探討聊天機器人輔助程式教學系統與問題解決教學融入STEM程式課程的學習感受、學習行為與學習成效之研究

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王政鑫(Cheng-Hsin Wang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

探討聊天機器人輔助程式教學系統與問題解決教學融入STEM程式課程的學習感受、學習行為與學習成效之研究
(Study on Learning Perceptions, Behaviors, and Effectiveness of Chatbot Assisted Programming Teaching System with Problem-Solving Teaching in STEM Programming Courses)

★ 探討延展實境與運算思維融入數學與化學課程的學習感受、學習行為與學習成效之研究

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至系統瀏覽論文 ( 永不開放)

摘要(中)

STEM程式已成為國內大專院校必修課程，而問題解決教學法是適合培養學員的計算思維能力。本研究使用聊天機器人輔助程式教學系統與問題解決教學法融入STEM程式課程來提升學習效益，探討學生的學習成效、學習感受以及學習行為之間差異。
實驗對象為北部大專院校學生，實驗主題為程式設計課程。實驗活動之後，蒐集測驗、問卷、訪談以及系統平台的操作紀錄等數據。依據前測將受試者分為三組進行比較學習成效差異，分析學習感受與學習行為間的相關性。接著，使用機器學習技術分析學習感受與學習行為對答題驗證的結果。
實驗結果發現大部分學生對於程式設計有一定程度的背景知識，部分學生注重實作能力，並不在意理論知識，所以成績提升有限；低先備與高先備知識組認知過程相似，學習感受問卷的成就、組織、參考等向度與學習行為有顯著的相關性，成就向度分數高的會重複觀看題目並提交程式碼驗證，組織向度分數高的會嘗試不同解題方法，參考向度分數高的課程隨堂測驗表現較好；在此研究中，監督式學習中SVM表現最好，其F1 score為0.758。使用與學習感受問卷向度相關性高的特徵進行分類後，其F1 score為0.74，這表示說可以藉由去除相關性較低的特徵實現資料降維，發現最好的STEM程式課程的教育資料探勘方法。

摘要(英)

STEM programming education has become a required course in domestic universities, and problem-solving instructional methods are suitable for cultivating students′ computational thinking ability. This study integrates chatbot-assisted instructional systems and problem-solving instructional methods into the STEM programming course to enhance learning performance, and we investigate the differences in students′ learning effectiveness, perceptions, and behaviors.
The experimental subjects are college students in Northern Taiwan, and the theme of the experiment is the STEM programming course. After the experiment, we collected data from tests, questionnaires, interviews, and system logs. According to the pre-test, the students were divided into three groups to compare the differences in learning effectiveness and analyzed the correlation between learning perceptions and behaviors. Next, we analyze the learning perceptions and learning behaviors using machine learning techniques to compare the results of verifying answers.
Results show that most students have a certain degree of background knowledge in STEM programming. Moreover, some students focus on practical abilities and do not care about theoretical knowledge, so their programming performance is limited. The cognitive process of low and high prior knowledge groups is similar. In the learning perception questionnaire, the effort, organization, and reference dimensions of students are significantly correlated with students′ learning behaviors. The high effort score of students can repeatedly improve the programming questions and submit the answer several times for validation. The high organization score of students can try different solutions, and the high reference score of students can get better programming performance in quizzes. In this study, the SVM performs the best in all machine learning algorithms, and the F1 score of SVM is 0.758. After classifying the highly correlated features on the learning perception questionnaire, the F1 score of SVM is 0.74. This finding indicated that the ideal dimension reduction is realized by removing low correlated features on learning perceptions. Finally, we find the best educational data mining approach in STEM programming courses.

關鍵字(中)

★ 聊天機器人
★ STEM
★ 問題解決教學方法
★ 學習行為
★ 學習成效
★ 學習感受

關鍵字(英)

★ Chatbot
★ STEM
★ problem-solving teaching
★ learning behavior
★ learning effectiveness
★ learning perceptions

論文目次

摘　　要 i
Abstract ii
誌謝 iv
目　　錄 v
圖目錄 viii
表目錄 ix
第一章緒論 1
1.1研究背景 1
1.2目的 2
1.3論文架構 2
第二章　文獻探討 3
2.1 程式教育 3
2.2 STEM程式教育 5
2.3 問題解決教學融入程式教育 6
2.4 聊天機器人融入程式教育 8
2.5教學評量 9
2.6教育資料探勘 11
2.7研究問題 13
第三章　系統設計 14
3.1問題解決程式教學平台系統設計 14
3.2 問題解決程式教學平台系統結合現有程式教學系統 17
3.3實作課程教學平台系統開發 19
3.4 LINE程式教學機器人系統開發 21
3.5問題解決程式教學平台系統資料庫設計 22
第四章研究方法 23
4.1 實驗對象 23
4.2 實驗教材 23
4.3 實驗程序 25
4.4 實驗工具 27
4.4.1 測驗試卷 27
4.4.2 STEM學習感受問卷 28
4.4.3 實作課程教學平台系統使用感受問卷 29
4.4.4 LINE程式教學機器人系統使用感受問卷 30
4.4.5 課程分組訪談 30
4.5 數據搜整編碼 31
4.6學習行為數據分析 32
4.6.1 監督式學習 33
4.6.2 非監督式學習 35
4.6.3 機器學習分類評估指標 36
第五章研究結果 38
5.1 學生先備知識學習成效分析 38
5.2 LINE平台與實作課程平台的使用感受差異 41
5.3 學生學習行為與學習感受 42
5.3.1 學習感受問卷描述性統計 43
5.3.2 ITSA提交次數統計 44
5.3.3 學習行為紀錄與學習感受問卷間的關聯性 45
5.4 學習行為紀錄訓練集與測試集的分割比例 47
5.4.1 Week1學習行為紀錄數據訓練集與測試集的分割比例 47
5.4.2 Week2學習行為紀錄數據訓練集與測試集的分割比例 49
5.4.3 Week3學習行為紀錄數據訓練集與測試集的分割比例 51
5.4.4 Week4學習行為紀錄數據訓練集與測試集的分割比例 53
5.4.5 Week5學習行為紀錄數據訓練集與測試集的分割比例 55
5.5 機器學習分類結果 57
5.6 訪談結果 61
第六章結論與建議 62
6.1結論與討論 62
6.2建議 64
參考文獻 65
英文部分 65
中文部分 72
附錄一、問題解決程式教學平台系統資料庫表格 74
附錄二、研究實驗同意書 81
附錄三、資料型態與輸入輸出實作課程教材 82
附錄四、遞迴與函式實作課程教材 84
附錄五、指標與動態記憶體實作課程教材 87
附錄六、動態規劃實作課程教材 89
附錄七、類別繼承實作課程教材 91
附錄八、學習成效前後測 93
附錄九、STEM學習感受問卷 95
附錄十、實作課程教學平台系統使用感受問卷 97
附錄十一、LINE 程式教學機器人使用感受問卷 98

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指導教授

蘇育生(Yu-Sheng Su)

審核日期

2020-8-20

推文