博碩士論文 110522101 詳細資訊




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姓名 林文涵(Wen-Hen Lin)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 根據SHAP解釋模型提供基於Coding pattern干預以提升學習成效
(Enhancing Learning Effectiveness through Coding Pattern-based Interventions Provided by the SHAP Explanation Model)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2028-8-1以後開放)
摘要(中) 資訊科技日益普及,程式能力逐漸成為教育當中不可或缺的一部分,大學也開始發展多種資訊科學相關的課程,並在課程中使用各種系統作為學習環境,像是電子書系統、程式編譯環境、開放交流論壇等,讓學生可以不受到時間和地點的限制來學習程式。透過早期的研究可知,程式初學者容易有程式語法以及邏輯方面的學習困擾,而且可能無法自行從錯誤訊息中發現並解決,最終導致程式設計任務失敗,因此如何識別出學生的學習情況以及了解學生的行為面向並給予干預回饋成為重要的議題。
本研究從程式初學者的線上程式設計行為中識別出Coding patterns推斷學生的行為面向,以及計算早期研究使用過的程式設計統計數據作為特徵,進行學習成效預測,預測方法是使用機器學習方類方法,透過常見的預測模型評估因子找出最佳預測學習成效的模型。接著使用SHAP計算預測模型中Coding patterns的影響不同預測結果之程度及方向,亦即對於預測結果的貢獻度,最後設計出學生個人化干預內容,並呈現在學生個人干預儀表板作為基於Coding pattern的干預輔導來提升學生之學習成效,最後探討此個人化干預輔導的有效性。
摘要(英) Information technology is increasingly prevalent, and programming skills have gradually become an essential part of education. Universities have also started developing various courses related to computer science and using various systems as learning environments, such as e-book systems, programming compilation environments, and open communication forums, allowing students to learn programming without being restricted by time and location. Early research has shown that novice programmers often encounter difficulties in learning programming syntax and logic and may struggle to identify and resolve errors on their own, ultimately leading to failed programming tasks. Therefore, it is important to identify students′ learning situations and understand their behavioral aspects in order to provide intervention feedback.
In this study, coding patterns were identified from the online programming behavior of novice programmers to infer students′ behavioral aspects. Early research used programming statistics as features to predict learning outcomes. Machine learning methods were employed to predict learning effectiveness, and common prediction models were evaluated to find the best model for predicting learning outcomes. The SHAP (Shapley Additive Explanations) method was then used to calculate the influence of coding patterns in the prediction model on different prediction results, that is, their contribution to the prediction results. Finally, personalized intervention content was designed based on coding patterns and presented in a student′s personalized intervention dashboard to improve their learning outcomes. The effectiveness of this personalized intervention guidance was subsequently investigated.
關鍵字(中) ★ Coding pattern
★ 程式初學者
★ 干預輔導活動
★ SHAP
關鍵字(英) ★ Coding pattern
★ Programming novice
★ Intervention
★ SHAP
論文目次 摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
1. 緒論 1
2. 文獻探討 2
2.1. 程式設計行為樣態(Coding pattern) 2
2.2. 早期預測學習成效 3
2.3. 程式設計干預輔導 4
3. 基於Coding patterns的個人化干預 4
3.1. 回饋建議 5
3.2. 程式行為趨勢分析 6
3.3. 作答時間分析 7
3.4. 錯誤類型分析 8
4. 研究方法 9
4.1. 課程設計 9
4.2. 學習系統 9
4.3. Coding patterns 12
4.3.1. Coding pattern 1: 認真思考後才撰寫程式 17
4.3.2. Coding pattern 2: 具有程式先備概念,短時間就可概念精熟 17
4.3.3. Coding pattern 3: 基礎概念精熟 17
4.3.4. Coding pattern 4: 掙扎到提交通過 18
4.3.5. Coding pattern 5: 嘗試過後容易提交失敗 18
4.3.6. Coding pattern 6: 多照抄範例程式碼,缺乏除錯練習過程 18
4.3.7. Coding pattern 7: 缺乏除錯過程,容易提交失敗 18
4.3.8. Coding pattern 8: 語法困擾,掙扎後容易提交失敗 19
4.3.9. Coding pattern 9: 語法不熟悉造成邏輯困擾,掙扎後容易提交失敗 19
4.3.10. 計算學生在各概念章節主要出現哪些Coding patterns行為 19
4.4. 程式設計特徵 20
4.5. 學習成效預測模型 21
4.5.1. 資料前處理 21
4.5.2. 資料正規化 22
4.5.3. 交叉驗證 23
4.5.4. 資料重採樣 23
4.5.5. 預測模型效能評估 23
4.5.6. 預測模型結果 24
4.6. 個人化干預措施 25
4.6.1. 每周行為回饋建議 26
4.6.2. 考前個人化干預複習活動 29
5. 結果 30
5.1. 是否能從程式行為歷程資料中識別出不同的 Coding pattern 30
5.2. 通過學習成效預測模型可以提取哪些關鍵的Coding pattern 33
5.3. 接受基於Coding patterns的干預的學生能否比接受傳統複習活動的學生有更高的學習成績 37
6. 結論 39
7. 未來研究與限制 40
參考文獻 42
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指導教授 楊鎮華(Jhen-Hua Yang) 審核日期 2023-7-5
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