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Online learning is in high demand due to benefits such as convenience, flexibility, cost efficiency, and improved accessibility. In online learning, video conferencing is an effective technology for collaboration and increasing online student engagement. This study is part of a larger study conducted using design-based research (DBR) to develop a video annotation tool using artificial intelligence (AI) methodologies such as machine learning and deep learning. This systematic literature review is the foundation of the process which identifies the characteristics and indicators of engaging teaching videos. The studies included in this systematic literature review have been gathered from seven databases and selected by applying inclusion/exclusion criteria in accordance with the Preferred Reporting Items for Systematic Reviews. From the selected studies, we identified, categorised, and explained the characteristics and indicators of engaging teaching videos based on teachers’ behaviours and movements. In this study, we identified 11 characteristics and 47 associated indicators of the characteristics critical in enhancing student engagement. Teachers and higher education institutions can use these characteristics and indicators as a benchmark to improve the quality of engaging teaching videos and later improve teaching and learning. In the final stage of DBR, the identified indicators can be used to train a machine learning tool, a form of AI. This tool can provide a report on engaging teaching videos by highlighting the teachers’ behaviours and movements.
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