Video Analysis Engine for Predicting Effectiveness
Thareja, Rushil Dwivedi, Deep Garg, Ritik Baghel, Shiva Shukla, Jainendra Mohania, Mukesh
Thareja, Rushil
Dwivedi, Deep
Garg, Ritik
Baghel, Shiva
Shukla, Jainendra
Mohania, Mukesh
Supervisor
Department
Natural Language Processing
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
In the realm of digital education, the growing use of short-form online videos, coupled with innovative generative AI methods, has dramatically expanded the production of didactic academic videos. This shift, however, underscores a critical question - how to ascertain the "effectiveness" of these videos for student learning? It is essential to devise a classification mechanism that filters videos for clarity, comprehensibility, and their capacity to meet student learning objectives. The automated evaluation of these learning videos holds substantial implications for student academic performance. Accordingly, this paper presents a novel supervised-learning-based approach, predicated on video feature analysis, to predict the effectiveness of K-12 science and mathematics videos. Our method integrates diverse features such as image, spoken text, and audio, among other hand-crafted elements, to accurately assess video effectiveness. We conduct an evaluation of our approach using a comprehensive dataset comprised of 3,134 short-form academic videos. The results demonstrate robust performance, with the system achieving an accuracy of 76.1% and an F1 score of 80.6%.
Citation
R. Thareja, D. Dwivedi, R. Garg, S. Baghel, J. Shukla, and M. Mohania, “Video Analysis Engine for Predicting Effectiveness,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 15322 LNCS, pp. 97–112, 2025, doi: 10.1007/978-3-031-78312-8_7.
Source
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference
International Conference on Pattern Recognition
Keywords
AI for Education, Computer Vision, Deep Learning, E-learning, Multimodal Frameworks, Natural Language Processing, Video Processing
Subjects
Source
International Conference on Pattern Recognition
Publisher
Springer Nature