Selected papers
Eight papers I'd point to first, grouped by theme. Shared first authorships are marked with a caret (^).
Rate of forgetting as an individual-differences construct
Sense, F., Behrens, F., Meijer, R. R., & van Rijn, H. (2016). An individual's rate of forgetting is stable over time but differs across materials. Topics in Cognitive Science, 8, 305–321. DOI · OSF · PDF
A learner's rate of forgetting is stable across weeks, but the same person forgets vocabulary, country flags, and maps at measurably different rates. Adaptive systems should therefore carry per-item rate-of-forgetting estimates across sessions rather than collapsing them into a single trait.
Sense, F., Meijer, R. R., & van Rijn, H. (2018). Exploration of the rate of forgetting as a domain-specific individual differences measure. Frontiers in Education, 3:112. DOI · OSF · PDF
Rate of forgetting is essentially uncorrelated with working-memory capacity and general cognitive ability, suggesting it captures a kind of individual variation that standard IQ-style measures don't pick up on.
Zhou, P., Sense, F., van Rijn, H., & Stocco, A. (2021). Reflections of idiographic long-term memory characteristics in resting-state neuroimaging data. Cognition, 212. DOI · bioRxiv · GitHub
A learner's rate of forgetting—estimated from a short fact-learning session—can be predicted from their resting-state brain connectivity, indicating the parameter reflects a stable neural trait rather than task noise.
Adaptive learning, from lab to deployment
van der Velde, M., Sense, F., Borst, J. P., & van Rijn, H. (2021). Alleviating the cold start problem in adaptive learning using data-driven difficulty estimates. Computational Brain & Behavior. DOI · PsyArXiv · OSF
Addresses a practical deployment problem: adaptive systems behave poorly in their first few minutes with a new learner because they have no prior data. The paper shows that item-difficulty estimates aggregated from prior learners are enough to warm-start new users meaningfully.
Sense^, F., van der Velde^, M., & van Rijn, H. (2021). Predicting university students' exam performance using a model-based adaptive fact-learning system. Journal of Learning Analytics, 1–15. DOI · OSF
In-session rate-of-forgetting estimates from an adaptive fact-learning system predict university students' exam grades weeks later—which reframes adaptive practice as a source of formative-assessment data, not only an instructional tool.
Wilschut, T., Sense, F., & van Rijn, H. (2025). Modality matters: Evidence for the benefits of speech-based adaptive retrieval practice in learners with dyslexia. Topics in Cognitive Science, 17(1), 57–72. DOI
Learners with dyslexia benefit disproportionately from speech-based adaptive retrieval practice compared to typing-based practice. Modality of response is a real lever for equity in adaptive learning technology, not a cosmetic choice.
Cognitive modeling and machine learning for real-world skills
Sense, F., Wood, R., Collins, M. G., Fiechter, J., Wood, A., Krusmark, M. A., Jastrzembski, T., & Myers, C. W. (2021). Cognition-enhanced machine learning for better predictions with limited data. Topics in Cognitive Science. DOI
Uses a cognitive model of memory, the Predictive Performance Equation, to engineer timing-related input features for a gradient-boosted decision trees model. The cognitive model's narrow but specialized knowledge about the temporal dynamics of learning and forgetting jump-starts the ML model, yielding better predictions than the default model, especially when training data are limited.
Maass^, S. C., Sense^, F., Gluck, K., & van Rijn, H. (2019). Keeping bystanders active: Resuscitating resuscitation skills. Frontiers in Public Health, 7:177. DOI · OSF · PDF
Only 2% of adults trained years earlier could still perform CPR to guideline standard, but a six-minute video refresher with brief practice restored 81% to competency. Argues empirically for routine short refreshers over one-and-done certification for lifesaving skills.
For the full list, see Google Scholar.