在線上學習環境下以腦波資料偵測認知負荷之研究

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研究生：	高千敏 Kao, Chien-Min
論文名稱：	在線上學習環境下以腦波資料偵測認知負荷之研究 Mental Effort Detection Using EEG Data in E-learning Contexts
指導教授：	林福仁 Lin, Fu-Ren
口試委員:	徐茉莉 Galit Shmueli 雷松亞 Ray, Soumya
學位類別：	碩士 Master
系所名稱：	科技管理學院 - 服務科學研究所 Institute of Service Science
論文出版年：	2015
畢業學年度：	103
語文別：	英文
論文頁數：	93
中文關鍵詞：	大規模網路公開課程、腦電圖、決策樹、支援向量機、類神經網路
外文關鍵詞：	MOOC (Massive Open Online Course), Classification Tree
相關次數：	點閱：1 下載：0
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在網際網路盛行下，線上學習已經是一個普遍的學習模式。大規模網路公開課程（MOOCs）是線上學習中的熱門議題，其公開且透過網路教授課程的特性，使大量的人數可以同時註冊一門課，如何了解學生的學習狀態並據以改善他們在學習平台上的服務體驗變得重要。本研究利用資料探勘技術分類人腦電波資料，希望藉由偵測使用者於線上學習時的腦波狀態，準確分類是否對於課程不理解，讓授課者和使用者可以據以調整學習內容並增進學習成效。本研究採用商業型腦波儀器，測試32個受試者在觀看線上學習影片時的腦波資料，並以兩種資料集、兩種正規化方式、兩種時間窗口、兩種類別標記方式組合，產生的十六個資料模型，訓練並測試決策樹、支援向量機、類神經網路三種分類器，以分類準確度、精確度和查全率來衡量分類結果。本研究測試的結果，產生了一個比過去精確度更高的腦波分類器來分辨學習的狀態我們認為這個研究提供了一個可以應用在真實情境的良好的資料處理方式，可以輔助使用者和授課者在線上學習的過程當中了解不理解之處並據以改進，以提高學習成效。

E-learning becomes an alternative learning mode since the prevalence of the Internet. Especially, the advance of MOOC (Massive Open Online Course) technology enabled a course to accommodate tens of thousands of online learners. How to improve learners’ online learning experiences on MOOC platforms becomes a crucial task for platform providers. This research adopts EEG technology to detect learners’ learning states while they are watching videos in online e-learning activities, hoping to improve their learning outcomes. In this research, we built a system to capture and tag the mental states while subjects are watching online videos and use different normalization methods and time windows to process the data obtained from EEG devices. Finally, we used different supervised learning algorithms to train and test the classifiers and evaluate the results. The results proved that we provide an efficient data processing way to train classifiers and obtain the high accuracy rate comparing with that of previous researches. We consider this system can facilitate users’ self-awareness of learning states in an efficient way while they are in online e-learning activities, and improve their experiences in MOOC platforms.

Table of Contents
Chapter 1 Introduction    1
1.1 Research Background    1
1.1.1 E-learning and Massive Open Online Courses (MOOCs)    1
1.1.2 Electroencephalography (EEG) and Portable EEG Device    2
1.2 Research Motivations    3
1.3 Research Objectives    4
Chapter 2 Literature Review    5
2.1 Massive Open Online Courses    5
2.1.1 Definition and Features    5
2.1.2 The Benefits and Challenges of MOOCs    6
2.2 Electroencephalography    7
2.2.1 Electroencephalography    7
2.2.2 Prior Researches on EEG Classification    11
2.3 Design Science Approaches    14
Chapter 3 Research Methodology    16
Chapter 4 Experimental Design    18
4.1 Subjects    18
4.2 Equipment and Environmental Settings    18
4.3 Experimental Procedure    19
4.4 Materials    24
Chapter 5 Data Analysis    25
5.1 Data Description    25
5.2 Data Preprocessing    26
5.2.1 Global and Local Normalization Methods    26
5.2.2 6 Seconds and 8 Seconds Time Windows    27
5.2.3 1, 4, and 5 Second State Labeling    28
5.2.4 Over-sampling    30
5.3 Explanation of 16 Models    30
5.4 EEG Data Classification    32
5.5 Evaluation    33
Chapter 6 Experimental Results    34
6.1 Classification Results in 16 Models    34
6.2 Comparing the Results between Category A and B Datasets    38
6.3 Comparing the Results in Different Normalization Methods    40
6.4 Comparing the Results in Different Time Windows    41
6.5 Comparing the Results in Different State Labeling    42
6.6 ANOVA Tests on Different Algorithms    43
6.7 Test on Different Datasets    48
6.8 Summary of Evaluation Results    51
Chapter 7 Discussion    53
7.1 Application Scenarios    53
7.2 Limitations and Future Works    54
Chapter 8 Conclusion    56
References    58
Appendix A    62
Appendix B    78

