Predicting affective states during e-learning : using deep neural networks
E-learning has recently taken over the conventional method of learning, i.e., classroom lectures. E-learning, being a one-way dialog, doesn’t give feedback to the teacher on how well they are doing. This area has been hardly studied where personalized and adaptive learning systems are implemented du...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/10356/74086 |
| _version_ | 1826125352721711104 |
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| author | Dalmia, Sachin |
| author2 | Jagath C. Rajapakse |
| author_facet | Jagath C. Rajapakse Dalmia, Sachin |
| author_sort | Dalmia, Sachin |
| collection | NTU |
| description | E-learning has recently taken over the conventional method of learning, i.e., classroom lectures. E-learning, being a one-way dialog, doesn’t give feedback to the teacher on how well they are doing. This area has been hardly studied where personalized and adaptive learning systems are implemented during an e-lecture. To monitor the learner’s facial expressions, e-learning experiments have been conducted on participants and their webcam videos and self-reports are gathered. This is done in the context of four CE7412 lectures. The data is then pre-processed to make it suitable for training. The TVL/1 algorithm is applied to extract the optical flows and frames for each video. The neural networks are then trained using learner’s self-reports and his needs for Feedback and Slide Improvement. In this paper, temporal segment networks (two-stream ConvNets) are implemented to predict the learner’s affective states and their needs for Feedback and Slide Improvement. Personalized learning models trained using learner’s self-reports generalize unseen data and gain capacities for prediction of Affective States, Feedback and Slide Improvement. |
| first_indexed | 2024-10-01T06:35:14Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/74086 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:35:14Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/740862023-03-03T20:58:06Z Predicting affective states during e-learning : using deep neural networks Dalmia, Sachin Jagath C. Rajapakse School of Computer Science and Engineering DRNTU::Engineering E-learning has recently taken over the conventional method of learning, i.e., classroom lectures. E-learning, being a one-way dialog, doesn’t give feedback to the teacher on how well they are doing. This area has been hardly studied where personalized and adaptive learning systems are implemented during an e-lecture. To monitor the learner’s facial expressions, e-learning experiments have been conducted on participants and their webcam videos and self-reports are gathered. This is done in the context of four CE7412 lectures. The data is then pre-processed to make it suitable for training. The TVL/1 algorithm is applied to extract the optical flows and frames for each video. The neural networks are then trained using learner’s self-reports and his needs for Feedback and Slide Improvement. In this paper, temporal segment networks (two-stream ConvNets) are implemented to predict the learner’s affective states and their needs for Feedback and Slide Improvement. Personalized learning models trained using learner’s self-reports generalize unseen data and gain capacities for prediction of Affective States, Feedback and Slide Improvement. Bachelor of Engineering (Computer Engineering) 2018-04-24T05:57:42Z 2018-04-24T05:57:42Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/74086 en Nanyang Technological University 42 p. application/pdf |
| spellingShingle | DRNTU::Engineering Dalmia, Sachin Predicting affective states during e-learning : using deep neural networks |
| title | Predicting affective states during e-learning : using deep neural networks |
| title_full | Predicting affective states during e-learning : using deep neural networks |
| title_fullStr | Predicting affective states during e-learning : using deep neural networks |
| title_full_unstemmed | Predicting affective states during e-learning : using deep neural networks |
| title_short | Predicting affective states during e-learning : using deep neural networks |
| title_sort | predicting affective states during e learning using deep neural networks |
| topic | DRNTU::Engineering |
| url | http://hdl.handle.net/10356/74086 |
| work_keys_str_mv | AT dalmiasachin predictingaffectivestatesduringelearningusingdeepneuralnetworks |