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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 蔡欣穆 | zh_TW |
| dc.contributor.advisor | Hsin-Mu Tsai | en |
| dc.contributor.author | 林松逸 | zh_TW |
| dc.contributor.author | Song-Yi Lin | en |
| dc.date.accessioned | 2023-09-22T16:33:43Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-09-22 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-14 | - |
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(2016). Convolutional pose machines. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 4724–4732). Whitehill, J., Serpell, Z., Lin, Y.-C., Foster, A., & Movellan, J. R. (2014). The faces of engagement: Automatic recognition of student engagementfrom facial expressions. IEEE Transactions on Affective Computing, 5(1), 86–98. Zhao, Y., Lofi, C., & Hauff, C. (2017). Scalable mind-wandering detection for moocs: A webcam-based approach. In Data driven approaches in digital education (pp. 330–344). Springer. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89890 | - |
| dc.description.abstract | 了解學生上課時的理解程度變化有助於提高學習效率。若不想影響課程進行，可使用穿戴式的感測器或相機等設備測量學生的反應、透過機器學習模型來分析資料並估計理解程度。然而和理解程度的變化頻率相比，這類資料集往往包含過少的標註資訊。因此我們提出了能夠處理大量資訊的系統，以有效建立含有充足標註資訊的資料集。此外我們實際利用這個系統收集了一個包含身體姿勢、臉部表情和視線軌跡的資料集，用於訓練機器學習模型及預測理解程度。結果顯示，即便在不使用所有資訊的情況下，測出的理解程度仍有可靠的準確度，充分展現了我們所提出的系統被廣泛採用的可能性。 | zh_TW |
| dc.description.abstract | Measuring students’ mental states, such as their understanding during class, helps im prove learning efficiency. Automatic approaches implement this idea without interrupting the class by sensing students’ reactions through devices such as wearable sensors or cam eras, and applying machine learning models to analyze the data. However, most of the previous works lack adequate annotations of understanding based on students’ reactions compared to the number of concepts conveyed during lessons. In this paper, we propose a scalable framework for efficiently constructing and annotating datasets. Additionally, we have collected a dataset consisting of posture, facial expression, and eye movement features, and benchmarked it for measuring understanding. The results show promising accuracy even in cases where not all features are available, demonstrating the potential for widespread adoption of the proposed framework. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-22T16:33:43Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-09-22T16:33:43Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i
摘要 iii Abstract v Contents vii List of Figures ix List of Tables xi Chapter 1 Introduction 1 Chapter 2 Related Work 5 Chapter 3 System Design 9 3.1 Environments and Settings 10 3.1.1 Video Selections 10 3.1.2 Hardware and Software 11 3.2 Collecting Process 13 3.3 Dataset 15 3.3.1 Eye Tracking Data 16 3.3.2 Webcam Recordings 17 3.3.3 Keyboard Events 17 3.3.4 Annotations 18 3.4 Features Preprocessing 19 3.4.1 Eye Movement 19 3.4.2 Posture 20 3.4.3 Facial Expression 21 3.5 Models 21 3.5.1 Support Vector Machine 22 3.5.2 Long Short-Term Memory 22 3.5.3 Self-Attention 24 Chapter 4 Implementation 25 4.1 Dataset Partitioning 25 4.2 Model Architecture 26 4.2.1 SVM 27 4.2.2 LSTM 28 4.2.3 Self-Attention 29 Chapter 5 Evaluation 31 Chapter 6 Conclusion 41 References 43 | - |
| dc.language.iso | en | - |
| dc.subject | 理解程度 | zh_TW |
| dc.subject | 影音數位學習 | zh_TW |
| dc.subject | 分類 | zh_TW |
| dc.subject | Video-Based Learning | en |
| dc.subject | Understanding | en |
| dc.subject | Classification | en |
| dc.title | 課程影片理解程度測量 | zh_TW |
| dc.title | Measuring Understanding in Video-Based Learning | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 許永真;李明穗;石美倫 | zh_TW |
| dc.contributor.oralexamcommittee | Yung-Jen Hsu;Ming-Sui Lee;Mei-Lun Shih | en |
| dc.subject.keyword | 影音數位學習,理解程度,分類, | zh_TW |
| dc.subject.keyword | Video-Based Learning,Understanding,Classification, | en |
| dc.relation.page | 46 | - |
| dc.identifier.doi | 10.6342/NTU202303507 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-08-14 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊工程學系 | - |
| dc.date.embargo-lift | 2028-08-07 | - |
| Appears in Collections: | 資訊工程學系 | |
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| File | Size | Format | |
|---|---|---|---|
| ntu-111-2.pdf Until 2028-08-07 | 1.72 MB | Adobe PDF |
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