Publications

Preprints

Cao, M., Gold, G., & Carvalho, P. F. (under review). What Drives Learning During Practice? Contributions of Exposure, Retrieval, and Error Correction. https://doi.org/10.31234/osf.io/fspmq_v1

Cao, M., Ling, J., Pavlik, P., & Carvalho, P.F. (under review). What Should Be Spaced? Variability Constrains Spacing Effects In Classroom Learning. https://osf.io/preprints/psyarxiv/g97u4_v1

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

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

Carvalho, P.F., Asher, M.W., Sana, F. & Koedinger, K. R. (under review). What Gets Retrieved? Testing Effects In The Absence Of Prior Study https://osf.io/preprints/psyarxiv/sqjbc_v1

Wei, Y., Carvalho, P.F., Stamper, J. (2024). Uncovering Name-Based Biases in Large Language Models Through Simulated Trust Game.  https://arxiv.org/pdf/2404.14682

2026

Cen, X., Aleven, V., Koedinger, K.R., Borchers, C., & Carvalho, P.F. (in press). Self-Efficacy and Favorability Shape Learning from Tutoring Systems and Paper Practice. In Proceedings of EC-TEL 2026 [link]

Zhang, T., Gok, S., Gold, G., Carvalho, P.F. & Fyfe, E.R. (in press) Effectiveness and Efficiency of Multimedia Worked Examples vs. Practice with Feedback on Learning Problem Solving. Proceedings of the Annual Meeting of the Cognitive Science Society, 48

Cao, M., Gold, G., & Carvalho, P.F. (in press) What Drives Learning During Practice and Testing? Evidence for Distinct Roles of Exposure and Retrieval. Proceedings of the Annual Meeting of the Cognitive Science Society, 48

Gold, G., Tjaden, J., & Carvalho, P.F. (in press) Less Talk, More Code: Practice-Based Instruction Improves Programming Skill Acquisition. Proceedings of the Annual Meeting of the Cognitive Science Society, 48 [link]

Chen, E., Asher, M. W., Gold, G., Chen, W., & Carvalho, P. F. (2026). AI or human? An open-source SDK and dashboard for detecting outsourced responding. In E. G. Blanchard, G. Chen, M. Chi, & S. Isotani (Eds.), Artificial intelligence in education. Late breaking results, WideAIED, practitioners, industry and policies, blue sky, doctoral consortium, FoL workshops and tutorials, FoL invited papers (Communications in Computer and Information Science, Vol. 3031). Springer. [link]

Gill, J., Asher, M. W., & Carvalho, P. F. (2026). Investigating the Efficacy of Mastery-Based Tests in Fostering Effective Self-Regulated Learning Behaviors in CS1 Courses. In Proceedings of the 57th ACM Technical Symposium on Computer Science Education V.2 (pp. 1663–1664). Association for Computing Machinery. [link]

Shi. Y., Zhang, S., Brusilovsky, P., Price, T., Akram, B., Leinonen, J., Lan, A., Carvalho, P.F., Koedinger, K. R., & Barnes, T. (2026). 10th Educational Data Mining in Computer Science Education (CSEDM) Workshop. In E. G. Blanchard, G. Chen, M. Chi, & S. Isotani (Eds.), Artificial intelligence in education. Late breaking results, WideAIED, practitioners, industry and policies, blue sky, doctoral consortium, FoL workshops and tutorials, FoL invited papers (Communications in Computer and Information Science, Vol. 3033). Springer. [link]

Wei, Y., Moore, S., Carvalho, P. F., Stamper, J., Brooks, C., & Liut, M. (2026). Small language models for education: Opportunities, challenges, and a shared research agenda. In E. G. Blanchard, G. Chen, M. Chi, & S. Isotani (Eds.), Artificial intelligence in education. Late breaking results, WideAIED, practitioners, industry and policies, blue sky, doctoral consortium, FoL workshops and tutorials, FoL invited papers (Communications in Computer and Information Science, Vol. 3033). Springer. [link]

