Instructional videos are common resources used in STEM classrooms and, when designed well, can help students better understand complex STEM topics. Understanding how students cognitively engage with videos and how engagement differs according to individual student characteristics, like prior knowledge, can inform the design of instructional videos as well as maximize our understanding of how students learn with such resources in STEM contexts.
In this study, we explored digital event data to reveal common sequences of students’ video-watching behaviors that are indicative of cognitive engagement and how cognitive engagement relates to learning from instructional videos. To understand these relationships, we conducted three analyses: (1) sequence mining to reveal the most common groups of video-watching behaviors, (2) a path analysis to investigate how pre-test scores and video-watching behaviors predicted unit exam scores, and (3) a moderation analysis to examine whether the relationships between video-watching behaviors and unit exam scores was moderated by pre-test scores.
Method and Outcomes
One hundred twenty-eight biology undergraduate students learned with a series of instructional videos and took a biology course exam one week later. We conducted sequence mining on the digital events of students’ video-watching behaviors to capture the most common behavioral sequences that occurred when students watched instructional videos. Ultimately, 26 sequences emerged and were aggregated into four groups of behaviors: repeated scrubbing, speed watching, extended scrubbing, and rewinding. We tested whether students’ video-watching behaviors predicted course exam scores and prior knowledge moderated the relationship between behaviors and exam scores. Results indicated that for students with lower prior knowledge, there was a positive relationship between unit exam scores and frequency of speed watching or rewinding behaviors.
Taken together, these findings suggest the ways students engage with instructional videos varies as a function of individual characteristics, like prior knowledge, and that types of cognitive engagement are predictive of learning from instructional videos in STEM.
Robert Plumley, University of North Carolina at Chapel Hill (UNC-CH), NC; Monty Evans, UNC-CH, NC; Matthew Bernacki, UNC-CH, NC; Jeffrey A. Greene, UNC-CH, NC; Kelly Hogan, UNC-CH, NC; Mara Evans, UNC-CH, NC; Kathleen Gates, UNC-CH, NC; Abigail Panter, UNC-CH, NC