Investigating the Effectiveness of Explanations in Active Video Watching

Abstract

The use of video-based learning (VBL) has increased in recent years due to its effectiveness in distance learning. However, challenges in VBL, such as passive learning and low levels of engagement, continue to exist. Active Video Watching (AVW) addresses these challenges by supporting students’ engagement and learning via features such as personalized nudges and visualizations. Furthermore, the AVW platform used in our research utilizes an AI model to classify comments students write on videos into several comment quality categories. Explanations of how comments are classified may enable students to understand the comment quality and, subsequently, write better comments. To determine whether explanations of comment quality would benefit students, we conducted a quasi-experimental study wherein we compared data collected from software engineering students at the Ateneo de Davao University (control group, 55 students using the AVW platform without explanations) to data collected from a comparable course at the University of Canterbury (experimental group, 45 students who received explanations). This study included a survey on students’ perceptions of explanations. Results show that students in the experimental group, especially those who accessed explanations for multiple comments, wrote more higher-quality comments. Results of the survey show students are satisfied with explanations, particularly regarding the clarity, correctness, and the level of detail of explanations. This research contributes to using explanations in VBL platforms to increase learning and engagement.