學術不誠實行為(ADB)是線上學習環境中一個新的、更嚴重的問題。這種混合方法研究的目的包括：1) 系統地回顧ADB的形式、相關因素、減少和研究線上學習中AD和TA差距的解決方案，2) 調查ADB的形式、動機、傾向，以及使用全國互相關調查解決印度尼西亞學生的學術不誠實問題，以及3) 使用每周知識共享(weekly knowledge sharing, WKS)和開放式AI探索修改評估對提高學生學習成果的影響。本研究採用質性和量化研究的混合研究方法以及案例研究來實現這些目標。研究一使用系統文獻綜述進行了分析。研究二在相關調查研究中使用定量方法來調查印度尼西亞高等教育中的ADB現象。然後將案例研究結果用於研究三。
根據研究一的分析結果發現，線上學習中廣泛報導的ADB形式通常包括作弊、串通和抄襲。將原因歸納為兩種，一是個人的，另一個是情境因素。針對這些問題，文獻中提出了一些解決方案，以及更強大的教學課程形式。此外，還有其他解決方案，例如改變評估設計。研究二調查研究結果顯示，約54.15%的受訪者在線上學習期間至少參與過1-3次ADB行為，大部分受訪者認為完全不能接受這種行為的比例高達45.02%，而23.95%選擇不能接受。獲得高度認可的幾個解決方案，包括每學期初對學生的學術道德教育。基於多元回歸測試的結果可以得出結論，使用ICT支持線上學習的動機、傾向、解決方案和能力對ADB有顯著影響。同時，年齡、性別、大學類型、受教育程度、教育領域和線上學習經驗對ADB無顯著影響。這項研究最重要的貢獻是，ADB現象普遍發生在所有層次和類型的教育中，包含男性和女性。驅動ADB的動機可能與他們對它的理解相反。而加強學術道德是一個重要的選擇，教師也需要考慮改變評估設計，使他們不依賴於一種類型的測試來確定學生的畢業。根據研究二的發現，剽竊是寫作作業中報告最多的ADB。檢查10 WKS和1 mini paper相似度的結果表明，一些學生在沒有解釋和記錄來源的情況下從其他來源抄襲。一定比例的學生有低、中、高層度抄襲行為。用於檢查學生寫作成績的Turnitin軟件已被證明有助於顯示學生的錯誤，並且學生會嘗試在下一次寫作作業中進行改述。使用WKS可以幫助學生增加對課程材料的知識，這可以從基於檢查前測和後測分數的結果中看出。
本研究的貢獻涉及理論、實踐和方法論。從理論上講，這些發現支持並強化了先前關於ADB的形式、影響因素和應對措施的理論。在實際貢獻中，本研究鼓勵每所大學就學術誠信問題和對違反者的處罰制定詳細的指導方針，除此之外，教師必須在每堂課中教育學生，並使用多種技術來減少ADB。同時，該研究最重要的貢獻是在方法論上，因為它試圖從系統性文獻分析（研究一）、相關調查（研究二）和案例研究（研究三）進行綜合研究。此外，教師可以學習使用改進的評估形式，以進一步減少學生中出現ADB的可能性。就開放式人工智慧的廣泛發展而言，學生可以輕鬆地訪問和利用它來支持他們的學習。然而，教師們需要制定指導方針，關於如何使用開放式人工智慧作為學習資源之一，同時又不過度依賴該技術。
關於未來的研究，關鍵是要進行額外的研究，以覆蓋更多未包括在本研究中的其他高等教育機構的受訪者。此外，對初中和高中學生情境的研究也有必要。在剽竊問題的背景下，進一步的研究，如研究三所討論的，可以調查在更多樣化的作業形式中，使用開放式人工智慧的有效性差異，以及對學生在各種科學領域中認知獲得的長期影響。未來研究的另一個重要議題是開發針對各級教育學生的學術道德線上課程，以促進學術誠信並盡量減少學術不端行為的現象。

Academic dishonesty behaviours (ADB) are reported to be a new, more massive problem in the online learning environment. The purposes of this mixed-method studies are includes: 1) to systematically review the forms of ADB, associated factors, solutions to reduce and research gaps about AD and TA in online learning, 2) to investigate the forms of ADB, motivation, propensity, and solution of academic dishonesty among Indonesian students using national cross-correlational survey, and 3) to explore the effect of modification assessments desain using Weekly Knowledge Sharing (WKS) and open AI in enhancing student learning outcomes. This research used a mixed method both qualitative and quantitative approaches as well as a case study to achieve these objectives. A qualitative approach was carried out in the first study used a systematic literature review (SLR). Meanwhile, quantitative methods are used in correlational survey research to investigate empirical ADB phenomenon in Indonesia's higher education. Then the case study was employed for the study 3.
