Automatic prediction of presentation style and student engagement from videos
Presentation style is an important dimension to be considered for delivering lectures or presentations. It affects the quality of the content delivery as well as the engagement of the students who consume the lectures, which is a key aspect of a learning environment. In this work, we investigate the...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Computers and Education: Artificial Intelligence |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666920X22000340 |
| _version_ | 1797987235101933568 |
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| author | Chinchu Thomas K.A.V. Puneeth Sarma Srujan Swaroop Gajula Dinesh Babu Jayagopi |
| author_facet | Chinchu Thomas K.A.V. Puneeth Sarma Srujan Swaroop Gajula Dinesh Babu Jayagopi |
| author_sort | Chinchu Thomas |
| collection | DOAJ |
| description | Presentation style is an important dimension to be considered for delivering lectures or presentations. It affects the quality of the content delivery as well as the engagement of the students who consume the lectures, which is a key aspect of a learning environment. In this work, we investigate the relationship between student engagement and the presentation style stressed by the speaker in an online learning environment. For this, we proposed automatic models based on deep learning to predict the presentation style (visual or verbal or balanced) from lecture videos and the student engagement from the emotional behavior of the students. The presentation style model performed with an accuracy of 86% at the frame level and 76% at the video level. The student engagement model resulted in an accuracy of 76% and an f1-score of 0.82 at the segment level and 95% accuracy and an f1-score of 0.97 at the video level in a binary classification setting. Also, the model resulted in a mean squared error of 0.04 at the segment level and 0.15 at the video level in a regression setting. The study to investigate the relationship between presentation style and student engagement showed that there is no statistically significant difference in the mean for student engagement with the presentation styles. We found that approximately 70% of the students are engaged in the considered online learning environment, irrespective of the presentation style. |
| first_indexed | 2024-04-11T07:45:39Z |
| format | Article |
| id | doaj.art-f91ff746c27f4541b9a5b800a4ccfdce |
| institution | Directory Open Access Journal |
| issn | 2666-920X |
| language | English |
| last_indexed | 2024-04-11T07:45:39Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computers and Education: Artificial Intelligence |
| spelling | doaj.art-f91ff746c27f4541b9a5b800a4ccfdce2022-12-22T04:36:20ZengElsevierComputers and Education: Artificial Intelligence2666-920X2022-01-013100079Automatic prediction of presentation style and student engagement from videosChinchu Thomas0K.A.V. Puneeth Sarma1Srujan Swaroop Gajula2Dinesh Babu Jayagopi3Corresponding author.; Multimodal Perception Lab, International Institute of Information Technology Bangalore (IIIT-B), Bangalore, 560100, Karnataka, IndiaMultimodal Perception Lab, International Institute of Information Technology Bangalore (IIIT-B), Bangalore, 560100, Karnataka, IndiaMultimodal Perception Lab, International Institute of Information Technology Bangalore (IIIT-B), Bangalore, 560100, Karnataka, IndiaMultimodal Perception Lab, International Institute of Information Technology Bangalore (IIIT-B), Bangalore, 560100, Karnataka, IndiaPresentation style is an important dimension to be considered for delivering lectures or presentations. It affects the quality of the content delivery as well as the engagement of the students who consume the lectures, which is a key aspect of a learning environment. In this work, we investigate the relationship between student engagement and the presentation style stressed by the speaker in an online learning environment. For this, we proposed automatic models based on deep learning to predict the presentation style (visual or verbal or balanced) from lecture videos and the student engagement from the emotional behavior of the students. The presentation style model performed with an accuracy of 86% at the frame level and 76% at the video level. The student engagement model resulted in an accuracy of 76% and an f1-score of 0.82 at the segment level and 95% accuracy and an f1-score of 0.97 at the video level in a binary classification setting. Also, the model resulted in a mean squared error of 0.04 at the segment level and 0.15 at the video level in a regression setting. The study to investigate the relationship between presentation style and student engagement showed that there is no statistically significant difference in the mean for student engagement with the presentation styles. We found that approximately 70% of the students are engaged in the considered online learning environment, irrespective of the presentation style.http://www.sciencedirect.com/science/article/pii/S2666920X22000340Presentation styleStudent engagementDeep learning modelsOnline learning |
| spellingShingle | Chinchu Thomas K.A.V. Puneeth Sarma Srujan Swaroop Gajula Dinesh Babu Jayagopi Automatic prediction of presentation style and student engagement from videos Computers and Education: Artificial Intelligence Presentation style Student engagement Deep learning models Online learning |
| title | Automatic prediction of presentation style and student engagement from videos |
| title_full | Automatic prediction of presentation style and student engagement from videos |
| title_fullStr | Automatic prediction of presentation style and student engagement from videos |
| title_full_unstemmed | Automatic prediction of presentation style and student engagement from videos |
| title_short | Automatic prediction of presentation style and student engagement from videos |
| title_sort | automatic prediction of presentation style and student engagement from videos |
| topic | Presentation style Student engagement Deep learning models Online learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666920X22000340 |
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