Cao, M. & Carvalho, P.F. (2026). Striking the Balance: How Variability Shapes Retrieval Practice and Worked Examples for Transfer Learning. Educational Psychology Review, 38:71. [link]

Lyu, Q., Borchers, C., Xia, M., Xiao, K., Carvalho, P.F., Koedinger, K.R., Aleven, V. (2026). Evaluating a data-driven redesign process for intelligent tutoring systems. In E. G. Blanchard, G. Chen, M. Chi, & S. Isotani (Eds.), Artificial intelligence in education: AIED 2026 (Lecture Notes in Computer Science, Vol. 16583). Springer. [link] [pdf]

Asher, M.W., Gold, G., & Carvalho, P.F. (2026) Benefit or Bottleneck? Assessing the Impact of Structured Reflection on Learning from AI-Driven Explanatory Feedback. In L@S '26: Proceedings of the Thirteenth ACM Conference on Learning @ Scale, pp. 1-11. Association for Computing Machinery. [link] 

Moletsane, P.P., Kwon, C., Stamper, J., Ogan, A., & Carvalho, P.F. (in press). Can Multilingual Environments Promote Scalable EdTech? Evidence from a Randomized Controlled Trial. In L@S '26: Proceedings of the Thirteenth ACM Conference on Learning @ Scale, pp. 287 - 296. Association for Computing Machinery. [link]

Kwon, C., Moletsane, P.P., Asher, M.W., Ouyang, D., Wang, L., Conejo, D.E., Stamper, J., Carvalho, P.F., & Ogan, A. (2026) Improving Accessibility and Quality of Learning through Multilingual Instruction in EdTech. In Proceedings of the 20th International Conference of the Learning Sciences (ICLS). Irvine, CA: International Society of the Learning Sciences. [link] [pdf]

Chen, E., Li, J., Huang, S., Tang, X., Lin, J., Carvalho, P.F., & Koedinger, K.R. (2026). AI Knows Best? The Paradox of Expertise, AI-Reliance, and Performance in Educational Tutoring Decision-Making Tasks. In LAK '26: Proceedings of the LAK26: 16th International Learning Analytics and Knowledge Conference, 736-743. [link] [pdf]

Hayashi, Y., Shimojo, S., Carvalho, P.F., & Koedinger, K.R. (2026). Active Learning Beyond Borders: PEOE Enhancement of Explanatory Understanding in Japanese Undergraduates. In LAK '26: Proceedings of the LAK26: 16th International Learning Analytics and Knowledge Conference, 491-502. [link] [pdf]

Wei, Y., Stamper, J., & Carvalho, P.F. (2026). Generate-Then-Validate: A Novel Question Generation Approach Using Small Language Models. In LAK '26: Proceedings of the LAK26: 16th International Learning Analytics and Knowledge Conference, 809-814. [link] [pdf]

Asher, M. W., Wei, Y., Reynolds, A. D., Ogan, A., & Carvalho, P. F. (2026). Will they try again? A large-scale RCT on scaffolds that support persistence in an intelligent tutoring system. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26). ACM. 🏆 **Honorable Mention Award** [link] [pdf]

Moletsane, P. P., Asher, M. W., Kwon, C., Carvalho, P. F., & Ogan, A. (2026). Inclusive mobile learning: How technology-enabled language choice supports multilingual students. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26). ACM. [link] [pdf]

Asher, M. W., Gold, G., Chen, E., & Carvalho, P. F. (2026). Chatbots Are Undermining Crowdsourced Research in the Behavioral Sciences: Detecting AI-Assisted Cheating with a Keystroke-Based Tool. Advances in Methods and Practices in Psychological Science, 9 (1), pp. 1-12. [link] [pdf]

Asher, M. W., & Carvalho, P. F. (2026). Conditions for Effective Learning Without Upfront Instruction: How Practice with Feedback Supports Memory, Generalization, Motivation, and Metacognition. Educational Psychology Review, 38 (12). [link] [pdf]

