以深度知識追蹤模型應用於程式學習系統

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

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姓名

李秉翰(Ping-Han Lee) 查詢紙本館藏

畢業系所

網路學習科技研究所

論文名稱

以深度知識追蹤模型應用於程式學習系統
(Application of Deep Knowledge Tracing Model to Support Programming Learning System)

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至系統瀏覽論文 (2027-8-1以後開放)

摘要(中)

近年來各國對於程式教育逐漸重視，程式設計能力已成為未來競爭的關鍵能力之
一。過去學生在程式教育課程中常因遇到困難無法解決，教師也不易瞭解學生在程式設
計學習過程中所遇到的問題與困境，導致學生學習成就與動機降低。目前眾多教育資料
探勘研究多著重於學生課程最終通過與否的預測，而透過深度知識追蹤可以針對學生的
學習軌跡進行知識建模，了解學生當下的知識掌握程度，協助學生針對弱點進行改善。
本研究以臺灣北部某國立大學研究所課程進行實驗，研究對象共計20 人，為期18
周。本研究結合深度知識追蹤開發一個程式設計教學輔助系統，並對學生課程中所累積
的數據進行預測，將成果即時的呈現於儀表板中，以幫助學生與教師了解學生學習行為
以及對於各項知識點的掌握程度，並提供相對應之學習建議。在資料分析方面則透過課
程專用伺服器，蒐集學生於程式編輯平台上所操作的日誌以及隨堂測驗的答題資料，並
透過深度知識追蹤進行預測。同時，檢視深度知識追蹤運用在程式設計課堂上的效果以
及是否能有效的協助學生進行學習。
研究結果發現，透過程式設計教學輔助系統，學生程式能力獲得顯著進步，運算思
維以及程式設計學習動機與學習成果呈現顯著正相關；深度知識追蹤能有效運用於程式
設計課堂中，而且針對學生進行不同知識點能力評估；於學習歷程中發現，學習表現較
差的學生有著較低的學習動機與較低的編程練習投入，授課教師可透過學習歷程數據主
動對學生提供協助。未來研究可參照本研究結果，進一步結合開源線上程式能力評量系
統，達到自動化辨識知識點的目標。

摘要(英)

In recent years, more and more countries have paid attention to program education. As a
result, programming ability has become one of the most critical competencies in the future. In
the past, it was hard for teachers to find and understand the problems that students face in the
coding process, resulting in reduced learning achievement and motivation. Currently, many
educational data mining studies focus on the dropout rate of students. Through deep knowledge
tracing, we can model students′ learning trajectories, understand their current knowledge level,
and help students overcome their weaknesses.
This study was conducted at a national university in northern Taiwan. A total of 20
graduate students participated in the experiment for 18 weeks. This study combines deep
knowledge tracing to develop a program learning system. The system supports the predictions
based on the data accumulated from students′ learning processes. The system dashboard can
immediately help students and teachers understand students′ learning behavior and mastery of
various knowledge points and provide corresponding learning suggestions. Students′ logs on
the integrated development environment and pop quizzes are collected for data analysis, and
predictions are made through the deep knowledge tracing technique. It also examines the
effectiveness of deep knowledge tracing in programming classes and whether it can effectively
assist students in learning.
The results show that students′ program ability has been significantly improved in this
study. Both computational thinking and learning motivation have a significant positive
correlation with learning outcomes. Deep knowledge tracing can effectively be used in
programming class to evaluate students′ abilities according to their different knowledge points.
The students with lower learning performance have lower learning motivation and engagement
in programming practice. Teachers can actively assist students with learning records about the
learning process. Future researchers can refer to this study by combining the open-source online
judge system to identify knowledge points automatically.

