結合重新設計之翻轉教室模型與視覺化分析系統對於程式設計學習之影響

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廖柏瑄(Po-Shan Liao) 查詢紙本館藏

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網路學習科技研究所

論文名稱

結合重新設計之翻轉教室模型與視覺化分析系統對於程式設計學習之影響
(Effects of Re-designed Flipped Learning Model and Visual Analytics System on Programming Learning)

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至系統瀏覽論文 (2028-8-1以後開放)

摘要(中)

在高度數位化的環境，數位資訊科學教育越來越重要。然而，程式新手在傳統的教學方法中面臨了高失敗率與高輟學率的問題。因此，本研究設計了包含合作學習與調節學習的翻轉教室模型以及SPOCs的課程模式。此外，通過線上互動程式開發環境與學習管理平台蒐集學生的編程行為、影片點擊行為以及同儕互評結果，將學生的學習歷程以學習分析儀表板呈現，幫助教師與學生評估自己的學習並制定新的學習策略。
本研究實驗對象為北部某國立大學三十一位研究所學生，研究工具包含Python能力測驗、程式態度量表、程式自我效能量表、自我調節學習量表、學習動機量表以及問題導向任務反思問卷。學生於課前通過SPOCs影片與問題解決任務對課程內容進行複習，課程中通過程式專案共創、同儕互評進行合作學習，課程後提供學生學習分析儀表板，讓同組學生參考儀表板內容進行反思與學習策略的制定，並於最後填寫問題導向任務反思問卷來評估自己本次課程的表現。
研究結果表明，學生學習表現、程式態度、程式自我效能、學習動機以及自我調節學習中的任務策略、尋求幫助、自我評估面向都有顯著提升。通過分群演算法也發現自我調節前測分數高的學生經常觀看儀表板且學習表現成長幅度較大，程式態度消極的學生經常在觀看影片時調整影片速率且學習表現的成長幅度較大。根據序列分析的結果也發現學習表現高的學生經常在暫停及調整影片速率後將影片看完，也會在觀看影片過程中重複確認影片重點與內容。本研究所設計的課程模式及視覺化分析系統提升了學生程式學習的效果，並找出學習表現進步幅度較大學生的學習行為。未來可進一步確認實驗設計在大規模課程的效果。

摘要(英)

As Information technology continues to advance, Computer Science education is increasingly important. However, programming novices were still plagued by the high failure rate of conventional teaching methods. This paper has designed a class model that combines SPOCs with a re-designed Flipped classroom module containing knowledge construction and regulated learning. Moreover, by using a web-based interactive development environment and a Learning management system to collect students’ coding log, video watching log and peer review results, this study shows students’ learning process in Learning Analytics Dashboard (LAD), helping students and teachers evaluate and improve their learning.
In this study, 31 graduate students from a national university in northern Taiwan participated in the experiment for 18 weeks. The research tools in this study contain Python ability exam, programming attitude questionnaire, programming self-efficacy questionnaire, self-regulated learning (SRL) questionnaire, learning motivation questionnaire and problem-oriented mission reflection questionnaire. Students review the course content through SPOCs videos and problem-solving tasks before class, collaborative learning through the co-creation of problem-solving projects and peer evaluation in the class, watch the learning analytics dashboard and develop new learning strategies with group members after class.
The results indicate that students’ learning performance, programming attitude, programming self-efficacy, learning motivation and task strategy, self evaluate, and help-seeking skills have been significantly improved. Results of the clustering algorithm show that students with high self-regulated learning score in pre-test check out LAD frequently and have more remarkable growth in learning performance. Students with low programming attitude scores in pre-test often change the video rate and have greater learning performance growth. Sequence analysis also indicates that students with high learning performance usually watch until the end after pausing or adjusting the rate of the video. They also repeatedly check the index of the video. The class design and visual analytics system improve students’ programming learning. Future researchers can confirm the effectiveness of the research design in the massive learning environment.

關鍵字(中)

★ 程式教育
★ 翻轉教室
★ 調節學習
★ 師博課
★ 學習分析儀表板

關鍵字(英)

★ Programming Education
★ Flipped Classroom
★ Regulated Learning
★ SPOCs
★ Learning Analytics Dashboard

