Learning analytics in synchronous online education: Making video conferencing more data-driven and interactivity-focused
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COVID-19 has been a turning point for online learning across the world, but the remarkable story has been the meteoric growth of synchronous or “live” online education. As instructors around the world have turned to video conferencing platforms to teach, a lot of instructor feedback has recently emerged on a variety of topics. One of the key emerging issues for instructors and institutions has been extracting and consolidating data on student engagement and learning outcomes. This paper attempts to identify and classify the types of learning data required to understand student learning behavior in synchronous (or “live”) online classes based on a multitude of factors such as immediacy, consolidation, and availability to different stakeholders at different points in time. Further, an attempt is made to assess how popular video conferencing platforms address the issues of data generation, presentation, and collation. Lastly, the paper also proposes an alternative approach that can potentially help create data in an easier, more efficient manner.
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