Based on the results of data analysis in study 1, it was found that, generally, the forms of ADB widely reported in online learning practices include cheating, collusion, and plagiarism. The causal factors are grouped into two, namely individual, as well as situational factors. Meanwhile, the solutions offered are the use of technology, strong introductory academic ethics courses, as well as changes to the assessment design. The survey research results revealed that about 54.15% respondents had been involved in ADB practice at least 1-3 times during their online learning experience. Most respondents understood that as much as 45.02% of this behaviour was totally unacceptable, 23.95% chose unacceptable. Several solutions that have received high approval to be applied sequentially include education about student academic ethics at the beginning of each semester. Based on the results of multiple regression tests, it can be concluded that motivation, propensity, solutions and capabilities of using ICT to support online learning significantly affect ADB with a total influence of 45.8%. Meanwhile, age, gender, type of university, level of education, the field of education and online learning experience have no significant effect on ADB. The most important contribution of this research is that the ADB phenomenon generally occurs at all levels and types of education, male and female. The motivation that drives ADB can be contrary to their understanding of it. Strengthening academic ethics is an important option, and changes in assessment design need to be considered by teachers so they do not rely on one type of test to determine student graduation. Based on the findings of the study 3, it revealed that plagiarism is the most reported ADB in the writing assignment. The results of checking the similarity level at 10 WKS and 1 mini paper showed that some students plagiarized from other sources without paraphrasing and writing down the sources. A certain percentage of students commit plagiarism at low, medium and high levels. The Turnitin software used to check students' writing results has proven to help show students' mistakes and they try to paraphrase in the next writing assignment. The use of WKS is claimed to help students increase their knowledge of lecture material, this can be seen from the effect based on the results of checking pre-test and post-test scores.
The contribution of this research involves theoretical, practical and methodological. Theoretically, these findings support and reinforce previous theories about the forms of ADB, influencing factors, and solutions to deal with them. In a practical contribution, this research encourages each university to make detailed guidelines on issues of academic integrity and penalties for those who violate them, besides that, lecturers must educate their students in every lecture, and use several technologies to reduce ADB. Meanwhile, the most important contribution of the research is methodologically because it has attempted to conduct comprehensive research from SLR research (study 1), cross correlational survey (study 2), and case studies (study 3). Moreover, teachers can learn to use modified forms of assessment to further reduce the potential for ADB to emerge among students. In terms of the widespread development of open AI which is easily accessible and used by students to support their lectures, practically lecturers must create guidelines on how to use open AI as one of the learning resources, but without relying on this technology.
Regarding the future research, it is critical to do additional research that can reach more respondents from other tertiary institutions that were not included in this study. Furthermore, study on the context of junior and senior high school pupils appears to be of interest. In the context of the plagiarism issue, as discussed in study 3 of this research, additional research can investigate differences in the effectiveness of using open AI in more diverse forms of assignments, as well as the impact on students' acquisition of cognition across various scientific fields in the long run. Another important issue for future research is the research into developing online courses on academic ethics for all students at various levels of education. It aims to promote academic integrity and minimize the ADB phenomenon.

摘要 iii
Abstract v
Acknowledgements vii
Table of Contents viii
List of Figures xii
List of Tables xiv
List of Appendices xvii
Chapter 1. Introduction 1
1.1 Motivation and Background 1
1.2 Research Objectives, Research Questions and Hypotheses 8
1.2.1 Research objectives 8
1.2.2 Research questions 9
1.2.3 Research hypotheses 11
1.2.4 Structure of writing 12
Chapter 2. Literature Review 14
2.1 Online Assessment 14
2.1.1 Concept and definition 14
2.1.2 Online assessment tools 15
2.1.3 Issues related to online assessment 16
2.2 Academic Dishonesty 17
2.2.1 Concept and definition 17
2.2.2 Kind of academic dishonesty 17
2.2.3 Associated factor toward ADB 22
2.2.4 Some reported efforts to reduce academic dishonesty 24
2.3 Theoretical framework 25
Chapter 3. Methods 27
3.1 Introduction 27
3.2 Study 1 Academic Dishonesty and Trustworthy Assessment on Online Learning 27
3.2.1 Planning of the review 28
a. Paper selection 29
3.2.2 Conducting the review 34
a. Coding and categorization 34
b. Synthesis and data analysis 35
3.2.3 Reporting the review 36
3.3 Study 2 Academic Dishonesty Phenomenon in Higher University Students in Indonesia 36
3.3.1 Research approach and design 36
3.3.2 Population and sample 38
