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研究生(外文):Lee, Ming-Wei
論文名稱(外文):The impact of individual differences on visual analytics of an orchestration platform: An empirical study using eye-tracking
指導教授(外文):Lin, Yi-Ling
口試委員(外文):Chou, Yen-ChunWu, I-Chin
外文關鍵詞:learning analyticsgraph comprehensioninformation visualizationlearning goal orientationpaper-based assessmentclassroom orchestration technologyeye tracking
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本研究著重於探討學習目標導向、視覺化圖表格式(折線圖、柱狀圖、雷達圖)與學習類型(程序性學習及推論學習)對學生在線上複習平台中複習紙本程式考試表現的影響。我們透過使用者研究及眼動儀,探討自行開發的視覺化系統之可行性。此研究總共募集了34 位曾經至少修習過一堂 Java 程式設計課的受測者,並收集了問卷資料、系統紀錄、眼動追蹤數據等相關資料進行後續分析。我們的實驗透過使用迴歸模型驗證學習目標導向、視覺化圖表格式以及學習類型對於使用者在視覺化分析上認知的影響,進而提出以實證研究分析視覺化學習的可行性。我們的實驗結果顯示具有較高學習目標導向的使用者在視覺化分析的輔助下,相對應會有較高的學習表現與學習認知。然而實驗結果也顯示,雷達圖因為組成較為複雜,會對使用者複習時的效率有負面影響。在學習類型方面,實驗結果顯示在視覺化分析的輔助下,使用者在資訊檢索類型的複習表現較推理發想類型更為優越。
We examined the impact of learning goal orientation, visualization format (line, bar and radar chart) and type of learning task (search fact vs. inference generation) upon a viewer’s perception of reviewing paper-based exams in an online virtual assessment environment. A lab experiment was conducted with an eye-tracker. System log, eye-tracking data and questionnaires were collected from 34 students who have taken at least one Java programming course. Our experiments demonstrate the empirical research practicality by using a regression model to validate the effect of learning goal orientation, format and task on user perceptions of visualization analytics. Our results show that the viewers with a high degree of learning goal orientation would have better learning perception of visualization material. Radar graph, however, would have a negative influence on the review performance due to its complicated composition. We also found that with the assistance of visualization analytics, users perform more efficiently on search fact tasks rather than inference generation tasks when reviewing programming exams.
1-1 Background and Motivation 1
1-2 Research Questions 3
1-3 Research Method 5
2-1 Orchestration in Learning Analytics 8
2-2 Dashboards and Visualizations in Learning Analytics 10
2-3 Visual Analytics in Learning Environment 11
3-1 Learning Goal Orientation, Format and Task 18
3-2 Learning Comprehension 20
3-3 Understanding of Visualization 22
3-4 Perceived Learning 23
Chapter 4 METHODOLOGY 25
4-1 Dataset 25
4-2 System Development and Interface 26
4-3 Search Fact Tasks and Inference Generation Tasks 30
4-4 Apparatus 33
4-5 Subjects and Experiment Procedure 33
4-6 Analysis Method 37
5-1 User Behavior and Perception Data 39
5-2 Eye-tracking Data 45
6-1 Log-based User Behavior and Perception Data Analysis 51
6-2 Eye-tracking Data - Fixation Analysis 57
6-3 Eye-tracking Data - Transition Analysis 64
Chapter 7 DISCUSSIONS 66
7-1 The Influence on User Behavior and Perception 66
7-2 Eye Movement and User Behavior 70
Chapter 8 CONCLUSION 80
Appendix A: Visualizations with different format 94
Appendix B: Learning Goal Orientation Measurement Items 95
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