電腦程式設計已經成為數位時代中，邁向成功的關鍵因素之一，有助於將人類與科技之間的距離最小化。然而，學習電腦程式設計會因其邏輯及數學知識上的複雜性，而十分困難，特別是對於非電腦科學相關科系的學生而言。

為了探知非電腦科學相關科系學生的學習歷程，本研究使用教育數據探勘技術，並使用網頁做為途徑，以檢驗學生們的線上學習行為模式。學習行為分群方面，使用群集分析技術，可以根據學生們的線上學習行為模式，將學生分成不同的群體；學習行為模式探勘方面，採用循序樣式探勘技術來探索非電腦科學相關科系學生的學習行為模式。

群集分析結果共生成三個群集：「各面向皆積極使用」、「只積極使用學習資源」、「各面向皆少量使用」三組。結果表明，在概念學習及程式實作的學習成就上，組間並無顯著差異；但在進階程式實作部分，組間具有顯著差異。事後比較檢定顯示，「各面向皆積極使用組」的表現明顯優於「各面向皆少量使用組」。

關於線上學習行為模式的研究結果還另有發現，在「只積極使用學習資源組」及「各面向皆少量使用組」之間，有十大相似行為，而「各面向皆積極使用組」與另外兩組不同。此外，變遷圖顯示，「各面向皆積極使用組」具有最高的作業訂正率，證明此為決定學業成功的重要因素。

以上研究結果，可使用於深入了解非電腦科學相關科系學生，如何學習電腦程式設計；以及供教師及開發人員，在改進程式課程或系統設計時作為參考。

Computer programming has become one of the key factors that lead to success in this digital era, it helps minimise the frontier between technology and human beings. Nevertheless, especially those whose majors are not Computer Science (non-CS majors), learning computer programming is challenging because of its logical and mathematical complications.

To explore non-CS major students’ learning process, this study used educational data mining techniques and web-based approaches to examine students’ online learning behavioural patterns. This research implements a clustering technique to differentiate students into groups based on their online learning behavioural patterns, and a sequential pattern
mining technique to explore non-CS majors’ learning patterns.

There were three clusters generated: the active use, the resource use, and the less use group. The results indicate that there was no significant difference in learning achievement in conceptual and coding scores. However, in terms of advanced coding scores, a significant difference was found among the clusters. Post-hoc tests revealed that the active use group outperformed the less use group significantly.

The findings, regarding online learning behavioural patterns, also revealed that they were identified that top ten frequently occurred behaviours appeared similarly between the resource use group and the less use group; the active use group, however, differed from the others. Transitional diagrams additionally suggested that the active group had the highest rate of assignments revisions is proved to be an important factor to determine academic success.

Ultimately, these findings can be used to acquire deep understanding on how non-CS majors learn computer programming, as well as to provide instructors and developers with the insightful information to improve computer programming learning courses and systems.

1 Introduction 1
1.1 Research Background 1
1.2 Research Objectives 7
1.3 Research Questions 8
1.4 Definition of Terms 8
1.5 Clarification of Variables 9
2 Literature Review 11
2.1. Computer Programming Learning for Non-CS Majors 11
2.2. Computer Programming Learning Performance 14
2.3. Online Learning Behaviour and Clustering 16
2.4. Online Learning Behavioural Patterns 19
2.5. Sequential Pattern Mining (SPM) 21
3 Methods 25
3.1 Research Design and Participants 25
3.2 Research Hypotheses 25
3.3 Research Procedures 27
3.4 Instruments 28
3.4.1 Learning Content 28
3.4.2 Achievement Evaluation 29
3.5 Peer Interaction Programming Learning System (PIPLS) 35
3.6 Data Collection and Analysis 37
3.6.1 Data Collection and Pre-processing 37
3.6.2 Online Learning Behaviour Analysis 38
3.6.3 Computer Programming Learning Performance 39
3.6.4 Behavioural Patterns Analysis 40
4 Results and Discussion 47
4.1 Online Learning Behaviour Analysis 47
4.2 Behavioural Pattern Analysis 56
5 Conclusion 69

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