論文目次

中文摘要 i
Abstract ii
誌謝 iv
目錄 vi
圖目錄 x
表目錄 xii
一、緒論 1
1-1 研究背景與動機 1
1-2 研究目的 2
1-3 研究問題 3
1-4 名詞解釋 3
二、文獻探討 5
2-1 程式教育與問題導向學習 5
2-2 學習分析 6
2-2-1 學習分析 6
2-2-2 學習分析儀表板 7
2-2-3 學習分析和學習分析儀表板於程式教育 10
2-3 調節學習 11
2-3-1 自我調節學習 11
2-3-2 社會共享調節學習 13
2-3-3 共同調節學習 14
2-4 翻轉教室 15
2-4-1 翻轉教室 15
2-4-2 翻轉教室面臨的挑戰 16
2-5 線上影片學習相關研究 18
2-5-1 MOOCs（大規模開放式線上課程）的定義與問題 18
2-5-2 SPOCs（小規模私人線上課程） 20
2-5-3 影片學習行為分析相關研究 22
三、研究方法 24
3-1 研究設計 24
3-2 研究對象 24
3-3 實驗設計 25
3-4 研究工具 27
3-4-1 Python能力測驗 28
3-4-2 程式態度問卷 28
3-4-3 程式自我效能問卷 29
3-4-4 自我調節問卷 29
3-4-5 學習動機問卷 30
3-4-6 問題導向任務反思問卷 30
3-4-7 系統日誌與其他 31
3-5 分析工具與方法 32
3-5-1 問卷工具信度 34
3-6 學習表現分群 35
四、系統設計 37
4-1 視覺化分析系統總覽 37
4-2 編程行為儀表板 39
4-3 影片點擊儀表板 47
4-4 同儕互評儀表板 53
五、研究結果 58
5-1 Python能力測驗 58
5-1-1 學習表現差異 58
5-2 程式態度問卷 58
5-2-1 程式態度變化 59
5-2-2 課程設計與系統對高低學習表現學生的程式態度影響 60
5-3 程式自我效能問卷 60
5-3-1 程式自我效能變化 62
5-3-2 課程設計與系統對高低學習表現學生的程式自我效能影響 63
5-4 學習動機問卷 63
5-4-1 學習動機變化 64
5-4-2 課程設計與系統對高低學習表現學生的學習動機影響 65
5-5 自我調節學習能力問卷 65
5-5-1 自我調節學習能力變化 67
5-5-2 課程設計與系統對高低學習表現學生的自我調節學習能力影響 68
5-6 問卷量表間的關係 69
5-7 編程動作日誌分析 70
5-7-1 編程動作日誌統計 70
5-7-2 不同學習表現組的編程動作次數差異 70
5-7-3 編程動作與問卷量表的相關 71
5-8 學習管理系統操作日誌 72
5-8-1 學習管理系統操作日誌統計 72
5-8-2 不同學習表現組的儀表板觀看次數差異 72
5-8-3 儀表板觀看次數與問卷量表的相關 73
5-9 影片點擊行為 73
5-9-1 影片點擊行為統計 73
5-9-2 不同學習表現組的影片點擊行為次數差異 74
5-9-3 與學習能力的相關 74
5-10 開放式問卷結果 75
5-10-1 開放式問卷代號編碼規則 75
5-10-2 問題導向任務：對學生有幫助的環節 76
5-10-3 問題導向任務：最有幫助環節選擇原因 76
5-10-4 問題導向任務：儀表板的幫助為何 78
六、討論 80
6-1 編程動作不同之學生影片觀看行為差異 80
6-2 影片觀看行為不同之分群比較 82
6-2-1 影片觀看行為不同之學生編程行為差異 84
6-2-2 影片觀看行為不同之學習表現差異 86
6-3 儀表板觀看次數不同之學生影片觀看行為差異 87
6-4 學習動機提升主因 90
6-4-1 自我調節學習與內在目標導向 90
6-5 自我調節學習前測不同之分群比較 91
6-5-1 自我調節學習前測不同之儀表板觀看次數差異 91
6-5-2 自我調節學習前測不同之學習表現差異 92
6-6 程式態度前測不同之分群比較 94
6-6-1 程式態度前測不同之影片觀看行為差異 95
6-6-2 程式態度前測不同之學習表現差異 95
6-7 學習表現進行分群間的比較 97
6-7-1 行為序列分析工具與行為編碼 97
6-7-2 影片點擊行為序列分析 98
6-7-3 行為序列分析圖解讀 99
6-8 與相關研究的比較 100
6-8-1 與其他翻轉教室研究的相關比較 100
6-8-2 學習分析儀表板研究相關比較 103
6-8-3 與其他影片學習研究相關比較 104
6-8-4 總結 105
七、結論 108
7-1 研究結論 108
7-1-1 學生的學習能力顯著提升 108
7-1-2 學生學習動機、程式自我效能與態度、自我調節學習顯著相關 108
7-1-3 學生認為觀看影片在問題導向任務中最有幫助 108
7-1-4 統整作業、同儕互評以及學習分析儀表板提升學生自我調節學習 108
7-1-5 自我調節學習影響學習動機的提升 109
7-1-6 重複觀看影片、積極編程的學生，學習表現提升幅度大 109
7-1-7 均衡觀看儀表板的學生有較積極的影片觀看行為 110
7-1-8 高自我調節學生積極觀看同儕互評儀表板，進而提升學習表現進步幅度 110
7-1-9 態度消極學生對影片速率進行控制，進而提升學習表現進步幅度 110
7-1-10 完成觀看與重複確認內容的影片學習模式提升學習表現得分 111
7-2 研究限制 111
7-3 未來展望 111
參考文獻 113
附錄一、知情同意書（中文） 131
附錄二、知情同意書（英文） 133
附錄三、Python能力測驗（前測） 135
附錄四、Python能力測驗（後測） 142
附錄五、程式態度問卷 149
附錄六、程式自我效能問卷 150
附錄七、學習動機問卷 151
附錄八、自我調節學習問卷 152
附錄九、問題導向任務反思問卷 153

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指導教授

洪暉鈞(Hui-Chun Hung)

審核日期

2023-7-26

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