effectiveness of adult Quranic recitation learning program. Previous studies used learning achievement and student satisfaction as learning outcome. Research problem is difficulties of learners to know which programs are effective. Seven research objectives had been identified.

CHAPTER 2.0 LITERATURE REVIEW

2.1 Introduction

This chapter identified the detail of research gaps, underpinning theories, conceptual frameworks, relevant constructs and hypotheses development.

2.2 Research Gap

This section elaborated in detail the development of research gap based on previous researches. Table 2.1 below summarises previous studies for the development of research gaps.

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Table 2.1 - Previous studies for research gap development Source: As stated under previous research column

Studies to evaluate effectiveness of recitation learning program in Table 2.1 row No. 1 had been carried out without using curriculum evaluation model. Studies by (Sharifah et al., 2018; Zulkifli, n. d.) used qualitative method in evaluation of program effectiveness. Studies by (Azarudin et al., 2011; Mohd et al., 2018; Surul & Muhammad, 2013) used quantitative method to evaluate program effectiveness but lack in using curriculum evaluation model.

Curriculum evaluation model is important because the model specifies scope and variables to be used. This is the first research gap (RG1) which become RO1 when compared with study by (Sapie et al., 2018) which adopted CIPP model with four variables namely context, input, process and product dimension variables. However, CIPP model does not specify causal relationship among variables.

Table 2.1 row No. 2 listed study by Sapie et al. (2018) which evaluated effectiveness of recitation program using CIPP model (Stufflebeam, 1971). Result showed that mean score for the level of program implementation under context, input, process and product dimensions were (2.86, 3.33, 2.88 and 3.52 or 71.5 %, 83.25 %, 72 % and 88 %) respectively. Figure 2.1 below summarises overall result.

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Figure 2.1- Mean scores CIPP analysis Source: Sapie et al. (2018)

Sapie et al. (2018) used quantitative and descriptive analysis with CIPP model as underpinning theory. However, recommendation by Sapie et al. (2018) had been made beyond CIPP model scope which recommended that context and process dimensions should be improved in order to improve product dimension.

Based on (Awang, 2015) the above statement is categorised as causal relationship where outcome of one event is caused by a prior event. Research by Sapie et al. (2018) was appropriate in using CIPP model but causal relationship in its recommendation was made beyond the scope of CIPP model.

A theory which guides causal relationship among constructs in educational research should be adopted in new research. This is second research gap (RG2) which become RO2 to RO5 when compared with studies by (Hanita & Norzaini, 2018) which used Walberg theory of education productivity. This theory specified causal relationship among variables therefore, R[2] value could be analysed.

Table 2.1 row No. 3 listed previous studies by (Surendran & Norazlina, 2019; Hanita & Norzaini, 2018) which evaluated learning programs using causal relationship analysis with R2 as effectiveness learning outcome indicator. Surendran & Norazlina (2019) which evaluated non-Quranic learning program using CIPP model and causal relationship found context and input dimensions affected product dimension constructs significantly with R2 (78.1 %).

However, study by (Surendran and Norazlina, 2019) was lacking in using causal relationship theory such as Walberg theory to guide causal relationship among constructs within CIPP model. R[2] refers to percentage of dependent variable explained by independent variables (Awang et al., 2018).

Research by (Hanita & Norzaini, 2018) on the other hand had been carried out using Walberg theory which found R2 (12.6 %) but without using CIPP model. Therefore, it could be the explanation why finding on R2 was far below 50 %. Walberg theory is theory which guides causal relationship among variables in evaluating educational performance (Walberg, 1984).

Based on studies by (Surendran & Norazlina, 2019; Hanita & Norzaini, 2028), research gap number two (RG2) should be resolved by adopting Walberg theory to define causal relationship of CIPP model variables and use R2 as indicators for dependent variables namely product dimension, learning achievement and student satisfaction variables. RO2 to RO5 were formulated based on this gap.

Table 2.1 row No.4 listed literatures which described the rational to treat learning achievement as mediator between relationship of context, input and process dimension variables towards student satisfaction variable. Literatures by (Awang, 2015; Calli et al., 2013) had been referred.

Learning achievement and student satisfaction originally a component of product dimension variable. In order to measure student satisfaction as a variable or construct, learning achievement becomes exogenous construct towards student satisfaction together with context, input and process dimension constructs. The relationships will be shown in two conceptual frameworks at the end of this chapter. Mediating factor is the third research gap (RG3) which become RO6.

Previous researches in row No.1 to No. 4 which studied recitation program were lack in analysing the difference in performance between student who completed learning for 30 chapters and not completed. Reason for this question was because recitation learning program module had been officially launched at government schools in 2004 by the Ministry of Education (MOE, 2004) which emphasised on learning until complete 30 chapters (Table 2.1 row No. 5). Copy of the letter in Malay language is shown in Appendix A.

It became evident that recitation program was not merely a traditional program among Muslim community but it was also part of government schools’ curriculum for Muslim students. Decision to introduce the curriculum was because the government expected that students who complete learning for 30 chapters would be able to recite Quran fluently.

This expectation could be applied in adult community learning program as well. It is the fourth research gap (RG4) and become RO7.

2.3 Curriculum Implementation Evaluation Model

Curriculum implementation evaluation model (CIPP model) is a comprehensive framework to guide evaluation of program, project, human resource, product and system (Stufflebeam, 2003). It was found by Daniel L. Stufflebeam in 1960’s (Stufflebeam, 1971, 2003; Stufflebeam & Coryn, 2014). The model is displayed in Figure 2.2 below.

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Figure 2.2 - CIPP evaluation model Source: Stufflebeam (2003)

It has four components namely context, input, process and product dimensions. The model could explain interrelation between core values and recitation program. Core values in new research are recitation fluency and student satisfaction. It links with adult recitation program from four domains namely the program objectives under context dimension, program plan under input dimension, action or actual implementation of the program under process dimension and finally learning program outcome under product dimension.

Context dimension is used to ensure the objective of recitation learning program fulfil the need of community with identified problem. The problem in this research is related to recitation fluency. Input dimension is used to plan out strategy such as resources, procedure and selection of best process.

Process dimension is used to detail out the actual learning activities and compare against plan such as teaching strategy and learning assessment. Product dimension is used to evaluate the learning outcome and compare with learning objective (Stufflebeam, 1971, 2003). Learning outcomes in new research are learning achievement and student satisfaction. These are components of product dimension construct.

Historically CIPP model was used to evaluate school program in United States of America. Subsequently it had been used for other sectors such as social, health, business and military (Stufflebeam, 2003). The evaluation process covered collection of data, analysis and provide information for decision making (Stufflebeam, 1971). It was also to evaluate whether the learning objective has been achieved according to plan (Tyler, 1949).

According to Stufflebeam (1971) CIPP model prior to 1971 was used to assist decision making in the implementation of learning improvement as proactive concept. In other word it was used to decide whether program improvement should be implemented or not implemented. However, after 1971 CIPP could be used to evaluate post implementation of learning program with retroactive concept for program implementation accountability and program improvement.

CIPP retrospective function justified for this research to adopt the model because adult Quranic recitation learning program had been implemented earlier. This research is carried out to evaluate effectiveness of program for improvement.

The application of CIPP model in recitation learning was found in the study by Sapie et al. (2018). Components for each dimension are shown in Table 2.2 below. Context dimension has components of learning program, objective and timeframe. Input dimension has components of teacher skill, infrastructure and curriculum. Process dimension has components of student assessment, teaching strategy and mentoring. Product dimension has components of learning achievement and student satisfaction.

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Table 2.2 - Components under CIPP dimensions Source: Sapie et al. (2018)

Rational for choosing CIPP model is because CIPP model could be used to evaluate program retrospectively and provide checklist evaluation tool (Stufflebeam, 2003). The checklist tool will be adopted to answer RO1 which later referred as score card. Constructs of learning achievement and student satisfaction in new research were adopted from CIPP model in previous study by (Sapie et al., 2018) under product dimension construct.

2.4 Walberg Education Productivity Theory

Walberg (1980) was the first causal relationship framework by him to evaluate productivity of education achievement with seven factors namely student ability and motivation, instructional quantity and quality, home and classroom environments, and age. In 1980 the model was without curriculum or program as predicting factor. The theory listed down factors affecting student learning in term of cognitive, affective and behaviour.

In 1984, Walberg published his improved model with nine factors compared with seven factors in 1980 (Walberg, 1984). Nine factors are student ability, development, motivation, instruction quantity, quality, home influence, classroom, peer and television. Until 1984 curriculum factor was still not considered as important factor though there were two more factors being introduced.

Development of the theory in various stages over decades could be observed through various literatures. It started with Walberg (1984) in his research to improve the productivity of America’s schools. Second development was by (McGrew, 2008). The third was through synthesis of over 800 meta-analyses relating to achievement by (Hattie, 2009) and finally through literature on system-based synthesis of research related to improving students’ academic performance by (Huitt et al., 2009).

Walberg (1984) described nine factors which influence student learning in term of affective, behavioural and cognitive. The causal relationships of factors which influence affective, behavioural and cognitive learning are shown in Figure 2.3 below.

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Figure 2.3 - Causal relationship among factors under Walberg theory Source: Walberg (1984)

The first factor is student aptitude which consist of ability, development and motivation. Second factor is instruction which consist of amount of time spent and quality of instruction. Third factor is environment which consist of home, classroom, peer and mass media. Learning outcome is measured in term of affective, behavioural and cognitive. There is causal relationship between aptitude, instruction and environment factors towards learning outcome and the existent of correlation between aptitude, instruction and environment factors.

McGrew (2008) had discussed the development of Walberg theory from 1981 to 2002. Related literatures had assisted new research in term of selecting variable under category of curriculum design, delivery and instruction. Instruction variable became important factor as compared with early 1980s. Curriculum design which was not discussed in detail during early development of the theory (Walberg, 1984) had subsequently been included as important factor for instruction variable under component of curriculum delivery and design.

