應用指數平滑法實現短期學習成效預測與學習歷程儀表板系統建置

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黃浩軒(Hao-Hsuan Huang) 查詢紙本館藏

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網路學習科技研究所

論文名稱

應用指數平滑法實現短期學習成效預測與學習歷程儀表板系統建置
(Application of Exponential Smoothing for Short-term Learning Outcome Prediction and Development of Dashboard System for Learning Portfolio)

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至系統瀏覽論文 (2029-8-1以後開放)

摘要(中)

隨著資訊科技進步及網路發展，人們開始利用數位學習平台提升知識。知識追蹤是數位學習系統的核心，根據學習者的學習紀錄追蹤知識掌握程度並預測學習成效。但目前知識追蹤模型仍存在模型複雜及未考慮遺忘因素等問題。因此，本研究嘗試使用指數平滑法進行改善並預測學習成效及設計開發一個學習儀表板，讓學習者及教師迅速的透過視覺化分析瞭解學習狀態。
本研究採取問卷調查法透過科技接受模式以探討不同背景的學習者對於本研究所開發之視覺化學習儀表板ES-Dashboard之知覺有用性及知覺易用性差異，透過Suvery Cake網路問卷發放進行資料蒐集，回收有效樣本共計64份，藉由敘述性統計、單因子變異數分析等統計方法進行分析。研究結果顯示ES-Dashboard具備不錯之知覺可用性及易用性，且職業對學習儀表板的「知覺有用性」與「知覺易用性」有顯著影響；開放問題分析結果為學習者認為不僅可以在ES-Dashboard迅速了解當前的學習狀態，更可透過系統視覺化圖表快速掌握學習重點、分配學習時間並進行進度追蹤。
除此之外，模型驗證結果顯示本研究所提出基於指數平滑法的知識追蹤模型(ES)不論在整體或是分群情況下，模型指標RMSE(12.57<19.01)及MAE(11.2<17.61)皆優於傳統知識追蹤模型(DKT)。

摘要(英)

Along with the advance in information technology and the popularity of the internet, people have started using e-learning platforms to enhance their knowledge. Knowledge tracing is the core of digital learning systems, tracking learners′ knowledge mastery and predicting learning outcomes based on their learning records. However, current knowledge tracing models face issues such as complexity and neglect of forgetting factors. Therefore, this study attempts to use the exponential smoothing method to improve and predict learning outcomes and design and develop a learning dashboard, allowing learners and teachers to quickly understand learning statuses through visual analysis.
This study adopts a questionnaire survey method based on the Technology Acceptance Model to explore the differences in perceived usefulness and perceived ease of use of the visualized learning dashboard, ES-Dashboard, developed in this study among learners with different backgrounds. Data was collected through an online questionnaire distributed via Suvery Cake, with a total of 64 valid samples collected. The data was analyzed using descriptive statistics and one-way ANOVA. The results indicate that the ES-Dashboard has good perceived usability and ease of use, and that occupation significantly influences the perceived usefulness and perceived ease of use of the learning dashboard. Open-ended question analysis revealed that learners believe they can quickly understand their current learning status through the ES-Dashboard, and also quickly grasp key learning points, allocate learning time, and track progress through the system′s visual charts.
Furthermore, the model validation results indicate that the knowledge tracing model proposed in this study based on exponential smoothing (ES) outperforms the traditional knowledge tracing model (DKT) in both overall and subgroup scenarios, with RMSE (12.57 < 19.01) and MAE (11.2 < 17.61).

