VideoDL: Video-Based Digital Learning Framework Using AI Question Generation and Answer Assessment

Authors

  • Abdur Rahim Mohammad Forkan Swinburne University of Technoogy
  • Yong-Bin Kang Swinburne University of Technology
  • Prem Prakash Jayaraman Swinburne University of Technology
  • Hung Du Vidversity
  • Steven Thomson Vidversity
  • Elizabeth Kollias Vidversity
  • Natalie Wieland VidVersity

DOI:

https://doi.org/10.3991/ijac.v16i1.35207

Keywords:

Video-based Learning, Question Generation, Learning Assessment, Online Learning

Abstract


Assessing learners’ understanding and competency in video-based digital learning is time-consuming and very difficult for educators, as it requires the generation of accurate and valid questions from pre-recorded learning videos. This paper demonstrates VideoDL, a video-based learning framework powered by Artificial Intelligence (AI) that supports automatic question generation and answer assessment from videos. VideoDL comprises of various AI algorithms, and an interactive web-based user interface (UI) developed using the principles of human-centred design. Our empirical evaluation using real-world videos from multiple domains demonstrates the effectiveness of VideoDL.

Cover iJAC Vol. 16 No. 1 (2023)

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Published

2023-03-13

How to Cite

Forkan, A. R. M., Kang, Y.-B., Jayaraman, P. P., Du, H., Thomson, S., Kollias, E., & Wieland, N. (2023). VideoDL: Video-Based Digital Learning Framework Using AI Question Generation and Answer Assessment. International Journal of Advanced Corporate Learning (iJAC), 16(1), pp. 19–27. https://doi.org/10.3991/ijac.v16i1.35207

Issue

Vol. 16 No. 1 (2023)

Section

Short Papers

License

Copyright (c) 2023 Abdur Rahim Mohammad Forkan, Yong-Bin Kang, Prem Prakash Jayaraman, Hung Du, Steven Thomson, Elizabeth Kollias, Natalie Wieland

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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