3.3.3 Sampling technique 39
3.3.4 Research instruments 39
3.3.5 Data collection procedure 43
3.3.6 Data analysis techniques 45
3.4 Study 3 Case study: an exploration of ADB and Effect of Assessment Modification on
Learning Outcomes 47
3.4.1 Research approach and design 47
3.4.2 Research context 50
3.4.3 Research instrument 51
3.4.4 Data collection procedure 52
3.4.5 Data analysis technique 52
Chapter 4. Results 55
4.1 Introduction 55
4.2 Study 1 Result of Systematic Literature Review Study 56
4.2.1 Forms of AD in online assessment 57
4.2.2 Factors associated with ADB 61
4.2.3 Solutions to Reduce ADB and Increase TA 62
a. Technology-based approach 63
b. Pedagogical approach 65
4.2.4 Future research direction 67
4.3 Study 2 Result of Quantitative Correlational Survey Study 68
4.3.1 Description of the collected data 69
4.3.2 The phenomenon of ADB among Indonesian students 71
a. Results from semi-structured interviews with instructors and students 74
b. Results of semi-structured interviews about ADB with instructors 75
c. Results of semi-structured interviews about ADB with students 76
4.3.2 The driving motivation for ADB among Indonesian students 77
a. Motivation to do ADB from the instructor's perspective 79
b. Motivation to do ADB from the student's perspective 80
4.3.3 The propensity of Indonesian students toward ADB 81
a. Students’ Propensity toward ADB based on instructors’ perspective 83
b. Students’ Propensity toward ADB based on students’ perspective 84
4.3.4 Solutions to reduce ADB based on Students’ and Instructors’ Perspective 85
a. Solutions to reduce ADB based on instructors’ perspective 87
b. Solutions to reduce ADB based on instructors’ perspective 87
4.3.5 Normality, Multicollinearity, Heteroscedasticity, and Autocorrelation Test 89
a. Normality test 89
b. Multicollinearity test 90
c. Heteroscedasticity test 91
d. Autocorrelation test 92
4.3.6 Compare Means of Each Variable Based on Controlled Variable 93
a. Compare means some variables based on educational degree (undergraduate and
graduate) 93
b. Compare means some variables based on university location (west and east) 96
c. Compare means some variables based on type of university (public and private) 98
d. Compare means some variables based on gender (male and Female) 100
e. Compare means some variables based on age (under 21 and 21 and upper) 102
4.3.7 Multiple Linear Regression Test 104
4.3.8 T-test (partial) 104
4.3.9 F Test (simultaneous) 109
4.3.10 The coefficient of determination 110
4.4 Study 3 Case study: An Exploration of the ADB and Effect of Modification
Assessments Design on Learning Outcomes 111
4.4.1 Weekly Knowledge Sharing (WKS) Platform 111
4.4.2 Recapitulation of student engagement/contribution in WKS in each group 112
a. WKS Contribution of Group A 112
b. WKS Contribution of Group B 114
c. WKS Contribution of Group C 115
d. Comparison of WKS contributions from each group 116
4.4.3 Correlation between contributions in WKS to learning outcomes 117
4.4.4 WKS level similarity recapitulation of each group 118
a. WKS Similarity Level of Group A 118
b. WKS Similarity Level of Group B 120
c. WKS Similarity Level of Group C 121
d. Comparison of WKS similarity between group 123
4.4.5 Recapitulation of similarity level of student’s mini paper score 126
a. Similarity Level of Mini Paper Group A 126
b. Similarity Level of Mini Paper Group B 128
c. Similarity Level of Mini Paper Group C 129
d. Comparison of similarity level of mini paper each group 130
4.4.6 Improved learning outcomes through pre-mid and post tests 131
a. Learning Outcomes Report of Group A 132
b. Learning Outcomes Report of Group B 134
c. Learning Outcomes Report of Group C 136
d. Comparison of mean scores and STDEV data from group A, B, and C 138
e. Wilcoxon Signed-Rank Test Analysis Result 140
f. Measuring the Effect Size Using Z score of Wilcoxon Signed-Rank Test 143
g. Measuring the Effect Size Using Cohen’s d 144
4.4.7 Students’ Feedback toward WKS and Open AI Application 149
a. Student feedbacks toward the use of WKS and Open AI in course assignments 151
b. Visualization of student feedback recapitulation based on variables 151
Chapter 5. Discussions 157
5.1 Introduction 157
5.2 Discussion of Study 1: Academic Dishonesty and TA in Online Learning 158
5.2.1 Forms of ADB in online assessment 159
5.1.2 Factors associated with ADB 161
5.1.3 Solutions to reduce ADB and increase TA 163
5.1.4 Future research direction and implication for practice 169
5.3 Discussion of Study 2: Correlational Survey Study about ADB in Indonesia 172
5.3.1 Forms of ADB in online assessment 173
5.3.2 Motivation to Do ADB 174
5.3.3 Propensity of students toward ADB 175
5.3.4 Solutions to Reduce ADB 176
5.3.5 Cultural difference on ADB based on research experience study in Taiwan and survey data
from Indonesia 178
5.4 Discussion of Study 3: Case study: An Exploration of the ADB and Effect of Modification
Assessments Design on Learning Outcomes 181
5.4.1 Forms of ADB in an Empirical Online Assignments 181
5.4.2 Students’ Contribution toward WKS Assignments 184
5.4.3 Correlation between WKS Contributions toward Learning Outcomes 185
5.4.4 The effect of WKS combined with (Chat GPT, Chat Sonic and Non-Open AI) toward
Learning Outcomes 186
5.4.5 Students’ Feedbacks toward WKS and Open AI usage in Online Assignments 188
5.5 Theoretical, Practical and Methodological Contributions 189
Chapter 6. Conclusion 192
6.1 Conclusion 192
6.2 Future Research Directions 197
6.3 Recommendation for Practices 198
References 200
Appendices 217

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