2025

Wei, Y., Carvalho, P.F., & Stamper, J. (2025). Small but Significant: On the Promise of Small Language Models for Accessible AIED. In Jiang, B. et al. (Eds.) (2025). In Proceedings of the 33rd International Conference on Computers in Education. Asia-Pacific Society for Computers in Education, 166-171. [link] [pdf]

Rachatasumrit, N., Koedinger, K. R., & Carvalho, P. F. (2025). Evidence and theory for why the best example-problem ratio to optimize learning gain depends on knowledge content. International Journal of Artificial Intelligence in Education. [link] [pdf]

Schuetze, B.A., Yan, V.X., & Carvalho, P.F. (2025). Capturing Session-to-Session Dynamics of Learning and Forgetting: A Test of Existing Knowledge Tracing Models. International Journal of Artificial Intelligence in Education. [link] [pdf]

Wei, Y., Carvalho, P.F. & Stamper, J. (2025). KCluster: An LLM-based Clustering Approach to Knowledge Component Discovery. Proceedings of the 18th International Conference on Educational Data Mining, 228--240. [link] [pdf]

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] 🏆

Asher, M. W., Hartman, J. D., Blaser, M., Eichler, J. F., & Carvalho, P. F. (2025). The Promise of Mastery-Based Testing for Promoting Student Engagement, Self-Regulated Learning, and Performance in Gateway STEM Course. Computers & Education, 235 (105387) [link] [pdf]

Borchers, C., Nguyen, H. T., Carvalho, P. F., Koedinger, K. R., & Aleven, V. (2025). Goal setting engages more caregivers in online math homework than instructional support. In K. Tammets, S. Sosnovsky, R. Ferreira Mello, G. Pishtari, & T. Nazaretsky (Eds.), Two decades of TEL: From lessons learnt to challenges ahead (Lecture Notes in Computer Science, Vol. 16063). Springer. [link] [pdf]

Lekshmi-Narayanan, A.B., Asher, M.W., Brusilovsky, P., & Carvalho, P.F. (2025). Can Motivated Students Do More Activities? In Proceedings of the 9th Educational Data Mining in Computer Science Education (CSEDM) Workshop, EDM 2025, Palermo, Sicily, Italy. CEUR Workshop Proceedings, Vol. 4019. [link]

Farzan, F., Wang, C., Ankney, R.L., & Carvalho, P.F. (2025). Modeling the Role of Graphical and Textual Complexity in Geometry Problem-Solving Using SEM. Proceedings of the 2nd International Conference on Education Research (ICER 2025), Lisbon, Portugal. [link] [pdf]

Borchers, C., Nguyen, H. T., Carvalho, P. F., Koedinger, K. R., & Aleven, V. (2025). Involving parents in tutoring systems to increase content confidence: A design probe study. In A. I. Cristea, E. Walker, Y. Lu, O. C. Santos, & S. Isotani (Eds.), Artificial intelligence in education: AIED 2025 (Lecture Notes in Computer Science, Vol. 15882). Springer. [link]

Borchers, C., Peng, C., Lyu, Q., Carvalho, P. F., Koedinger, K. R., & Aleven, V. (2025). Student perceptions of adaptive goal setting recommendations: A design prototyping study. In A. I. Cristea, E. Walker, Y. Lu, O. C. Santos, & S. Isotani (Eds.), Artificial intelligence in education: AIED 2025 (Lecture Notes in Computer Science, Vol. 15881). Springer. [link] [pdf]

Chen, E., Li, J., Huang, S., Xinyi, T., Lin, J., Carvalho, P.F. & Koedinger, K.R. (2025). Identifying Effective Praise in Tutoring: Large Language Models with Transparent Explanations. In: Cristea, A.I., Walker, E., Lu, Y., Santos, O.C., Isotani, S. (eds) Artificial Intelligence in Education. AIED 2025. Lecture Notes in Computer Science(), vol 15882. Springer, Cham. [link] [pdf]