Table of Tables
Table 2.1. Summary of different bands    8
Table 5.1. Feature set and examples    26
Table 5.2. Category A: Original Dataset    31
Table 5.3. Category B: Adjusted Dataset    31
Table 5.4. Settings in classification tree nodes in Orange    32
Table 5.5. SVM parameters in Orange    32
Table 5.6. Settings in Neural Network nodes in Orange    33
Table 5.7. Confusion Matrix    33
Table 6.1. Data size in two classes in Models A1 to A8    35
Table 6.2. Classification results in Models A1 to A8    36
Table 6.3. Data amount in two classes in Models B1 to B8    37
Table 6.4. Classification results in Models B1 to B8    38
Table 6.5. p-value of t-testing on two different datasets    39
Table 6.6. p-value of t-testing on two different normalization methods    40
Table 6.7. p-value of t-testing on two different time windows    41
Table 6.8. p-value of t-testing on two different state labeling    42
Table 6.9. The statistic distribution of accuracy rate of Model A3    43
Table 6.10. The ANOVA of accuracy rate of Model A3    43
Table 6.11. The statistic distribution of accuracy rate of Model A4    44
Table 6.12. The ANOVA of accuracy rate of Model A4    44
Table 6.13. The statistic distribution of accuracy rate of Model A7    44
Table 6.14. The ANOVA of accuracy rate of Model A7    45
Table 6.15. The statistic distribution of accuracy rate of Model A8    45
Table 6.16. The ANOVA of accuracy rate of Model A8    45
Table 6.17. Data size in two classes in Model A2    48
Table 6.18. Classification accuracy in Models A2    48
Table 6.19. p-value of t-testing on different training methods    49
Table 6.20. Data size in two classes in Model A4    50
Table 6.21. Classification accuracy in Models A4    50
Table 6.22. p-value of t-testing on different training methods    51

Table of Figures
Figure 2.1. Information Systems Research Framework (Hevner et al., 2004)    15
Figure 3.1. The design of EEG classification system    16
Figure 4.1. Experimental Procedure    19
Figure 4.2. The system snapshot of choosing a video    21
Figure 4.3. The system snapshot of instructing subjects to close their eyes    21
Figure 4.4. The system snapshot of watching a video    22
Figure 4.5. The system snapshot of instructing subjects to remove the EEG device    22
Figure 4.6. The system snapshot of asking subjects to answer questions    23
Figure 4.7. Playing raining sounds for one minute    23
Figure 5.1 Segmenting 10 seconds videos into five 6 seconds epochs    28
Figure 5.2 Segmenting 10 seconds videos into three 8 seconds epochs    28
Figure 5.3. 1 second state labeling under 6 second time window    29
Figure 5.4. 4 second state labeling under 6 second time window    29
Figure 5.5. 1 second state labeling under 8 second time window    30
Figure 5.6. 5 second state labeling under 8 second time window    30
Figure 6.1. Classification Tree Graph    47
Figure 7.1. An application Scenario    54


                                

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