Digital learning environments provide rich and engaging experiences for students to develop different knowledge and skills. However, learning systems in these environments generally lack the capacity to assess student difficulties in realtime. The lack of timely assessment and guidance can result in unproductive floundering and associated frustration [3, 9]. Standard measures are mostly focused on the use of questionnaires, intervews or think-aloud protocols for capturing learners' subjective feedback on affect, mental-effort, perceived learning, and user preferences . Though these instruments are effective in capturing overall sentiments and reactions, they do not provide enough granularity to conduct detailed analyses on how specific parts of the lecture affect the learning experience.
|Number of pages||5|
|Journal||Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies|
|Publication status||Published - Sep 2019|
- Learning Analytics
- Online Learning
- Video Lectures