Hattie (2009) who was inspired by Walberg theory had conducted more than 800 meta-analyses on learning achievement. The above syntheses enabled to formulate predicting factors called effect size (d). Effect size of one unit means the predicting factor could affect learning outcome with one unit of standard deviation. The example of predicting factors with effect size are shown in Appendix B.

The list of factors with effect size by Hattie (2009) is too lengthy for researcher to choose. Huitt et al. (2009) had developed framework to guide researcher to choose predicting factors with effect size more than 0.4 which is considered strong predictor. There were 66 factors being shortlisted from

138 factors and included in the framework. The framework is shown in Figure 2.4 below.

For new research, component of CIPP dimensions in Sapie et al. (2018) had been matched against variables in framework by (Huitt et al., 2009) under curriculum factor of school level context variables, teacher and student characteristics under classroom variables, teaching strategy and student assessment under classroom process variable, and learning outcomes. The itemised matching will be shown in Chapter 2.7.

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Figure 2.4 - Framework to choose strong factors Source: Huitt et al. (2009)

The different between Walberg theory and CIPP model had been clearly deliberated specifically in term of causal relationship among variables. Walberg theory in Figure 2.3 clearly displays causal relationships among variables in its model as opposed to CIPP model in Figure 2.2 which does not display causal relationship among variables.

Rational for choosing Walberg theory is because it allows empirical analysis for causal relationship among variables. It had been used in research related to learning in schools due to three advantages (Walberg, 1984). Firstly, parsimonious which means that the theory could predict learning outcome using minimal predictor. Secondly, replication means same model could produce almost same result and thirdly, generalisation means the model could produce same result for larger sample at national and international level.

2.5 Goal-Free Evaluation Model

It is a program evaluation developed by Michael Scriven in 1973 (Stufflebeam & Shinkfield, 1985). The approach of program evaluation was by ignoring the goals of the program and focus on all possibles outcomes whether intended or unintended. The rational of this concept is because the model intends to evaluate what actually happen rather than what is expected to happen.

This model is suitable to be used for program evaluation which the objective of the program is unclear or for program which has comprehensive effect on possible outcomes. Therefore, the model was not adopted in new research which has clear and identified objectives.

2.6 Kirkpatrick’s Four Levels Evaluation Model

This evaluation model was developed by Kirkpatrick in 1959 (Kirkpatrick, 1994). It has four levels starting with student reaction such as whether student find that the program is relevant. Second level is whether student acquire the knowledge being taught. Third level is whether student capable to apply the knowledge acquired. Finally, whether the knowledge being applied can make contribution to organization.

In summary the model evaluates student reaction, learning, behaviour and result with increasing degree of important from level one to level four. Therefore, this model was not adopted in new research because new research evaluated program based on four dimensions with equal important.

2.7 Matching CIPP Model and Walberg Theory

Constructs or variables in new research are developed from combination of CIPP components in Sapie et al. (2018) and Walberg theory as shown in

Figure 2.5 below. Context dimension construct consists of school recitation program and program objective matched with curriculum and motivation factors under Walberg theory.

Input dimension construct consists of teacher skill and knowledge matched with teacher quality factor under Walberg theory. Student input wasn’t used in Sapie et al. (2018) but included in new research by adopting student engagement factor under Walberg theory. Process dimension construct consists of teaching strategy and student assessment matched with teaching strategy and student feedback factors under Walberg theory.

Product dimension construct consists of student performance or learning achievement and student satisfaction which matched with cognitive learning and affective learning factors under Walberg theory.

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Figure 2.5 - Matching CIPP components with Walberg factors

Source: (Sapie et al., 2018; Huitt et al., 2009)

The matched constructs or factors in Figure 2.5 met minimum criteria of strong factors affecting learning by (Huitt et al., 2009). Factors which strongly affect learning with value (d > 0.4) had been selected for the development of two conceptual frameworks. The factors become variables or construct in new research.

2.8 Conceptual Frameworks Development

Development of conceptual frameworks was based on Stufflebeam CIPP theoretical framework in Figure 2.6 and Walberg education productivity theoretical framework in Figure 2.7 below.

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Figure 2.6 - Stufflebeam CIPP theoretical model Source: Stufflebeam (1971)

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New research adopted two conceptual frameworks below (Figure 2.8 & Figure 2.9) developed from two theoretical frameworks above (Figure 2.6 & 2.7).

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Figure 2.8- Conceptual framework for CIPP model Source: (Stufflebeam, 1971; Walberg, 1984)

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Figure 2.9 - Conceptual framework for CIPP model showing learning achievement & student satisfaction

Source: (Stufflebeam, 1971; Walberg, 1984; Awang, 2015; Calli et al.,

2013)

The first conceptual framework (Figure 2.8) is to explore causal relationship among CIPP model constructs. Second conceptual framework (Figure 2.9) is an extended CIPP model to explore causal relationship of CIPP model constructs towards learning achievement and student satisfaction separately.

First conceptual framework consists of context, input and process dimensions representing independent variables or exogenous constructs. Product dimension represents dependent variable or endogenous construct. This framework tests three hypotheses namely H1, H2 and H3.

Second conceptual framework consists of context, input and process dimensions represents exogenous constructs for learning achievement and student satisfaction constructs. Learning achievement is endogenous construct for context, input and process dimension constructs. Learning achievement is also exogenous construct for student satisfaction construct. Student satisfaction construct is also endogenous construct for learning achievement, context, input and process dimension constructs. This conceptual framework tests H4 to H13 hypotheses.

Second conceptual framework has potential mediating factor for learning achievement between construct, input and process dimension constructs and student satisfaction construct. There are three indirect hypotheses to be tested namely H11, H12 and H13.

2.9 Research Constructs

Research variables are referred as constructs in SEM analysis (Awang, 2015). Otherwise refers as variable.

Table 2.3 below shows mean scores for context, input, process and product dimension variables in various studies (Sapie, Barinah, Suhana, Muhamad & Siti, 2018; Sapie, Harzita, Suhana, Muhamad & Siti, 2018; Abdullah et al., 2016) with average mean of 73 %, 76 %, 74 % and 76 %.

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Table 2.3 - Mean scores for CIPP components Source: As listed under authors column

Table 2.4 below shows R[2] of learning achievement from various factors with average value of 42 %. All of the selected factors have strong effect size above 0.4. Empirical statistics such as mean score, effect size and R2 had been used to support causal relationship among constructs towards product dimension, learning achievement and student satisfaction constructs. The statistics were also used for hypotheses development. It would be compared with the actual findings in Chapter 4.

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Table 2.4 - R[2] of learning outcome from various factors Source: As listed under authors column

2.9.1 Context Dimension Construct

Context dimension construct is selected to analyse the effectiveness of recitation learning program under CIPP context dimension supported with average mean score (73 %) as shown in Table 2.3 above.

This construct consists of adult recitation program and program objective components. These components matched with curriculum and motivation factors by (Huitt et al., 2009) with (d = 0.6 & 0.48) respectively. These components affect learning performance with R[2] range from 1 % to 75 % as shown in Table 2.4 under curriculum, goal setting and motivation factors (Row 1,2 & 3).

As summary this construct is expected to evaluate the level of program effectiveness supported by average mean scores of 73 % and causal effect to learning with R2 range from 1 % to 75 %.

2.9.2 Input Dimension Construct

Input dimension construct is selected to analyse the effectiveness of recitation learning program under CIPP input dimension supported with average mean score (76 %) as shown in Table 2.3 above.

This construct consists of student engagement and teacher skill components. These components adopted from Huitt et al. (2009) with (d = 1.09 & 0.44) respectively. These components affect learning performance with R[2] range from 24 % to 83.2 % as shown in Table 2.4 under student engagement and teacher quality factors (Row 4 & 5).

As summary this construct is expected to evaluate the level of program effectiveness supported by average mean scores of 76 % and causal effect to learning with R2 range from 24 % to 83.2 %.

2.9.3 Process Dimension Construct

Process dimension construct is selected to analyse the effectiveness of recitation learning under CIPP process dimension supported with average mean score (74 %) as shown in Table 2.3 above.

This construct consists of teaching strategy and student assessment components. These components adopted from Huitt et al. (2009) with (d = 0.6 & 0.73) respectively. These components affect learning performance with R2 range from 2 % to 74 % as shown in Table 2.4 above under teaching method and feedback (Row 6 & 7).

As summary this construct is expected to evaluate the level of program effectiveness supported by average mean scores of 74 % and causal effect to learning with R[2] range from 2 % to 74 %.

2.9.4 Product Dimension Construct

Product dimension construct is selected to analyse the effectiveness of recitation learning under CIPP product dimension supported with average mean score (76 %) as shown in Table 2.3 above.

This construct consists of learning achievement and student satisfaction. This construct is expected to evaluate the level of program effectiveness supported by average mean score of 76 %. The success of new research could be observed from R2 value of this construct which should be at least equivalent to average R2 value affected by variables in previous researches of 42 % as shown in Table 2.4 above.

2.9.5 Learning Achievement Construct

Learning achievement construct is originally a component of product dimension construct. The mean score of student learning achievement is 90 % or high level of program evaluation using mean score (Sapie et al., 2018). The measurement of the construct was carried out based on oral Quranic recitation test from three indicators namely: (1) suitability of assessment method, (2) appropriateness of assessment frequency and (3) appropriateness of learning frequency per week.

The new research had adopted other assessment criteria namely six criteria to assess Quranic recitation (Surul & Muhammad, 2013). The criteria are: (1) correct letters and correct vowel signs, (2) correct point of articulation and letter characteristics, (3) thick and thin sound of recitation, (4) nasal and without nasal sound, (5) length of vowel sound and (6) start and stop recitation technique. Additional criteria for assessment of recitation complete 30 chapters is special recitation rules under Imam Haf.

The reason for splitting this component from student satisfaction under product dimension construct is to enable separate assessment of learning achievement R[2] affected by exogenous constructs as shown in conceptual framework. Average R2 value from various factors towards learning is 42 % as shown in Table 2.4 above should be the benchmark of finding from this construct.

2.9.6 Student Satisfaction Construct

Student satisfaction construct is originally from product dimension construct. The mean score of student satisfaction is 86 % or high level of program evaluation using mean score (Sapie et al., 2018).

