博碩士論文 111524001 詳細資訊




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姓名 呂浚宏(Lu-Chun Hung)  查詢紙本館藏   畢業系所 網路學習科技研究所
論文名稱 基於自動問題生成與知識追蹤之適性化學習系統設計與應用:以 Python 程式設計學習為例
(Design and Application of an Adaptive Learning System Based on Automatic Question Generation and Knowledge Tracking: A Case Study of Python Programming Learning)
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摘要(中) 因應大數據時代下的人工智慧熱潮,程式設計與資料分析能力成為業界最欠缺的實 作技能之一。在教學過程中發現,教師在培養學生產業實戰程式設計能力時面臨諸多挑戰,例如無法確定學生是否有效吸收課堂所學知識、針對不同程度學生設計測驗題耗時 費工以及無法對學生知識缺陷提出適性化學習建議等問題。
為解決這些教學現場的問題,本研究欲導入所研發之適性化間隔學習系統,利用適 性化推題機制輔以知識追蹤技術,提供學生適合的學習內容,期望能提高學生的學習成效,並讓教師掌握學生的學習狀況。此系統將結合生成式人工智慧技術,例如自動摘要、自動出題和角色扮演等功能。其中,本研究讓生程式人工智慧模型 ChatGPT 嘗試扮演 Python 程式課老師,進行自動出題(Automatic Question Generation, AQG),減輕教師在設 計測驗題上的負擔。通過知識追蹤技術對每個問題進行知識點標記,根據學生的回答結果,自動調整後續問題的難度和內容,實現適性化學習。
本研究結果顯示,適性化學習系統確實能夠有效減輕教師負擔,提升學生學習效果,並促進教師與學生之間的互動。由 ChatGPT 所自動生成題目也具有與教師生成之題目品質相似,證實了 ChatGPT 作為教師角色的可行性;且透過結合知識追蹤的適性化推題機制,不僅使學生學習更加高效,也讓教師能夠更精確地了解學生的學習狀況,進而進行針對性的教學調整,這對於生程式人工智慧與現代教育的結合發展具有重要意義。
摘要(英) In response to the surge of artificial intelligence in the era of big data, programming and data analysis skills have become some of the most lacking practical abilities in the industry. During the teaching process, it was found that teachers face numerous challenges in developing students′ practical programming skills for the industry, such as being unable to determine whether students effectively absorb the knowledge taught in class, the time-consuming task of designing test questions for students of different levels, and the inability to provide tailored learning recommendations for students′ knowledge deficiencies.
To address these issues in the teaching field, this research intends to introduce a developed adaptive spaced learning system. This system uses an adaptive question-pushing mechanism supplemented by knowledge tracking technology to provide students with suitable learning content, aiming to improve students′ learning outcomes and help teachers understand students′ learning statuses. The system will integrate generative AI technologies, such as automatic summarization, automatic question generation, and role-playing functions. In particular, this research will have the generative AI model ChatGPT attempt to act as a Python programming course teacher, generating questions automatically (Automatic Question Generation, AQG) to reduce the burden on teachers in designing test questions. Through knowledge tracking technology, each question will be marked with knowledge points, and based on students′ answers, the difficulty and content of subsequent questions will be automatically adjusted to achieve adaptive learning.
The results of this study demonstrate that the adaptive learning platform effectively reduces the burden on teachers, enhances student learning outcomes, and fosters interaction between teachers and students. The questions automatically generated by ChatGPT are of similar quality to those created by teachers, confirming the feasibility of using ChatGPT in a teaching role. By integrating a knowledge-tracking adaptive question recommendation mechanism, the system not only makes student learning more efficient but also allows teachers to accurately understand students′ learning progress. This enables targeted teaching adjustments, which is significant for the integration of generative artificial intelligence and modern education.
關鍵字(中) ★ 適性化學習
★ 自動問題生成
★ 知識追蹤
★ 間隔學習
★ ChatGPT
關鍵字(英) ★ Adaptive Learning
★ Automatic Question Generation
★ Knowledge Tracing
★ Spaced Learning
★ ChatGPT
論文目次 中文摘要 ...i
Abstract...ii
誌謝 ...iv
目錄 ... v
圖目錄 ...viii
表目錄 ...ix
一、緒論 ... 1
1-1 研究背景與動機...1
1-2 研究目的...9
1-3 研究問題...3
1-4 名詞解釋...4
二、文獻探討 ...6
2-1 人工智慧...6
2-1-1 人工智慧 ... 6
2-1-2 教育中的人工智慧...7
2-1-3 自然語言處理與自動問題生成在教育上的應用 ... 8
2-1-4 生成式人工智慧 ... 9
2-1-5 生成式人工智慧在教育上的應用 ... 10
2-2 程式設計教學...11
2-2-1 運算思維 ... 11
2-2-2 程式設計教學 ... 12
2-3 知識追蹤與適性化學習...13
2-3-1 知識追蹤 .... 13
2-3-2 個人化學習 ... 14
2-3-3 適性化學習 ... 15
2-3-4 適性化學習的應用 ... 17
2-4 間隔學習(Space Learning) ... 18
2-4-1 間隔學習/間隔效應於教育中的應用 ... 18
2-4-2 測試效果/測試效應 ... 19
三、研究方法 .... 21
3-1 研究設計...21
3.2 研究對象與場域...21
3.3 實驗設計...21
3.4 研究工具...24
3-5 分析工具與方法...25
四、 系統設計 .... 28
4-1 系統簡介...28
4-1-1 系統使用情形 ...28
4-1-2 適性化間隔學習系統建置 ... 28
4-2 系統環境架構...29
4-3 系統處理流程...30
4-3-1 適性化推題機制...38
4-3-2 題庫說明...39
4-4 學習平台功能介紹...41
4-4-1 學習平台首頁...41
4-4-2 使用者介面 ...42
4-4-3 教師端介面 ... 45
4-4-4 管理者介面 ... 47
4-4-5 題目管理 ...48
4-5 教學目標與方法...48
4-6 各週課程進度與教學空間 ... 49
4-7 學生成績考核比例...50
五、研究結果 ... 51
5-1 Python 程式能力檢驗 ...51
5-2 考試焦慮...51
5-3 程式設計自我效能問卷(CPSES) ...52
5-4 開放式問題...53
5-5 機器生成題目之品質檢測...54
六、討論 ...58
6-1 學生練習頻率對每週測驗的影響...58
6-2 練習頻率對正確率的影響...58
6-2-1 學生練習頻率與正確率...58
6-2-2 部分學生答題情況...60
6-3 系統使用行為之探討...63
6-4 與其他AQG的研究的比較...64
6-5 間隔學習於學習語言研究相關比較...65
6-6 GenAI 於教育中的應用...65
6-7 透過知識追蹤的適性化學習...66
七、結論 ... 68
7-1 研究結論...68
7-1-1 適性化學習系統能夠有效率的提升學習成效 ... 69
7-1-2 機器自動生成的題目能夠在一定程度上取代教師生成的題目 .. 69
7-1-3 適性化學習能夠降低學生的考試焦慮 ... 69
7-2 研究限制...70
7-3 未來展望...71
參考文獻 ... 72
附錄一、知情同意書(學生)... 82
附件二、學習動機問卷 ... 86
附件三、程式設計自我效能問卷 ... 87
附件四、系統使用問卷 ... 88
附件五、開放式問卷 ... 89
附件六、Python 程式能力前測...89
附件七、Python 程式能力後測...94
附件八、機器生成題目判別 ... 98
附件九、課堂照片 ... 104
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指導教授 洪暉鈞(Hung-Hui Chun) 審核日期 2024-12-24
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