Gold, G., Asher, M. W, & Carvalho, P. F. (2025). To Honor or Dishonor Student Choices? The Impact of Self-Regulation on Instructional Methods and Learning Outcomes. Proceedings of the Annual Meeting of the Cognitive Science Society, 47. [link] [pdf]

Cao, M., Yan, V. X, Sana, F., & Carvalho, P. F. (2025). Optimizing Learning Efficiency: Balancing Spacing and Repetition Under Time Constraints. Proceedings of the Annual Meeting of the Cognitive Science Society, 47. [link] [pdf]

Scupelli, P., & Carvalho, P. (2025). Design futures pedagogy: Does the type of exercise, year of study, order, and number of exercises matter? In V. Clemente, G. Gomes, M. Reis, S. Félix, S. Ala, & D. Jones (Eds.), Learn X Design 2025. [link] [pdf]

Butler, D., Borchers, C., Asher, M., Lee, Y., Karnataki, S., Dangi, S., Athreya, S., Stamper, J., Ogan, A., & Carvalho, P. (2025). Does the doer effect generalize to non-WEIRD populations? Toward analytics in radio and phone-based learning. In Proceedings of the 15th International Learning Analytics and Knowledge Conference (pp. 844–850). Association for Computing Machinery. [link]

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, 14(3), 355–368. [link] [pdf] 

Carvalho, P.F. & Godwin, K. (2025). Comparing generating predictions with retrieval practice as learning strategies for primary school children. Journal of Experimental Psychology: Applied, 31(2), 71-83 [link] [preprint]

2024

Asher, M. W., Sana, F., Koedinger, K.R., & Carvalho, P.F. (2024). Students Can Learn More Efficiently When Lectures Are Replaced with Practice Opportunities and Feedback. Proceedings of the Annual Meeting of the Cognitive Science Society, 46. [link]

Rachatasumrit, N., Carvalho, P.F., & Koedinger, K. (2024). Beyond Accuracy: Embracing Meaningful Parameters in Educational Data Mining. In B. Paaßen & C. D. Epp (Eds.), Proceedings of the 17th International Conference on Educational Data Mining (pp. 203--210). International Educational Data Mining Society. [link]

Yadav, G., Carvalho, P. F., McLaughlin, E. A., & Koedinger, K. R. (2024). Beyond repetition: The role of varied questioning and feedback in knowledge generalization. In Proceedings of the Eleventh ACM Conference on Learning @ Scale (pp. 451–455). Association for Computing Machinery. https://doi.org/10.1145/3657604.3664688

Gold, G., Borchers, C., & Carvalho, P.F. (2024).Further Evidence for Regularity in Student Learning Rates Across Demographic, Academic Proficiency, and Motivational Groups. Companion Proceedings 14th International Conference on Learning Analytics & Knowledge (LAK24). [link]

Aleven, V., Borchers, C., Huang, Y., Nagashima, T., McLaren, B., Carvalho, P., Popescu, O., Sewall, J., & Koedinger, K. (2024). An Integrated Platform for Studying Learning with Intelligent Tutoring Systems: CTAT+TutorShop. Proceedings of the Fifth Annual Workshop on Learning@Scale 2024: A/B Testing and Platform-Enabled Learning Research. [pdf]

Yan, V. X., Sana, F., & Carvalho, P. F. (2024). No Simple Solutions to Complex Problems: Cognitive Science Principles Can Guide but Not Prescribe Educational Decisions. Policy Insights from the Behavioral and Brain Sciences [Invited Submission]  [link] [pdf]

Wei, Y., Carvalho, P.F., Stamper, J. (2024). Uncovering Name-Based Biases in Large Language Models Through Simulated Trust Game.  https://arxiv.org/pdf/2404.14682