This construct was separated from learning achievement under product dimension construct in order to measure effectiveness indicator in term of student satisfaction R[2] affected by exogenous constructs as shown in conceptual framework. Average R2 value from various factors towards learning is 42 % as shown in Table 2.4 above should be the benchmark of finding from this construct.

2.10 Hypotheses Development

Hypothesis is defined as an expectation on what to happen from data analysis for selected constructs based on theory (Muda et al., 2018). There are 15 hypotheses developed in new research from two conceptual frameworks to answer RQ2 until RQ7 as shown in Figure 2.8 and Figure 2.9 above.

H1 - Context dimension affects product dimension constructs positively and significantly.

It is supported by components of curriculum program and motivation to enrol program with effect size (d = 0.6 & 0.48) respectively. Also supported by factors of curriculum change, goal setting and motivation with average R[2] of 44 % for learning outcome in Table 2.4.

H2 - Input dimension affects product dimension constructs positively and significantly.

It is supported by components of student engagement and teaching quality with effect size (d = 1.09 & 0.44) respectively. Also supported by factors of student engagement and teaching quality with average R2 of 39.8 % for learning outcome in Table 2.4.

H3 - Process dimension affects product dimension constructs positively and significantly.

It is supported by components of teaching strategy and student learning feedback with effect size (d = 0.60 & 0.73) respectively. Also supported by factors of teaching method and student feedback with average R2 of 41 % for learning outcome in Table 2.4.

H4 - Context dimension affects student learning achievement constructs positively and significantly.

H5 - Input dimension affects student learning achievement constructs positively and significantly.

H6 - Process dimension affects student learning achievement constructs positively and significantly.

It is supported by components of teaching strategy and student learning feedback with effect size (d = 0.60 & 0.73) respectively. Also supported by factors of teaching strategy and learning assessment with average R2 of 41 % for learning outcome in Table 2.4.

H7 - Context dimension affects student satisfaction constructs positively and significantly.

H8 - Input dimension affects student satisfaction construct positively and significantly.

H9 - Process dimension affects student satisfaction constructs positively and significantly.

H10 - Learning achievement affects student satisfaction constructs positively and significantly.

It is supported by correlation between learning achievement and student satisfaction with correlation value r = 50.2 % being moderate relationship (Kumar et al., 2013). However, according to Awang et al. (2018) this relationship is more appropriate to use causal relationship because student will feel satisfy after obtaining good learning outcome.

H11 - Context dimension affects student satisfaction through learning achievement constructs positively and significantly.

Literatures by (Awang, 2015; Calli et al., 2013) had been used to describe the rational for using learning achievement as mediator between relationship of context dimension and student satisfaction constructs.

H12 - Input dimension affects student satisfaction through learning achievement constructs positively and significantly.

Literatures by (Awang, 2015; Calli et al., 2013) had been used to describe the rational for using learning achievement as mediator between relationship of input dimension and student satisfaction constructs.

H 13 - Process dimension affects student satisfaction through learning achievement constructs positively and significantly.

Literatures by (Awang, 2015; Calli et al., 2013) had been used to describe the rational for using learning achievement as mediator between relationship of process dimension and student satisfaction constructs.

H14 - There is significant different in learning achievement between students who have completed 30 chapters and not completed 30 chapters.

H15 - There is significant different in student satisfaction between students who have completed 30 chapters and not completed 30 chapters.

MOE (2004) had launched recitation program until completion of 30 chapters in 2004 because the government expected that students who have completed learning for 30 chapters will be able to recite Quran fluently. Students were expected to satisfy with learning program after successfully completed their learning (Calli et al., 2013).

2.11 Summary

This chapter identified six constructs for new research namely context dimension, input dimension, process dimension, product dimension, learning achievement and student satisfaction constructs. CIPP model and Walberg theory are underpinning theories for two conceptual frameworks to test 15 hypotheses.

CHAPTER 3.0 RESEARCH METHODOLOGY

3.1 Introduction

This chapter covers methodology to analyse seven research objectives. It consists of research philosophy, research design, population and unit analysis, data collection method, protocol and period, sample size, sampling technique, research instrument, pre-testing, pilot test and method of data analysis.

3.2 Research Philosophy

This research adopted pragmatist research philosophy. It started with research problem then identifying research objectives where the findings could be recommended for practical improvement in adult community learning (Saunders et al., 2016). Pragmatism focused on exploration of data as opposed to rigid theory confirmation (Creswell, 2010).

3.3 Research Design

New research adopted exploratory research design. Exploratory research design is to explore data to develop theory from the finding (Kumar et al., 2013; Muda et al., 2018). Data collected had been analysed using CIPP model and Walberg theory to explore relationship among CIPP constructs and evaluate the effectiveness of recitation learning program.

3.4 Population and Unit Analysis

Based on Muda et al. (2018), unit analysis of this research is adult recitation learning program in Seremban, Negeri Sembilan, Malaysia. The population of data for analysis is adult students in Seremban.

3.5 Data Collection Method

This research had adopted quantitative method because there are hypotheses to be analysed (Kumar et al., 2013).

3.6 Data Collection Protocol and Period

Data collected by sending google form questionnaires distributed to respondents through WhatsApp group of learners by respective learning operator. The copies of WhatsApp communication are compiled in Appendix C. Survey using questionnaire is one of the strategies in data collection from population (Kumar et al., 2013). Data collected from the period between 1 March 2025 to 30 June 2025.

3.7 Sample Size

Sample size had been estimated using Yamane’s 1967 formula (Adam, 2020). The estimated population size of adult students joining community learning providers were 600 students. The list of learning centres and estimation of average students per centre and sample size are shown in Appendix D.

The population estimation was based on Al Baghdadi face to face students before Covid 19 (471 centres with 127 students per centre) and after Covid 19 (103 centres with average 30 students) which showed reduction by 78 % (Norsyida et al., 2014; Al Baghdadi Group Sdn Bhd, 2023). Therefore, after Covid the average adult students per centre were 30 students. Applying the population to sampling size formula by Yamane, the estimated sample size for new research were 240 respondents.

3.8 Sampling Technique

Sample should be selected using random selection technique to ensure sample unit selected is independent from another sample unit (Kumar et al., 2013). As only 163 respondents recorded in google responses compared with 240 expected sample, all respondents had been considered as sample data for analysis. The adoption of this practice was supported by sample adequacy test which will be discussed in pilot test section.

3.9 Research Instrument

Instrument was adapted from study by (Sapie et al., 2018). The final copy of instrument in Malay language is attached in Appendix F.

The questionnaire consists of nine sections with 42 questions. Section A consists of questions regarding respondent’s profile such as age and respondent background. Section B to section I consist of questions to measure constructs of learning effectiveness using 10 points Likert scale to obtain more independent data (Awang, 2015).

Section B and C consist of questions to measure context dimension constructs. Section D and E consist of questions to measure input dimension constructs. Section F and G consist of questions to measure process dimension construct. Section H consists of questions to measure learning achievement. Section I consists of questions to measure student satisfaction.

3.10 Pre-testing Procedure

Awang et al. (2018) emphasised that instrument had to go through pre-testing stage for content, face and measurement validity. For this research, the instrument had been checked in term of content and language by selected teachers and owners of learning providers in Seremban.

Three copies of instruments had been sent to owners who also teachers of learning providers via WhatsApp messages. Feedbacks of the instrument are shown in Appendix E. After receiving feedback, the questions of the instrument had been increased to 42 from 35 earlier.

3.11 Pilot Test

Awang et al. (2018) described that pilot test be carried out after pre-testing before final instrument being circulated. New research carried out pilot test using 163 data received through google form responses. Data had been analysed using IBM SPSS V31 for exploratory factor analysis (EFA). The summary of the findings is shown in Table 3.1 below.

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Table 3.1 - Results of pilot test

Source: IBM SPSS V31

Cronbach’s Alpha for context, input, process and product dimension constructs achieved minimum internal reliability of 0.7 (Muda et al., 2018) with value (0.881, 0.876, 0.852 & 0.924) respectively.

Items Q4, Q5, Q6, Q7, Q8, Q9 and Q10 of context dimension construct achieved factor loading above 60 %. Items Q12, Q13, Q14, Q15, Q16, Q19 and Q20 of input dimension construct achieved factor loading above 60 %. Items Q24, Q25, Q26 and Q29 of process dimension construct achieved factor loading above 60 %. Items Q30, Q31, Q32, Q33, Q34, Q35, Q37, Q38, Q39 and Q40 achieved factor loading above 60 %. The above items are strong items to measure context, input, process and product dimension constructs (Muda et al., 2018).

Context, input, process and product dimension constructs achieved cumulative total variance explained (TWE) above 60 %. This indicates that each construct is explained by items with value (81 %, 80 %, 71.3 % & 75.2 %) respectively (Muda et al., 2018).

Context, input, process and product dimension constructs achieved great threshold of KMO value above 0.7 with value (0.807, 0.819, 0.739 & 0.831) respectively. High KMO value indicates sampling adequacy (Kumar et al., 2013).

According to Awang et al. (2018) items with factor loading below 0.6 has to be removed from instrument before final circulation to respondents. New research however, retained items below 0.6 because it functioned as evaluation checklist score card as provided in CIPP model (Stufflebeam & Coryn, 2014).

3.12 Method of Data Analysis

After performing IBM SPSS V31 descriptive analysis, PLS-SEM analysis was performed using SMART PLS4 (Ahmad, 2023). First step was measurement model analysis or confirmatory factor analysis (CFA). Second step was structural model (SEM) analysis. These analyses were to answer all research objectives. The analysis procedures are shown in Table 3.2 below.

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Table 3.2 - CFA & SEM analysis criteria Source: Ahmad (2023)

Ahmad (2013) explained that CFA is to test reliability and v alidity of data before hypotheses testing. Reliability refers to how consistent the items of instrument measure a construct and validity refer to how well items of instrument measure a construct (Sekaran & Bougie, 2010; Hair, 2010).

