Video Features for Predicting Knowledge Gain in Search as Learning

Abstract

While video platforms increasingly serve as primary learning resources during exploratory web searches, current approaches to predicting knowledge gain largely ignore video-specific features. This paper bridges this gap by examining how video interaction features (e.g., pausing, rewinding, forward navigation, viewing coverage) and video resource features (e.g., words per minute in speech transcripts, complex word ratios, and video file size density) correlate with learning outcomes. Using a publicly available dataset of 94 participants who engaged with educational videos during their search sessions, our analysis reveals that video interaction features, particularly those related to interaction frequency, are the strongest predictors of learning outcomes. Moreover, we analyze the influence of individual features on classification performance, revealing distinct relationships between different types of video interactions and knowledge gain. While our study is exploratory and based on a limited dataset, it provides valuable first insights and a foundation for future research on video-based learning behavior in search as learning settings. These insights can inform the design of adaptive learning systems that recognize and promote productive video engagement behaviors. To support future research, we release our feature extraction pipeline and analysis code1