Tracing Need for Cognition in Digital Learning

Need for Cognition (NFC) reflects individuals’ tendencies to engage in and enjoy effortful cognitive activities and has been linked to positive academic outcomes (e.g., higher academic achievement). However, the extent to which NFC is expressed in actual learning behaviors remains unclear. Therefore, we investigated how NFC manifests in digital learning behaviors by analyzing behavioral trace data from undergraduate students in an online chemistry course across 4 periods (before Midterms 1 and 2, respectively, and before and after the final exam). We identified 20 behavioral indicators that were most strongly correlated with NFC and used them in supervised machine learning (ML) models to predict achievement (final course grade) and motivation (continued course interest). Interestingly, whereas NFC was correlated with interest but not achievement, NFC-related behaviors moderately predicted achievement but explained only little variance in interest. Building on the full set of > 700 behavioral indicators, ML models more strongly predicted academic achievement but were also less successful in predicting interest. Generally, greater overall activity, self-testing behavior, and lecture engagement were predictive of better performance. Interest was predicted by indicators reflecting behavioral variability. Importantly, NFC-related behaviors were not among the most predictive features for either outcome. This finding suggests that, although NFC has previously been linked to better academic functioning, its behavioral expressions might not align with the most effective digital learning patterns. Our study offers novel insights into NFC in digital learning and highlights the importance and challenges of using trace data to predict motivational outcomes, such as interest.