Two reliability tests were performed. First reliability test using Cronbach’s Alpha was performed to test internal consistency of the instrument with minimum threshold of 0.7 (Muda et al., 2018). Second reliability test using composite reliability for each construct was carried out to test how consistent a set of items measure a construct with minimum threshold of 0.7 (Hair et al., 2010).

Two validity tests were performed. Firstly, convergent validity refers to agreement of items measure same construct (Sadikoglu & Zehir, 2010). It consists of three criteria. All items must have factor loadings above 0.708 (Hair, 2014). Factor loading is the strength of relationship between set of items towards a construct. Total variance extracted must be above 0.5 (Hulland, 1999; Barclay et al., 1995). It refers to total variance of items accounted for by a construct. Composite reliability must be above 0.7 (Hair, 2010).

Second validity test is discriminant validity by (Fornell & Larcker, 1981). The purpose is to ensure items of each construct does not overlap in measuring other constructs. This could be achieved by checking that the AVE value of a construct should be greater than squared correlation between constructs. It could be observed from Fornell & Larcker table.

After CFA test for measurement model, SEM was performed to check structural model of conceptual frameworks. The first criteria namely collinearity (VIF) (Diamantopoulos & Siguaw, 2006) to ensure each construct was not highly correlated among each other with maximum value (< 3.3).

Next was checking the significance of model path. Each path or causal relationship must have two tails (t value > 1.96) and (p value < 0.05) (Hair et al., 2022). Beta value (0.10 - 0.30) indicate small effect, value (0.30 - 0.50) indicate moderate effect and value (> 0.50) indicate high effect.

Next step was to check coefficient of determination (R[2]) to assess the extent of variance in endogenous construct explained by exogenous constructs in a conceptual framework (Chin, 1998). Value (0.19 - 0.33) indicate weak effect, value (0.33 - 0.67) indicate moderate effect and value (> 0.67) indicate substantial effect.

Up to this stage, the frameworks were expected to meet minimum requirement for CFA and SEM analyses to answer all research objectives.

3.13 Summary

RO 1 was analysed using mean score from IBM SPSS V31 and factor loadings from SMART PLS4 softwares. RO 2 was analysed using beta value, VIF, t value and p value from SMART PLS4. RO 3, RO 4 and RO 5 were analysed using R2 from SMART PLS4. RO 6 was analysed using path analysis of indirect effect from SMART PLS4. RO 7 was analysed using t test from IBM SPSS V31.

CHAPTER 4.0 DATA ANALYSIS AND FINDINGS

4.1 Introduction

This chapter analysed data demography and analysis for RO1 to RO7. Empirical output from IBM SPSS V31 and SMART PLS4 were attached either directly copied from the software or reconstructed into simpler presentation. Findings for all research objectives had been compared with literatures under literature review chapter and other sources.

4.2 Data Profile

Data from 163 respondents was cleaned up where 57 data containing answers with all 10 Likert scales had been removed (Muda et al., 2018).

Extracted output from IBM SPSS V31 statistics showed that data from respondents recorded better skewness and kurtosis value (-3.025 & 12.072) compared with (-3.572 & 16.973) before manual data cleaning. Negative skewness indicates data were concentrated near to 10 scale of Likert scale.

High kurtosis value indicates that the higher peak of the data distribution was well above the normal distribution peak. According to Kumar et al.

(2013) the findings of data profile (skewness < -1 and kurtosis > 3) showed that data was not within normal distribution parameter (Kumar et al., 2013).

Data profile detail is displayed in Figure 4.1 below. Based on Hair et al. (2022) skewness and kurtosis value within (-2 and +2) still acceptable as normal distribution criteria. Learning achievement construct met the criteria. Process dimension, product dimension and student satisfaction constructs marginally met the criteria. Context and input dimension constructs were far beyond acceptable criteria.

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Figure 4.1 - Profile of data after manual cleaning exercise

Source: IBM SPSS V31

Nevertheless, PLS-SEM was able to carry out statistical analysis with non-normal data. It justified the selection of PLS-SEM for analysis beside the method is suitable for exploratory research (Ahmad, 2023).

4.3 Respondent Demography

The demography of respondents had been extracted from descriptive

analysis using IBM SPSS V31. Table 4.1 shows the gender of respondents.

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Table 4.1 - Respondents gender Source: IBM SPSS V31

Out of 106 respondents, 31 % were male and 69 % were female. It was consistent with gender trend by (Sapie et al., 2018) where female (54.4 %) respondents were more than male (45.6 %). Table 4.2 below shows the age group of respondents.

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Table 4.2 - Respondents age group Source: IBM SPSS V31

Age group above 56 years represented highest respondents. It was consistent with testimony of adult student who started learning recitation quite late at the age of 45 years old (Berry, 2025). This student started recitation learning after fairly successful in discharging his responsibility towards family and career. It was right time to consider about self-improvement. Therefore, age above 45 years represents majority (84 %) of adult students. Age 56 years was approaching to retirement age of 60 years old according to Minimum Retirement Age Act 2012 (MOHR, 2023).

Table 4.3 below shows the category of program which respondents either had been attending or still attending.

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Table 4.3 Programs attended Source: IBM SPSS V31

Approximately 79 % of respondents either had attending or still attending recitation program. The other 21 % were still learning basic knowledge. The high percentage of respondents attended recitation until completion of 30 chapters was consistent with government circular in 2004 in tandem with the launching of recitation program curriculum for 30 chapters in school (MOE, 2004).

Table 4.4 below shows learning providers within Seremban area.

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Table 4.4 Learning providers in Seremban Source: IBM SPSS V31

Majority (68 %) of adult learners in Seremban attending programs conducted by individual or providers other than popular learning centers. Popular centers are Al Baghdadi Learning Center, Azdan Quranic Center and Rumah Ngaji (Al Baghdadi Group Sdn Bhd, 2018; Rumah Ngaji, 2025; Azdan Quranic Center, 2019). Al Baghdadi Learning Center had received many awards by Malaysia Book of Records. Rumah Ngaji had 485 learning centers across the country. Azdan Quranic Center had 1.5 million social media followers.

Learners selected learning centers based on other considerations rather than just popular brand name. According to the above testimony, he started recitation learning at the age of 45 years old and felt happy with his fellow learners who treated the group like small community with support and motivation (Berry, 2025).

Table 4.5 below shows profile of respondents who had completed Quranic recitation learning for 30 chapters.

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Table 4.5 - Profile of respondents completed learning for 30 chapters Source: IBMM SPSS V31

There was larger percentage of respondents who had learned or completed Quranic recitation for 30 chapters. With this profile, it could be expected that majority of respondents should be able to recite Quran fluently (MOE, 2004).

4.4 RO1 - To Determine Effectiveness Level of Learning Program

RO1 is to determine the effectiveness level of Quranic recitation program implementation using CIPP model. An evaluation tool called implementation score card was used to display comparative findings of IBM SPSS descriptives statistics and PLS-SEM statistics.

Table 4.6 below displays the score card for program implementation under context dimension construct. CIPP model suggested to use checklist in evaluation such as checklist for institutionalization and mainstreaming evaluation (Stufflebeam & Coryn, 2014).

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Table 4.6 - Implementation score card for context dimension Source: IBM SPSS V31 & SMART PLS4

Overall means for this construct indicated high level of implementation with average mean score of 98.1 % compared with study by (Sapie et al., 2018) which found mean score of 71.5 % in the implementation of recitation program at government primary school.

However, after performing CFA using PLS-SEM all items under learning program component had to be removed because factor loading (< 0.708) (Hair, 2014). It indicated either respondents deliberately refused to choose the items or due to their actual level of knowledge about the program during the course of learning.

However, those low factor loading indicators are still relevant indicators for program implementation evaluation checklist (Stufflebeam & Coryn, 2014) as supported by average mean (Q1 to Q5) of 98.14 %. Items Q1 to Q5 supported by MOE (2004) which emphasized the importance of student to learn recitation for 30 chapters to achieve recitation fluency. The method of learning in recitation program is called talaqqi & musyafahah which require student to learn in front of teacher (al-Majidi, 2000; Nik, 2004).

Items Q6 to Q10 of learning objective components recorded factor loadings above 0.708 (Hair, 2014). It indicated that respondents had chosen the items to measure the construct with AVE of 76.6 % (Hulland, 1999). Items Q6, Q8 and Q10 were adopted for describing the benefit of recitation learning (Surul, 2021). Items Q7 and Q9 were adopted for describing the benefit of recitation learning for the perfection of prayer (Hazri, 2016).

Item Q11 read as “able to teach others” scored low factor loading (< 0.708) (Hair, 2014). Nevertheless, this item was still an important indicator based on CIPP model checklist by (Stufflebeam & Coryn, 2014) as it was supported by previous research on adult recitation learning which found that 30 % of adult learners had been able to become Quranic recitation teacher after successfully completed adult recitation learning program at UniSZA university mosque (Sharifah et al.,2018).

As summary, items Q1 to Q5 and item Q11 created items gap of 54 % between indicators should be in the checklist (Stufflebeam & Coryn, 2014) and actual indicators with factor loadings (< 0.708).

Table 4.7 below shows the implementation score card under input dimension construct.

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Table 4.7 - Implementation score card for input dimension Source: IBM SPSS V31 & SMART PLS4

Overall means for this construct indicated high level of implementation with average mean score of 96.6 % compared with previous study by (Sapie et al., 2018) which found mean score of 83.23 % in the implementation of the program at government primary schools.

However, after performing CFA all items under student attitude components except Q17 had been removed because of factor loading (< 0.708) (Hair, 2014). Item Q17 provided evident that among the criteria of successful learner was the one who had high commitment in term of time and financial (Ghazali, 2007). Items with low factor loading indicated either respondents deliberately refused to choose the items or due to their actual level of knowledge about the program during the course of learning.

However, items with low factor loadings are still relevant indicators for program implementation evaluation checklist by (Stufflebeam & Coryn, 2014) as these items were supported with average mean score of 93.6 % (Q18, Q19 and Q20). Item Q18 regarding learning fee was supported by Ghazali (2007) who mentioned that to be successful in learning, student should have high commitment in term of financial and time. Item Q19 and Q20 were supported with high effect size (d = 1.09) referring to high engagement by student which affect learning performance (Huitt et al., 2009).

