  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.