關鍵字(中)

★ 指數平滑法
★ 學習儀表板
★ 知識追蹤
★ 學習分析

關鍵字(英)

★ Exponential Smoothing
★ Learning Analytics Dashboard
★ Knowledge Tracing
★ Learning Analytics

論文目次

中文摘要 i
Abstract ii
致謝 iii
圖目錄 viii
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 研究流程 3
1-5 名詞解釋 4
二、文獻探討 6
2-1 數位學習 6
2-1-1 數位學習發展現況 6
2-1-2 數位學習適性化發展與挑戰 7
2-2 智慧家教系統 8
2-2-1 智慧家教系統與適性化 8
2-2-2 智慧家教與知識追蹤 9
2-3 知識追蹤 9
2-3-1 貝葉斯知識追蹤 10
2-3-2 深度知識追蹤 10
2-3-3 知識追蹤的延伸與應用 11
2-4 時間序列 12
2-4-1 移動平均法 12
2-4-2 指數平滑法 12
2-4-3 指數平滑法在教育領域相關研究與應用 13
2-5 學習分析 14
2-5-1 學習分析儀表板 14
2-5-2 學習分析儀表版在教育之應用 15
2-6 VARK學習風格 16
三、研究方法 18
3-1 知識追蹤模型建模與模型評估 18
3-1-1 實驗數據收集 19
3-1-2 資料前處理 19
3-1-3 測試集資料 20
3-1-4 訓練集資料 20
3-1-5 資料建模 20
3-1-6 模型調校 22
3-1-7 模型預測與評估 22
3-2 不同學習行為模式驗證 22
3-2-1 分群特徵選擇 22
3-2-2 產生分群 23
3-2-3 分群及群組命名 23
3-3 系統可用性及易用性分析 26
3-3-1 研究對象與方式 26
3-3-2 研究工具 26
3-3-3 問卷實施流程 27
3-3-4 統計分析方法 28
3-3-5 受測者編碼及原則說明 28
四、系統設計與實作 29
4-1 系統簡介 29
4-2 系統架構 29
4-3 系統功能介紹 32
4-3-1 登入頁面 32
4-3-2 系統功能介紹 32
4-3-3 題庫試題分布功能介紹 33
4-3-4 學生學習分析功能介紹 37
4-3-5 分群學習分析功能介紹 41
4-3-6 預測模型建置功能介紹 44
五、資料分析與結果 48
5-1 問卷資料分析 48
5-1-1 背景資料之敘述性統計 48
5-1-2 問卷量化分析 50
5-1-3 不同學習者背景對學習儀表板的知覺可用性及易用性之探討 53
5-1-4 問卷開放問題分析 54
5-1-5 問卷開放問題探討 58
5-2 不同答題模式分析 59
5-2-1 不同答題模式群組之前、後測之描述性統計 59
5-2-2 不同答題模式群組之共變數分析 59
5-3 模型預測與評估 61
5-3-1 所有樣本指數平滑最佳α值 61
5-3-2 所有樣本模型預測與評估 61
5-3-3 樣本分群之模型預測與評估 62
5-3-4 不同群組指數平滑最佳α值 63
六、討論 66
6-1 基於短期數據的知識追蹤模型 66
6-1-1 知識追蹤模型在短期學習數據的預測成效 66
6-1-2 知識追蹤模型在短期數據與不同學習模式之預測成效 67
6-1-3 不同學習模式在不同α值之學習成效預測表現 68
6-2 其它知識追蹤相關研究探討 69
6-2-1 指數平滑法預測學習成效相關研究 69
6-2-2 指數平滑法與深度學習模型相關研究 70
6-3 IRT系統相關研究 70
七、結論 73
7-1 研究結論 73
7-1-1 指數平滑法在短期學習數據預測成效顯著且優於傳統知識追蹤 73
7-1-2 指數平滑法知識模型對不同學習行為特徵群體在預測成效上無差異 73
7-1-3 基於指數平滑法的儀表板系統設計 74
7-1-4 學習者透過ES-Dashboard可快速掌握學習狀態並分配學習時間 74
7-2 研究限制 74
7-3 未來展望 75
參考文獻 77
附錄 89
附錄一問卷背景資料 89
附錄二科技接受模式問卷-知覺有用性 90
附錄三科技接受模式問卷-知覺易用性 91
附錄四問卷開放式問題 92
附錄五開放式問題1回覆內容 93
附錄六開放式問題2回覆內容 96
附錄七開放式問題3回覆內容 98

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指導教授

洪暉鈞(Hui-Chun Hung)

審核日期

2024-7-27

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