Items Q12 to Q16 of teacher skill component recorded factor loadings above 0.708 (hair, 2014). It indicated that respondents had chosen the items to measure the construct. These items were supported by factor of teacher quality with strong effect size (d = 0.44) (Huitt et al., 2009).

As summary, items Q18 to Q20 about student attitude created item gap of 33.3 % between items should be in the checklist (Stufflebeam & Coryn, 2014) and low factor loadings.

Table 4.8 displays findings for the implementation score card of process dimension construct as shown below.

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Table 4.8 - Implementation score card of process dimension Source: IBM SPSS V31 & SMART PLS4

Overall means for this construct indicated high level of implementation with average mean score of 96.6 % compared with study by (Sapie et al., 2018) which showed 72 % in the implementation level of the program at government primary schools.

However, after performing CFA analysis items Q21, Q22, Q27 and Q28 under teaching strategy and learning assessment components had been removed because factor loading (< 0.708) (Hair, 2014). It indicated either respondents deliberately refused to choose the items or due to their actual level of knowledge about the program during the course of learning.

Items Q23, Q24, Q25 and Q26 were supported by literatures which described the method of talaqqi and musyafahah adopted in recitation learning (al-Majidi, 2000; Nik, 2004). Item Q29 was supported by element under student feedback with strong effect size (d = 0.73) (Huitt et al., 2009)

Despite low factor loadings for items Q21, Q22, Q27 and Q28, the items were still relevant indicators for program implementation evaluation items which should be in the checklist (Stufflebeam & Coryn, 2014, p. 677) as it was supported by average mean score of 95 % (Q21, Q22, Q27 & Q28). Item Q21, Q22 and Q28 were supported by (MOE, 2004) which require student to learn recitation using talaqqi and musyafahah method.

Under talaqqi and musyafahah method teacher had to explain tajwid rules and demonstrate the recitation for student (al-Majidi, 2000; Nik, 2004). Item Q27 was supported by theory by Ibnu Khaldun which stated that student must understand certain topic before moving to next topic (Jasmi & Che Noh, 2013).

Items Q23, Q24, Q25, Q26 and Q29 of teaching strategy and learning assessment components recorded factor loadings above 0.708 (Hair, 2014). It indicated that respondents had chosen the items to measure the construct.

As summary, items Q21, Q22, Q27 and Q28 of teaching strategy and learning assessment created item gap of 44.4 % between items should be in evaluation checklist (Stufflebeam & Coryn, 2014) and low factor loadings.

Table 4.9 displays findings for the implementation score card of product dimension construct as shown below.

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Table 4.9 - Implementation score card for product dimension Source: IBM SPSS V31 & SMART PLS4

Overall means for this construct indicated high level of implementation with average mean score of 94.5 % compared with study by (Sapie et al., 2018) which found mean score of 88 % in the implementation of the program at government primary school.

However, after performing CFA analysis items Q32, Q34, Q35 and Q36 under learning achievement had been removed. Item Q41 and Q42 under student satisfaction also had been removed. These items did not qualify for minimum factor loading above 0.708 (Hair, 2014). It indicated either respondents deliberately refused to choose the items or due to their actual level of knowledge about the program during the course of learning.

Items Q30, Q31, Q33, Q37, Q38, Q39 and Q40 recorded factor loadings above 0.708 (Hair, 2014). These items had been chosen by respondents to measure the construct. Items Q30, Q31 and Q33 were adopted from study by (Surul & Muhammad, 2013) which identified the criteria to measure the performance of Quranic recitation. Items Q37, Q38, Q39 and Q40 were adopted from (Surul, 2021; Hazri, 2016) who described the benefit of learning Quranic recitation.

Despite items indicators with low factor loadings, these items were still relevant indicators for program implementation evaluation checklist by (Stufflebeam & Coryn, 2014) as it was supported by average mean score of 91.8 % (Q32, Q34, Q35, Q36, Q41 & Q42). Items Q32, Q34, Q35 and Q36 were adopted from study by (Surul & Muhammad, 2013) to determine criteria to measure Quranic recitation performance. Item Q41 and Q42 were supported by (Calli et at., 2013) which stated that customer who satisfied will share their satisfaction to others.

As summary, items Q32, Q34, Q35, Q36, Q41 and Q42 of learning achievement and student satisfaction component created item gap of 46 % between item should be in checklist by (Stufflebeam & Coryn, 2014) and low factor loadings.

Figure 4.2 displays overall summary of findings for implementation score card of all dimensions in CIPP model.

SUMMARY DESCRIPTIVE ANALYSIS OF INDICATORS USING CIPP MODEL FOR LEARNING PROGRAM

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Figure 4.2 - Summary for implementation score card of all CIPP dimensions

Source: IBM SPSS V31 & SMART PLS4

Overall summary, based on all CIPP model checklists by (Stufflebeam & Coryn, 2014) context dimension recorded highest item gap followed by product dimension, process dimension and input dimensions with gap percentage (54 %, 46 %, 44 % & 33 %) respectively. Recommendation for items gap improvement will be made in Chapter 5.

4.5 RO2 - To Study the Effect of Context, Input and Process Towards Product Dimension Constructs

Figure 4.3 displays result of CIPP model analyzed using PLS-SEM. Detail of measurement model (CFA) test and structural model (SEM) test are explained below.

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Figure 4.3 - PLS-SEM for CIPP model Source: SMART PLS4

All indicators under context, input, process and product dimension constructs had achieved criteria of factor loadings above 0.708 (Hair, 2014) as shown in Table 4.10 below. Based on the results, respondents selected item Q9 reads as “More confident with my recitation during prayers” to be the strongest indicator to measure context dimension construct with highest factor loading of 0.923. Item Q14 reads as “Teachers have patient during teaching” was selected by respondents as the strongest indicator to measure input dimension construct with highest factor loading of 0.871.

Item Q25 read as “Teacher corrects my recitation mistake if any” was selected by respondents to be strongest indicator to measure process dimension construct with highest factor loading of 0.855. Item Q38 read as “I am more confident in my recitation during prayer” was selected by respondents to be strongest indicator to measure product dimension construct with highest factor loading of 0.903.

It was found that item Q9 referring to respondent expected learning objective (To be more confident with recitation during prayer) and Q38 referring to actual learning experience (I am more confident with my recitation during prayer) had been consistently selected by adult learners which indicated the importance of good recitation during prayer as learning objective.

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Table 4.10 - CIPP construct items factor loading value Source: SMART PLS4

CFA test results showed that context, input, process and product dimension constructs had achieved minimum requirement for convergent validity with respective value of factor loading above 0.708 (Hair, 2014) as shown in Table 4.10 above.

Context, input, process and product dimension constructs achieved Cronbach’s alpha above 0.7 (Muda et al., 2018) with respective value (0.923, 0.911, 0.864 & 0.908). Context, input, process and product dimension constructs achieved composite reliability above 0.7 (Hair, 2010) with respective value (0.942, 0.932, 0.902 & 0.927).

Context, input, process and product dimension constructs achieved average variance extracted above 0.5 (Hulland, 1999) with respective value (0.766, 0.695, 0.649 & 0.648). Result for Cronbach alpha, composite reliability and AVE are shown in Table 4.11 below.

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Table 4.11 - CFA results for CIPP model

Source: SMART PLS4

Next test under CFA is to ensure context, input, process and product dimension constructs achieved discriminant validity test using Fornell- Larker. Test result in Table 4.12 below showed value 0.875 greater than 0.716, 0.551 and 0.743. Value 0.833 greater than 0.716, 0.719 and 0.727. Value 0.806 greater than 0.719, 0.551 and 0.699. Value 0.805 greater than 0.699, 0.727 and 0.743.

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Table 4.12 - Discriminant validity test for CIPP model Source: SMART PLS4

Having satisfied with measurement model (CFA) test for item indicators measuring all constructs, next step was to test structural model which consisted of many causal relationships among constructs. Results on structural model test are shown in Table 4.13, Table 4.14 and Table 4.15 below.

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Table 4.13 - Structural model test criteria for CIPP model Source: Ahmad (2023)

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Table 4.14 - Structural model test results for CIPP model Source: SMART PLS4

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Table 4.15 - Hypotheses result for CIPP model Source: SMART PLS4

Ahmad (2023) described hypothesis testing for structural model start with checking value of VIF, t value, p value, beta value and R[2] In answering RO2, criteria of VIF, t value, p value and beta value had been checked for each hypothesis as shown in Table 4.13, Table 4.14 and Table 4.15 above. Criteria R2 will only be used for RO3 analysis.

H1 represented causal effect of context dimension construct towards product dimension construct. Context dimension construct was represented by learning objective factor with strong effect size (d = 0.48). Curriculum factor with effect size (d = 0.6) was removed from the construct due to low factor loading.

Test results in Table 4.14 and 4.15 supported H1 hypothesis. VIF value (2.066) was less than threshold of 3.3 indicated there was no problem with multicollinearity or high correlation among constructs in conceptual framework. T-value (2.338) above threshold (1.96) and p-value (0.019) within significant level (0.05) indicated that the hypothesis was significant.

Beta value (0.431) is categorised as moderate level (Cohen, 1988) which mean that for one unit improvement in context dimension construct, product dimension construct increases by 0.431 unit. Item Q9 of learning objective

(To be more confident with recitation in prayer) was strongest indicator should be priority factor to improve by learning provider because it could affect product dimension construct significantly with potential effect of 0.431 in term of beta value.

H3 represented causal effect of process dimension construct towards product dimension construct. Process dimension construct was represented by teaching strategy factor with strong effect size (d = 0.60) and student feedback with strong effect size (d = 0.73).

Test result in Table 4.14 and 4.15 supported H3 hypothesis. VIF value (2.081) was less than threshold of 3.3 indicated there was no problem with multicollinearity or high correlation among constructs in conceptual framework. T-value (2.385) above threshold (1.96) and p-value (0.017) within significant level (0.05) indicated that the hypothesis was significant.

