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1. Learning Mechanisms: Practice, Feedback, and Discovery
1. Learning Mechanisms: Practice, Feedback, and Discovery
We study the cognitive and motivational processes that drive learning, with a focus on how learners acquire knowledge through practice, feedback, and discovery rather than passive instruction. Using controlled experiments, we examine how learners infer abstract rules, regulate their learning, and retain knowledge over time—identifying when and why specific learning experiences lead to durable understanding.
Researchers: Michael Asher, Julia Conti, Gillian Gold
Selected Publications - Learning Mechanisms
Selected Publications - Learning Mechanisms
Asher, M. W., Sana., F., Koedinger, K.R., & Carvalho, P.F. (2025). Practice with Feedback vs. Lecture: Consequences for Learning, Efficiency, and Motivation. Journal of Applied Research in Memory and Cognition. Advance online publication. [link] [pdf]
Gold, G., Asher, M. W., & Carvalho, P. F. (under review). Well-calibrated intuitions, flawed judgments: Low post-instruction self-efficacy steers students away from efficient learning. osf.io/6bq4d_v1
2. Intelligent and Adaptive Learning Systems
2. Intelligent and Adaptive Learning Systems
We develop precise computational models of learning and use them to design AI-driven systems that adapt to individual learners. This research integrates learning science, data analytics, and human-centered design to model knowledge, generate practice and feedback, and personalize learning experiences at scale.
Researchers: Yumou James Wei, Meng Cao, Gillian Gold, Jess Turner
Selected Publications - Adaptive Systems
Selected Publications - Adaptive Systems
Wei, Y., Carvalho, P.F., & Stamper, J. (under review). Small but Significant: On the Promise of Small Language Models for Accessible AIED. https://arxiv.org/pdf/2505.08588
Cao, M., Yan, V. X., Sana, F., & Carvalho, P. F. (under review). Balancing Spacing and Repetition for Time-Constrained Learning. https://doi.org/10.31219/osf.io/f6xu2_v3
3. Learning at Scale and Across Contexts
3. Learning at Scale and Across Contexts
We translate cognitive and computational models into educational interventions that function in real classrooms and diverse learning contexts. This line of research examines how instructional designs and learning technologies perform at scale, across institutions, and across linguistic and cultural settings, with an emphasis on access, inclusion, and real-world impact.
Researchers: Phenyo Moletsane, Meng Cao, Michael Asher, Yumou James Wei
Selected Publications - Learning Across Contexts
Selected Publications - Learning Across Contexts
Asher, M.W., Kwon, C., Stamper, J., Ogan, A., & Carvalho, P.F. (2025) Validating a New Approach for Measuring Student Engagement in Remote, Low-Infrastructure Learning Environments. In Proceedings of the Twelfth ACM Conference on Learning @ Scale (L@S '25). Association for Computing Machinery, New York, NY, USA, 62–72. [link] [pdf]
Cao, M., & Carvalho, P. F. (under review). Adaptive Spaced Retrieval Practice in Algebra I: A Classroom-Based Study. https://doi.org/10.31219/osf.io/ge7vq_v1
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