關鍵字(中)

★ 學習分析
★ 教育資料探勘
★ 深度知識追蹤
★ 程式設計教育
★ 視覺化儀表板

關鍵字(英)

★ Learning analysis
★ Educational data mining
★ Deep knowledge tracing
★ Programming education
★ Dashboard

論文目次

目錄
中文摘要 i
英文摘要 ii
誌謝 iv
目錄 v
圖目錄 ix
表目錄 x
一、緒論 1
1-1 研究背景與動機 1
1-2 研究目的 3
1-3 研究問題 3
1-4 名詞釋義 3
二、文獻探討 5
2-1 教育資料探勘 5
2-1-1 學習管理系統 5
2-1-2 教育資料探勘 6
2-1-3 程式教育資料探勘 7
2-2 學習預測與深度知識追蹤 9
2-2-1 學習成效的預測 9
2-2-2 知識追蹤 11
2-2-3 深度知識追蹤 12
2-3 程式設計學習動機 15
2-3-1 學習動機 15
2-3-2 程式設計學習動機 15
2-4 運算思維 17
三、研究方法 20
3-1 研究對象 20
3-2 研究流程 21
3-2-1 第一階段(2021年6月至2021年8月)：課程設計與教學輔助系統建置 22
3-2-2 第二階段(2021年9月至2022年1月)：系統實測 22
3-2-3 第三階段(2022年1月至2022年4月)：資料探勘及分析 22
3-3 課程設計 23
3-4 研究工具 26
3-4-1 程式能力測驗與隨堂測驗 26
3-4-2 程式能力專案測驗 29
3-4-3 學生背景問卷 30
3-4-4 程式設計學習動機問卷 31
3-4-5 運算思維問卷 33
3-4-6 創新教學計劃問卷 35
3-4-7 課後問卷 37
3-5 資料分析 38
3-5-1 成績部分 38
3-5-2 知識點部分 39
3-5-3 學生學習編程log相關紀錄 42
3-5-4 問卷結果 43
3-5-5 分析方法 44
四、系統設計與實作 46
4-1 系統環境架構 46
4-2 學習管理系統 48
4-2-1 答題記錄蒐集 49
4-2-2 資料視覺化儀表板 50
4-3 程式編程日誌資料蒐集系統 53
4-3-1 log系統操作日誌蒐集套件 54
4-4 深度知識追蹤 56
五、研究結果 58
5-1 程式設計學習成效 59
5-2 程式設計學習動機問卷 60
5-2-1 程式設計學習動機與學習表現成績的相關 61
5-2-2 程式設計學習動機與學習歷程的相關 62
5-3 運算思維問卷 62
5-3-1 運算思維問卷與學習表現成績的相關 63
5-4 深度知識追蹤預測結果 64
5-4-1 深度知識追蹤預測學生能力結果與學習表現成績的相關 64
5-4-2 運用深度知識追蹤對能力專案考表現進行加權預測的相關 65
5-5 學生答題知識點分群相似與成績 66
5-6 學習歷程特徵於不同學習表現學生間之差異 71
5-6-1 程式能力專案測驗不同之學生的特徵差異 71
5-6-2 程式能力專案測驗不同之學生的運算思維與程式設計學習動機差異 73
六、討論 74
6-1 學生認知作業難度、參與度與表現的關係 74
6-2 學習歷程特徵於不同儀表板觀看重點學生間之差異 77
6-3 學生問卷調查結果 81
6-3-1 教學輔助系統對學生的幫助 81
6-3-2 結合視覺化儀表板可增進學生了解學習歷程 82
6-3-3 程式設計學習動機與運算思維的相關 83
6-4 與其他知識追蹤相關研究比較 83
七、結論 85
7-1 研究結論 85
7-1-1 教學輔助系統能有效給予學生學習幫助及提升運算思維 85
7-1-2 深度知識追蹤及知識點應用於程式教育可有效預測學生各項能力 86
7-1-3 藉由學生程式編輯歷程可建立學生預警機制 86
7-2 研究限制 87
7-3 未來展望 87
參考文獻 89
附錄一、程式能力測驗前測 99
附錄二、程式設計學習動機量表 105
附錄三、運算思維量表 107
附錄四、學生背景問卷 109
附錄五、課程隨堂測驗題目 110
附錄六、教學創新計畫期中問卷 114
附錄七、程式能力專案測驗 117
附錄八、課後問卷 124
附錄九、知情同意書 125

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指導教授

洪暉鈞(Hui-Chun Hung)

審核日期

2022-7-26

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