Beta value (0.333) was categorised as moderate level (Cohen, 1988) which mean that for one unit improvement in process dimension construct, product dimension construct increases by 0.333 unit. Item Q25 of teaching strategy (Teacher has to correct student recitation) was strongest indicator should be the priority factor to improve by learning provider because it could affect product dimension construct significantly with potential effect of 0.333 in term of beta value.

H2 represented causal effect of input dimension construct towards product dimension construct. Test result in Table 4.14 and 4.15 did not support H2 hypothesis. VIF value (2.976) was less than threshold of 3.3 indicated that there was no problem with multicollinearity or high correlation among constructs in conceptual framework.

However, t-value (0.953) below threshold (1.96) and p-value (0.341) above significant level threshold (0.05) indicated that the hypothesis was not significant. Beta value (0.179) was also categorised as small level (Cohen, 1988).

4.6 RO3 - To Assess the Strength of Context, Input and Process Affecting Product Dimension Constructs

Analysis on RO3 had been based on Figure 4.3 and Table 4.13 above. Based on Figure 4.3, R[2] value of product dimension construct is 0.683. According to Chin (1998) this is substantial value means that 68.3 % of variance in product dimension construct is explained by variance in context and process dimension constructs.

Ramayah et al. (2018) described R2 as a way to assess model fit. Therefore, R2 of 68.3 % considered as substantial good model fit for context and process dimension constructs to predict product dimension construct with substantial accuracy. It was higher than average R[2] (42 %) discussed in literature review chapter. It indicated that this study had been successfully carried out based on literatures.

4.7 RO4 - To Assess the Strength of Context, Input and Process Affecting Learning Achievement Constructs

Analysis on RO4 was based on result of second conceptual framework analyzed using PLS-SEM method as displays in Figure 4.4 below.

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Figure 4.4 - PLS-SEM for extended CIPP model Source: SMART PLS4

CFA test (Ahmad, 2023) result showed that all indicators under context, input, process, learning achievement and student satisfaction constructs had achieved factor loadings above 0.708 (Hair, 2014) as shown in Table 4.16 below.

Item Q9 read as “To be more confident with my recitation during prayer” was strongest indicator to measure context dimension construct with highest factor loading (0.923). Item Q25 read as “Teacher correct my recitation mistake” was strongest indicator to measure process dimension construct with highest factor loading (0.851).

Item Q31 read as “Correct point of articulation and letter characteristic” was strongest indicator to measure learning achievement construct with highest factor loading (0.873). Item Q38 reads as “More confident with my recitation during prayer” was strongest indicator to measure student satisfaction construct with highest factor loading (0.931).

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Table 4.16 - Extended CIPP model construct items factor loading value Source: SMART PLS4

CFA test found that context, input, process, learning achievement and student satisfaction constructs achieved minimum requirement for convergent validity with respective value of factor loading above 0.708 (Hair, 2014) as shown in Table 4.16 above.

Context, input, process, learning achievement and student satisfaction dimension constructs achieved Cronbach’s alpha above 0.7 (Muda et al.,

2018) with respective value (0.923, 0.911, 0.864, 0.900, 0.900). Context, input, process, learning achievement and student satisfaction dimension constructs achieved composite reliability above 0.7 (Hair, 2010) with respective value (0.942, 0.931, 0.902, 0.923 & 0.927).

Context, input, process, learning achievement and student satisfaction dimension constructs achieved average variance extracted above 0.5 (Hulland, 1999) with respective value (0.766, 0.694, 0.649, 0.666 & 0.719). Result of Cronbach alpha, composite reliability and AVE are shown in Table 4.17 below.

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Table 4.17 - CFA results for extended CIPP model Source: SMART PLS4

Next test under CFA found that context, input, learning achievement, process and student satisfaction constructs achieved discriminant validity test using Fornell-Larker as shown in Table 4.18 below. Value 0.875 greater than 0.717, 0.491, 0.549 and 0.777. Value 0.833 greater than 0.717, 0.586, 0.719 and 0.666. Value 0.816 greater than 0.491, 0.586, 0.623 and 0.657.

Value 0.805 greater than 0.549, 0.719, 0.623 and 0.623. Value 0.848 greater than 0.777, 0.666, 0.657 and 0.623.

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Table 4.18 - Discriminant validity test for extended CIPP model

Source: SMART PLS4

Having satisfied with CFA test, the structural model analysis had been performed (Ahmad, 2023). The criteria and result are shown in Table 4.19 and Table 4.20 below.

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Table 4.19 - Structural model test criteria for extended CIPP model

Source: SMART PLS4

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Table 4.20 - Structural model test results for extended CIPP model Source: SMART PLS4

Ahmad (2023) described hypothesis testing for structural model starting with checking value of VIF, t value, p value and beta value. Therefore, analysis for RO4 had used all test criteria in Table 4.19 above.

H6 represented causal effect of process dimension construct towards learning achievement construct. Process dimension construct was represented by teaching strategy and student feedback factors with respective strong effect size (d = 0.60 & 0.73).

Test result in Table 4.20 above supported H6 hypothesis. VIF value (2.082) was less than threshold of 3.3 indicated there was no problem with multicollinearity or high correlation among constructs in conceptual framework. T-value (2.976) above threshold (1.96) and p-value (0.003) within significant level (0.05) indicated that the hypothesis was significant.

Beta value (0.408) is categorised as moderate level (Cohen, 1988) which mean that for one unit improvement in process dimension construct, learning achievement construct increases by 0.408 unit. Item Q25 read as “Teacher correct my recitation mistake” was strongest indicator with highest factor loading (0.851).

H7 represented causal effect of context dimension construct towards student satisfaction construct. Context dimension construct was represented by learning objective factor with strong effect size (d = 0.48). Curriculum factor with effect size (d = 0.6) was removed from the construct.

Test criteria in Table 4.20 supported H7 hypothesis. VIF value (2.090) was less than threshold of 3.3 indicated there was no problem with multicollinearity or high correlation among constructs in conceptual framework. T-value (2.676) above threshold (1.96) and p-value (0.007) within significant level (0.05) indicated that the hypothesis was significant.

Beta value (0.564) is categorised as moderate level (Cohen, 1988) which mean that for one unit improvement in context dimension construct, student satisfaction construct increases by 0.564 unit. Item Q9 on learning objective to increase confident in prayer was strongest indicator with highest factor loading (0.923).

H10 represented causal effect of learning achievement towards student satisfaction constructs. Test criteria in Table 4.20 supported H10 hypothesis. VIF value (1.767) was less than threshold of 3.3 indicated there was no problem with multicollinearity or high correlation among constructs in conceptual framework. T-value (2.610) above threshold (1.96) and p- value (0.009) within significant level (0.05) indicated that the hypothesis was significant.

Beta value (0.305) is categorised as moderate level (Cohen, 1988) which mean that for one unit improvement in learning achievement construct, student satisfaction construct increases by 0.305 unit. Item Q31 on correct letters articulation and characteristics was strongest indicator with highest factor loading (0.873).

H4 represented causal effect of context dimension construct towards learning achievement construct. Test criteria in Table 4.20 did not support H4 hypothesis. VIF value (2.065) was less than threshold of 3.3 indicated there was no problem with multicollinearity or high correlation among constructs in conceptual framework.

However, t-value (0.624) below threshold (1.96) and p-value (0.532) above significant level threshold (0.05) indicated that the hypothesis was not significant. Beta value (0.118) was also categorised as small level (Cohen, 1988).

H5 represented causal effect of input dimension construct towards product dimension construct. Test criteria in Table 4.20 did not support H5 hypothesis. VIF value (2.990) was less than threshold of 3.3 indicated there was no problem with multicollinearity or high correlation among constructs in conceptual framework.

However, t-value (1.151) below threshold (1.96) and p-value (0.250) above significant level threshold (0.05) indicated that the hypothesis was not significant. Beta value (0.208) was also categorised as small level (Cohen, 1988).

H8 represented causal effect of input dimension construct towards student satisfaction construct. Test criteria in Table 4.20 did not support H8 hypothesis. VIF value (3.066) was less than threshold of 3.3 indicated there was no problem with multicollinearity or high correlation among constructs in conceptual framework.

However, t-value (0.052) below threshold (1.96) and p-value (0.958) above significant level threshold (0.05) indicated that the hypothesis was not significant. Beta value (-0.013) was way below category of small level (Cohen, 1988).

H9 represented causal effect of process dimension construct towards student satisfaction construct. Test criteria in Table 4.20 did not support H9 hypothesis. VIF value (2.376) was less than threshold of 3.3 indicated there was no problem with multicollinearity or high correlation among constructs in conceptual framework.

However, t-value (0.866) below threshold (1.96) and p-value (0.387) above significant level threshold (0.05) indicated that the hypothesis was not significant. Beta value (0.133) was categorised as small level (Cohen, 1988).

Having satisfied all SEM criteria, R[2] for learning achievement could be obtained from Figure 4.4 with value of 0.434 or 43.4 %. According to Chin (1998) this is moderate value which indicate that 43.4 % of variance in learning achievement construct is explained by variance in process dimension constructs.

Ramayah et al. (2018) described R2 as a way to assess model fit. Therefore, R2 of 43.3 % considered as moderate level model fit for process dimension constructs to predict learning achievement construct with moderate accuracy. This value was higher compared with 42 % discussed in literature review chapter for non-Quranic recitation learning studies. It indicated that this study had been successfully carried out based on literatures.

4.8 RO5 - To Assess the Strength of Context, Input, Process and Learning Achievement Affecting Student Satisfaction Constructs

Analysis of RO5 is based on SEM output results in Table 4.19 and Figure 4.4 above. Having satisfied all SEM criteria, R[2] for student satisfaction could be obtained from Figure 4.4 with value of 0.713 or 71.3 %. According to Chin (1998) this is substantial level which indicate that 71.3 % of variance in student satisfaction construct is explained by variance in process dimension, learning achievement and context dimension constructs.

Ramayah et al. (2018) described R2 as a way to assess model fit. Therefore, R2 of 71.3 % considered as substantial level model fit for process dimension, learning achievement and context dimension constructs to predict student satisfaction construct with substantial accuracy. This value was higher compared with 42 % discussed in literature review chapter for non-Quranic recitation learning studies. It indicated that this study had been successfully carried out based on literatures.

