Examining the relationship between student performance and video interactions

Abstract

In this work, we attempted to predict student performance on a suite of laboratory assessments using students' interactions with associated instructional videos. The students' performance is measured by a graded presentation for each of four laboratory presentations in an introductory mechanics course. Each lab assessment was associated with between one and three videos of instructional content. Using video clickstream data, we define summary features (number of pauses, seeks) and contextual information (fraction of time played, in-semester order). These features serve as inputs to a logistic regression (LR) model that aims to predict student performance on the laboratory assessments. Our findings show that LR models are unable to predict student performance. Adding contextual information did not change the model performance. We compare our findings to findings from other studies and explore caveats to the null-result such as representation of the features, the possibility of underfitting, and the complexity of the assessment.

Tracking student engagement with open educational resources (OER) and online homework

“…Our work addresses the PERC 2016 call for methodical approaches to PER, including predicting and generalizing learning outcomes [8][9]. Previous work has explored correlations between video engagement and student performance showing little to no correlation [10][11]. We hypothesize our results may be different due to the customized nature of our videos and how much they are integrated into our curriculum.…”