Steven Thomson
Research Subjects
Research Interests
Steven Thomson has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Abdur Rahim Mohammad Forkan
Swinburne University of Technology, Australia
1 shared publication Elizabeth Kollias
Vidversity
1 shared publication- Hung Du
Applied Artificial Intelligence Initiative (A2 I2), Deakin University, VIC 3216, Australia
1 shared publication - Natalie Wieland
VidVersity Australia
1 shared publication - Prem Prakash Jayaraman
Swinburne University of Technology
1 shared publication - Yong-Bin Kang
Swinburne University of Technology, Hawthorn, Australia
1 shared publication
Latest Publications
- Open Access
- 13 March 2023
- Published by International Association of Online Engineering (IAOE) in International Journal of Advanced Corporate Learning (iJAC)
- Vol. 16 (1) , 19-27
VideoDL: Video-Based Digital Learning Framework Using AI Question Generation and Answer Assessment
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.
- 13 March 2023
- Published by International Association of Online Engineering (IAOE) in International Journal of Advanced Corporate Learning (iJAC)
- Vol. 16 (1) , 19-27
Open AccessVideoDL: Video-Based Digital Learning Framework Using AI Question Generation and Answer Assessment
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.