For comparison, the alternative SEM model for predictor of student satisfaction as shown in Figure 4.5 below showed lesser model fit value with moderate R[2] (44.4 %). Therefore, this alternative framework was less preferred.

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Figure 4.5 - Alternative PLS-SEM for extended CIPP model Source: SMART PLS4

4.9 RO6 - To Analyze Mediating Effect of Learning Achievement Between Context, Input, Process Dimensions and Student Satisfaction Constructs

RO6 analysis is based on Table 4.21 below. Table 4.21 shows the result of path analysis using PLS-SEM from SEM diagram in Figure 4.4 above.

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Table 4.21 - Path analysis for indirect relationship

Source: SMART PLS4

H11 represented causal relationship between context dimension, learning achievement and student satisfaction constructs. Path analysis result found t-value (0.522) below threshold of 1.96 and p value (0.602) above threshold of 0.05. Therefore, H11 was not significant.

H12 represented causal relationship between input dimension, learning achievement and student satisfaction construct. Path analysis result found t-value (0.934) below threshold of 1.96 and p value (0.350) above threshold of 0.05. Therefore, H12 was not significant.

H13 represented causal relationship between process dimension, learning achievement and student satisfaction constructs. Path analysis result found t-value (1.817) below threshold of 1.96 and p value (0.069) above threshold of 0.05. Therefore, H13 was not significant.

As summary none of the above indirect relationships were statistically significant. It means that learning achievement construct is not mediating factor between relationship of context, input and process dimensions and student satisfaction constructs.

4.10 RO7 - To Compare the Difference in Effectiveness on Learning Achievement and Student Satisfaction Between Students Completed Learning for 30 Chapters and Not Completed

RO7 is analyzed using t test from IBM SPSS V31 software. The result is shown in Table 4.22. H14 represented significant different on learning achievement between students completed learning for 30 chapters and not completed.

The rationale for this hypothesis was based on circular by MOE (2004) which expected that student who completed learning recitation for 30 chapters would be fluent in recitation.

Result shows equal variance assumed (0.024) and equal variance not assumed value (0.050) were below threshold of 0.05 significant level. Therefore, H14 was supported. It means that there is significant different in learning achievement between students who completed learning for 30 chapters and not completed.

Group Statistics

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Table 4.22 - Independent t test for learning achievement

Source: IBM SPSS V31

H15 represented significant different on student satisfaction between students completed learning for 30 chapters and not completed. Independent t test result in Table 4.23 shows that equal variance assumed (0.450) and equal variance not assumed value (0.508) were above threshold of 0.05 significant level.

Therefore, H15 was not supported. It means that there is no significant different in student satisfaction between students who completed learning for 30 chapters and not completed.

Group Statistics

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Table 4.23 - Independent t test for student satisfaction

Source: IBM SPSS V31

4.11 Summary

Analyses for RO1 to RO7 had been carried out successfully to answer RQ1 to RQ7. Hypotheses (H1, H3, H6, H7, H10 & H14) were tested as significant while (H2, H4, H5, H8, H9, H11, H12, H13 & H15) were tested as not significant.

Findings from RO1 to RQ7 established new implementation score card for recitation program using CIPP model, two new conceptual frameworks using (CIPP model & Walberg theory) with moderate and substantial model fit value (R[2] = 43.4 %, 68.3 % & 71.3 %) respectively.

Predictors for learning achievement, product dimension and student satisfaction constructs had been established with accuracy from moderate to substantial respectively.

Finding of t test supported the government initiative in launching of recitation program for 30 chapters in 2004 under JQAF curriculum in government schools aiming for recitation fluency upon completion.

CHAPTER 5.0 SUMMARY, RECOMMENDATION AND CONCLUSION

5.1 Introduction

This chapter discusses research summary, recommendation, contribution, implication, future research, limitation and conclusion.

5.2 Research Summary

Research problem was about the difficulties of learners to know the effectiveness of adult learning program because most of program advertisements claimed the programs were effective. Seven research problems and objectives had been identified supported by fifteen hypotheses.

Research objectives RO1, RO2, RO3, RO4, RO5 and RO7 had been achieved. RO6 and sub-objective of RO7 had not been achieved. Hypotheses H1, H3, H6, H7, H10 and H14 had been tested as significant while H2, H4, H5, H8, H9, H11, H12, H13 and H15 tested as not significant.

5.3 Research Recommendation

Recommendations were made for each research objective.

5.3.1 Recommendation for RO1 - Minimize Score Card Gap

This section discusses how to minimize indicators gap in the implementation score card. Figure 5.1 below shows statistics of implementation item indicators gap under respective dimension starting from highest gap from left hand side to right hand side.

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Figure 5.1 - Indicators gap statistics

Source: SMART PLS4 & IBM SPSS V31

Context dimension - Learning providers are recommended to include in learning syllabus and continuously share with students the concept of recitation learning program and its benefit as stated in Q1, Q2, Q3, Q4, Q5 and Q11 in Table 4.6 which represented items gap aimed to minimize the gap.

Example of the item is the importance of recitation learning until complete 30 chapters which had been implemented in government schools. This contradicted with the advertisement claiming that student can learn recitation shortcut within 3, 7 or 24 hours (Refer Chapter 1.2). Therefore, learning providers should focus on minimizing gap under this construct because this construct has highest gap (54 %).

Product dimension - Learning providers are recommended to include in learning syllabus and continuously share to students the element of good recitation criteria to assess the correctness of Quranic recitation as described by (Surul & Muhammad, 2013). It is referred as six elements of good recitation criteria to assess student proficiency objectively.

The elements are: (1) Correct letters and vowel signs, (2) Correct articulation and letter characteristics, (3) Thick and thin recitation, (4) Recitation with or without nasal sound, (5) Length of mad (vowel sound) with 2,4 or 6 counts and (6) Start and stop recitation. This dimension represents second highest of items gap (46 %).

Process dimension - Learning providers are recommended to emphasize on teaching strategy for example the method in teaching tajwid rules effectively to students. A diagram to display some of the six criteria above (Criteria 3,4 & 5 in Malay language) might be used as shown in Figure 5.2 below. Visual diagram could assist student in cognitive learning better than textual notes (Gates, 2018). This dimension has third highest gap (44 %).

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Figure 5.2- Diagram for criteria 2, 4 & 5 recitation criteria Source: Surul & Muhammad (2013)

Input dimension - Learning providers are recommended to continuously remind students that their engagement in learning has strong effect to learning achievement with high effect size (d = 1.09) (Huitt et al., 2009). Teachers should ensure all students demonstrate their recitation in class either individually or in group. This dimension represents the lowest gap of (33 %).

Besides recommendation to learning providers, state religious authority particularly in Negeri Sembilan (Mufti Department) is recommended to engage with Muslim community through social media such as podcast, YouTube or Facebook live. Among the topics to be discussed are how to assess recitation program effectiveness using CIPP implementation evaluation score card. It was regular practice by the authority to conduct such program for example discussion on recitation tajwid error by Dr Anuar Hasin on 21 November 2024 (MuftiN9 Channel, 2024). Learners would obtain information on effectiveness of recitation program from these media.

5.3.2 Recommendation for RO2 & RO3

The religious state authority in Negeri Sembilan is recommended to engage with Muslim community through social media similar in Chapter 5.3.1. Topic to discuss is conceptual model based on CIPP and Walberg theory to evaluate adult recitation learning. Among key discussion is the factors affecting product dimension and empirical indicators of R[2]

5.3.3 Recommendation for RO4 - Increase Learning Achievement

This section discusses how to increase learning achievement of recitation program. Figure 5.3 below shows framework to predict learning achievement with moderate (R2 = 43.4 %) predictive accuracy using process dimension as predictor. Beta value (0.408) means for one unit increase in process dimension, learning achievement will increase by 0.408 unit.

Learning providers are recommended to improve indicators with higher factor loading following the sequence of Q25, Q26, Q29, Q24 and Q23. The rational is indicator with higher factor loading contributes stronger to measure process dimension variable.

During actual teaching process teacher should emphasize on: (1) Q25 - Teacher correct student recitation mistake, (2) Q26- Teacher ensure student repeat recitation until achieve correct recitation, (3) Q29 - Teacher should inform student what other aspects to improve to achieve perfect recitation, (4) Q24 - Teacher should listen attentively to student’s recitation to assess fluency and (5) Q23 - Teacher should ensure student listen attentively during recitation demonstration by teacher. This sequence follows the order of indicators factor loading values from highest to smallest.

Abb. in Leseprobe nicht enthalten

Figure 5.3- Improvement on learning achievement Source: SMART PLS4

5.3.4 Recommendation for RO5 - Increase Student Satisfaction

This section discusses how to increase student satisfaction of recitation program. Figure 5.4 below shows framework to predict student satisfaction with substantial (71.3 %) predictive accuracy using context dimension and process dimension as predictors. Priority should start with context dimension because it has higher beta value. Beta value (0.564) means for one unit increase in context dimension student satisfaction will increase by 0.564 unit.

Learning providers are recommended to improve indicators with higher factor loading following the sequence of Q9, Q10, Q7, Q8 and Q6. The rational is indicators with higher factor loading contribute stronger to measure context dimension variable.

Teacher should ensure students understand the method and objectives of recitation program by emphasizing on: (1) Q9 - Objective of program to be more confident with recitation in prayer, (2) Q10- Achieve happiness during recitation, (3) Q7 - Improve recitation fluency in prayer, (4) Q8 - More confident in recitation and (5) Q6 - Improve recitation fluency. This sequence follows the order of indicators factor loading values from highest to smallest.

Process dimension construct as second predicting factor with effective small beta value towards student satisfaction (0.408 X 0.305=0.124) (Cohen, 1988) should be improved to ensure learning achievement is improved which finally improve student satisfaction. The sequence of improving indicators for process dimension follows recommendation in Chapter 5.3.3 above.

