Exploring the relationship between Self-Reported Feedback and students’ achievement in Video-Based Learning

This study explores the relationship between self-reported feedback and learning outcomes in video-based learning (VBL) environments. We analyzed data from 32 university students who engaged with a video on machine learning using the Evoli video annotation tool. Participants provided self-reported feedback on their prior knowledge, understanding, and perceived di-culty of the subject matter, while video interaction logs tracked their behaviour. Correlation analysis revealed a strong relationship between self-reported prior knowledge and pre-test scores (0.67) and a moderate relationship between perceived di-culty and mental e3ort (0.58). However, a weaker correlation was found between self-reported understanding and post-test scores (0.37), suggesting that con7dence in understanding does not always correspond to better achievement. K-means clustering identi7ed three distinct learner pro7les: “Careful Viewers”, who demonstrated signi7cant improvement despite low initial knowledge; “Super7cial Viewers”, who showed no improvement despite high self-reported understanding; and “Regular Viewers”, who exhibited steady progress with moderate e3ort. These 7ndings highlight the complexity of the learning process, showing that while self-reported feedback provides valuable insights into student engagement, it may not fully predict learning outcomes. The study emphasizes the need to consider multiple dimensions of engagement when evaluating VBL e3ectiveness and suggests avenues for future research to improve instructional design in digital learning environments.