Exploring Explainable Artificial Intelligence in Active Video Watching
DOI:
https://doi.org/10.58459/icce.2024.4824Abstract
Active Video Watching supports engagement through scalable interventions, such as notetaking in the form of comments. Machine Learning is used to categorize comments based on their quality to provide personalized feedback to students. In previous work on AVW-Space, an online portal for active video watching, a machine learning model was trained using data from several studies on presentation skills. In this paper, we explore the effectiveness in assessing the comment quality of this model in Face-to-Face Meeting Communication skills in comparison to a model trained specifically for this soft skill. We used Explainable Artificial Intelligence to identify and compare the important features of the models. Results show the need for comment quality assessment models to be specific to the soft skill in question and show major differences between their important features, highlighting the necessity to create a model specific to a particular soft skill.