Abb. in Leseprobe nicht enthalten

Figure 5.4 - Predicting factor for student satisfaction

Source: SMART PLS4

5.3.5 Recommendation for RO7 - State Religious Authority Issues Guideline That Adult Learners Should Learn Recitation Complete 30 Chapters

Based on finding of RO7 that there was significant different in learning achievement between student who complete recitation learning for 30 chapters and not complete for 30 chapters, state religious authority of Negeri Sembilan which monitor adult recitation learning is recommended to issue circular to all community learning teachers that adult student should complete learning for 30 chapters to achieve recitation fluency. The same circular had been issued in 2004 by MOE to all government primary schools in 2004.

5.4 Research Contribution

The most significance contribution from this research is to establish causal relationship among CIPP model components using causal relationship theory from Walberg education productivity theory.

This finding was able to establish a conceptual framework with substantial model fit (R[2] = 68.3 %) above threshold of 67 % by (Chin, 1988). According to Ramayah et al. (2018) this could be a substantial predictive model for context and process dimension variables towards product dimension variable with 68.3 % accuracy.

The model was supported with significant causal relationship (p = 0.019) between context dimension and product dimension variables with moderate beta value of 0.431 (Cohen, 1988). Also supported with significant causal relationship (p = 0.017) between process dimension and product dimension variables with moderate beta value of 0.333 (Cohen, 1988) as shown in Figure 5.5 below.

This is a significant contribution because CIPP model is a descriptive theory without causal relationship among variables. This study has established causal relationship in CIPP model guided by Walberg theory on education productivity. Walberg theory provides causal relationship among variables.

Abb. in Leseprobe nicht enthalten

5.5 Research Implication

There are research implications in term of theoretical, policy and practical.

5.5.1 Theoretical Implication - Extended of CIPP Model

This section discusses theoretical implication which enable to develop extended CIPP model in Chapter 5.4 to predict learning achievement and student satisfaction in recitation learning separately.

Based on framework in Figure 5.5 above, the framework was extended further to allow prediction of learning achievement and student satisfaction separately using extended framework shown in Figure 5.6 below.

Learning achievement predictor - The finding was able to establish a framework with moderate model fit (R[2] =43.4 %) above threshold of 33 % by (Chin, 1988) to predict learning achievement. According to Ramayah et al. (2018) this could be a moderate predictive model for process dimension variable towards learning achievement variable with 43.4 % accuracy.

The framework was supported with significant causal relationship (p = 0.003) between process dimension and learning achievement variables with moderate beta value of 0.408 (Cohen, 1988).

Student satisfaction predictor - The finding was able to establish a model with substantial good fit (R2 = 71.3 %) above threshold of 67 % by (Chin, 1988) to predict student satisfaction. According to Ramayah et al. (2018) this could be a substantial predictive model for context dimension, process dimension and learning achievement towards student satisfaction variables with 71.3 % accuracy.

The model was supported with significant causal relationship (p = 0.007) between context dimension and student satisfaction variables with high beta value of 0.564 (Cohen, 1998). Also supported with significant causal relationship (p = 0.003) between process dimension and learning achievement variables with moderate beta value of 0.408 (Cohen, 1988). It also supported with another significant relationship (p = 0.009) between learning achievement and student satisfaction variables with moderate beta value of 0.305 (Cohen, 1988).

Abb. in Leseprobe nicht enthalten

Figure 5.6 - Model to predict learning achievement & student satisfaction

Source: SMART PLS4

5.5.2 Policy Implication - Supported Recitation Learning For 30 Chapters Launched by MOE In 2004

This section discusses policy implication in relation to recitation learning. Independent t test found that there was significant different in learning achievement between student who had completed Quranic recitation learning for 30 chapters and not completed with equal variance assumed (0.024) and equal variance not assumed value (0.050) within threshold (p value < 0.05).

Finding supported initiative to improve Quranic recitation fluency by introducing recitation program until completion of 30 chapters in 2004 launched by 5th prime minister the late Tun Abdullah bin Ahmad Badawi (MOE, 2004). The program was part of JQAF curriculum in government primary schools. This finding was significant because despite of about twenty years implementation, study using t test on this comparison was still lacking.

5.5.3 Practical Implication

The practical implication affecting recitation learning had been discussed in recommendation chapter.

5.6 Limitation of Study

The study within Seremban had limitation in term of composition of learning providers. Individual learning providers other than popular learning providers represented 72 samples (68 %) compared with other providers (Al Baghdadi 14 %, Azdan 1 % and Rumah Ngaji 17 %). Therefore, the findings and conclusions were more representing individual providers category than other categories. With the composition, findings from this study had limitation to make comparison among learning providers for example comparison of R[2] from different centers.

5.7 Future Study

In order to improve the limitation, future study could be carried out at national level for example to focus on larger population of adult learners from Al Baghdadi, Azdan and Rumah Ngaji centers separately. The findings would enable to make comparison on program effectiveness for example in term of R2 from different centers.

5.8 Conclusion

The findings of this research found that the claims by learning providers that their learning programs were effective with short cut approach could not be used by learners to assess the effectiveness of learning programs.

Alternatively, learners should be educated to assess the learning program based on CIPP implementation score card, factors affecting learning using CIPP and Walberg theory, R[2] for product dimension, learning achievement and student satisfaction within CIPP curriculum implementation evaluation model. These findings could be shared with learners by religious state authority through its regular social media engagement.

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Appendix

Appendix A

MOE circular dated 2004 on launching of Quranic recitation program in schools

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Appendix B

Example of factors with strong effect size

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Appendix C

WhatsApp communication to learning providers

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Appendix D

Estimation of average student per learning center and sample size

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Appendix E

Pre-test feedbacks of the instrument

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Appendix F

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Final copy of instrument in Malay language

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Title: The Effectiveness of Adult Community Learning Program Implementation in Seremban, Negeri Sembilan, Malaysia

Doctoral Thesis / Dissertation , 2025 , 148 Pages , Grade: 73.00 %

Autor:in: Aminuddin Adnan (Author)

Pedagogy - General

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Title: The Effectiveness of Adult Community Learning Program Implementation in Seremban, Negeri Sembilan, Malaysia
Subtitle: Case of Quranic Recitation Program
Course: Curriculum evaluation
Grade: 73.00 %
Author: Aminuddin Adnan (Author)
Publication Year: 2025
Pages: 148
Catalog Number: V1722844
ISBN (PDF): 9783389190746
ISBN (Book): 9783389190753
Language: English
Tags: Quranic recitation program evaluation CIPP model Walberg theory PLS-SEM methodology Predicting factors for learning achievement and student satisfaction Causal relationship among CIPP model components
Product Safety: GRIN Publishing GmbH

Quote paper: Aminuddin Adnan (Author), 2025, The Effectiveness of Adult Community Learning Program Implementation in Seremban, Negeri Sembilan, Malaysia, Munich, GRIN Verlag, https://www.grin.com/document/1722844

The Effectiveness of Adult Community Learning Program Implementation in Seremban, Negeri Sembilan, Malaysia

Case of Quranic Recitation Program

Excerpt

TABLE OF CONTENTS

Appendix

LIST OF TABLES

LIST OF FIGURES

ABBREVIATIONS

Abstract

Acknowledgement

CHAPTER 1.0 INTRODUCTION

1.1 Background of Study

1.2 Research Issue

1.3 Statement of Problem

1.4 Research Objectives

1.5 Research Questions

1.6 Scope of the Study

1.8 Definition of Terms

1.8.1 Product Dimension Construct

1.8.2 Student Satisfaction Construct

1.8.3 Learning Achievement Construct

1.8.4 Context Dimension Construct

1.8.5 Input Dimension Construct

1.8.6 Process Dimension Construct

1.9 Summary

CHAPTER 2.0 LITERATURE REVIEW

2.1 Introduction

2.2 Research Gap

2.3 Curriculum Implementation Evaluation Model

2.4 Walberg Education Productivity Theory

2.5 Goal-Free Evaluation Model

2.6 Kirkpatrick’s Four Levels Evaluation Model

2.7 Matching CIPP Model and Walberg Theory

2.8 Conceptual Frameworks Development

2.9 Research Constructs

2.9.1 Context Dimension Construct

2.9.2 Input Dimension Construct

2.9.3 Process Dimension Construct

2.9.4 Product Dimension Construct

2.9.5 Learning Achievement Construct

2.9.6 Student Satisfaction Construct

2.10 Hypotheses Development

2.11 Summary

CHAPTER 3.0 RESEARCH METHODOLOGY

3.1 Introduction

3.2 Research Philosophy

3.3 Research Design

3.4 Population and Unit Analysis

3.5 Data Collection Method

3.6 Data Collection Protocol and Period

3.7 Sample Size

3.8 Sampling Technique

3.9 Research Instrument

3.10 Pre-testing Procedure

3.11 Pilot Test

3.12 Method of Data Analysis

3.13 Summary

CHAPTER 4.0 DATA ANALYSIS AND FINDINGS

4.1 Introduction

4.2 Data Profile

4.3 Respondent Demography

4.4 RO1 - To Determine Effectiveness Level of Learning Program

SUMMARY DESCRIPTIVE ANALYSIS OF INDICATORS USING CIPP MODEL FOR LEARNING PROGRAM

4.5 RO2 - To Study the Effect of Context, Input and Process Towards Product Dimension Constructs

4.6 RO3 - To Assess the Strength of Context, Input and Process Affecting Product Dimension Constructs

4.7 RO4 - To Assess the Strength of Context, Input and Process Affecting Learning Achievement Constructs

CHAPTER 5.0 SUMMARY, RECOMMENDATION AND CONCLUSION

5.1 Introduction

5.2 Research Summary

5.3.2 Recommendation for RO2 & RO3

5.3.3 Recommendation for RO4 - Increase Learning Achievement

5.5 Research Implication

5.5.1 Theoretical Implication - Extended of CIPP Model

5.5.2 Policy Implication - Supported Recitation Learning For 30 Chapters Launched by MOE In 2004

5.5.3 Practical Implication

5.6 Limitation of Study

5.7 Future Study

5.8 Conclusion

REFERENCES

Appendix