A deeper understanding of student preferences for in-class video use: a segmentation analyses of needs, group differences and preference clusters

Abstract

This study analyzed segment differences of student preference for video use in lecture classes and university use of video lecture classes. The authors then conducted novel gap analyses to identify gaps between student segments' preferences for videos versus their level of exposure to in-class videos. Multivariate analysis of variance (MANOVA) was used to identify significant factors that explain the gaps. Segment differences of student preference for video use in lecture classes and university use of video lecture classes were analyzed. Novel gap analyses were then conducted to identify gaps between student segments' preferences for videos versus their level of exposure to in-class videos. MANOVA was used to identify significant factors that explain the gaps. Gap analysis of video preference relative to video exposure showed a bimodal distribution, with an approximately even split between students with an overall deficit (44.5%) and surplus (47%) of in-class videos. Deficit means students preferred to see more videos than what the lecturer showed them. Surplus means the lecturer showed students more videos than they preferred to see. Further analyses break down the deficits and surpluses based on the type of videos shown. Results are useful as an effective diagnostic tool for education managers because they are not at the individual student level but rather by course level. One implication for educational managers is that a one-size-fits-all approach for all courses will benefit some students and annoy others. This paper extends Alpert and Hodkinson’s (2019) findings by identifying preference clusters and performing segmentation analyses based on finer-grained disaggregated data analysis.