2023

Borchers, C., Carvalho, P.F., Xia, M., Liu, P., Koedinger, K.R., Aleven, V. (2023). What Makes Problem-Solving Practice Effective? Comparing Paper and AI Tutoring. In Proceedings of the Eighteenth European Conference on Technology Enhanced Learning. [link] [pdf]

Rachatasumrit, N., Carvalho, P.F., Li, S., & Koedinger, K.R. (2023). Content Matters: A Computational Investigation into the Effectiveness of Retrieval Practice and Worked Examples. In Artificial Intelligence in Education: 24th International Conference, AIED 2023, Tokyo, Japan, July 3-7, 2023, Proceedings, Part I (pp. 54-64). Cham: Springer International Publishing. [link] [pdf] 🏆

Koedinger, K. R., Carvalho, P.F., Liu, R., & McLaughlin, E. A. (2023). An Astonishing Regularity in Student Learning Rate. Proceedings of the National Academy of Sciences, 120(13), e2221311120, https://www.pnas.org/doi/abs/10.1073/pnas.2221311120

Yan, V., Carvalho, P.F., & Sana, F. (2023). How Students' Decisions to Space Their Practice are Related to Better Learning. In Overson, C. E.,  Hakala, C. M.,  Kordonowy, L. L. & Benassi, V. A. (Eds). In their own words: What scholars want you to know about why and how to apply the science of learning in your academic setting (pp. 473-449). Society for the Teaching of Psychology. https://teachpsych.org/ebooks/itow

2022

Carvalho, P.F., McLaughlin, E.A., & Koedinger, K.R. (2022). Varied practice testing is associated with better learning outcomes in self-regulated online learning. Journal of Educational Psychology, 114(8), 1723–1742. https://doi.org/10.1037/edu0000754 [link] [pdf]

Carvalho, P.F., & Goldstone, R.L. (2022). A computational model of context-dependent encodings during category learning. Cognitive Science, 46 (4), e13128. https://onlinelibrary.wiley.com/doi/10.1111/cogs.13128  [link] [pdf]

Sana, F., Yan, V.X., & Carvalho, P.F. (2022). On rest-from-deliberate-practice as a mechanism for the spacing effect: Commentary on Chen et al. (2021). Educational Psychology Review. https://doi.org/10.1007/s10648-022-09663-8 [link] [pdf]

de Leeuw, J.R., Motz, B.A., Fyfe, E.R., Carvalho, P.F., & Goldstone, R.L. (2022) Generalizability, transferability, and the practice-to-practice gap. Behavioral and Brain Sciences, 45, E11. doi:10.1017/S0140525X21000406 [preprint]

Carvalho, P.F., Rachatasumrit, N., & Koedinger, K.R. (2022). Learning depends on knowledge: The benefits of retrieval practice vary for facts and skills. In J. Culbertson, A. Perfors, H. Rabagliati, & V. Ramenzoni (Eds.), Proceedings of the 44th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.  [pdf]

Chine, D. R., Brentley, C., Thomas-Browne, C., Richey, J.E., Gul, A., Carvalho, P.F., Branstetter, L., & Koedinger, K.R. (2022). Educational Equity Through Combined Human-AI Personalization: A Propensity Matching Evaluation. In Artificial Intelligence in Education: 23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part I (pp. 366-377). Cham: Springer International Publishing. [pdf]

2021

Carvalho, P.F., Chen, C., & Yu, C. (2021). The distributional properties of exemplars affect category learning and generalization. Scientific Reports, 11, 11263, 1-10. https://doi.org/10.1038/s41598-021-90743-0 [link] [pdf] [data and stimuli]

Fyfe, E. de Leeuw, J.R., Carvalho, P.F., Goldstone, R.L., Motz, B.A. (2021). ManyClasses 1: Assessing the generalizable effect of immediate versus delayed feedback across many college classes. Advances in Methods and Practices in Psychological Science, 4 (3),  https://doi.org/10.1177/25152459211027575 [link] [pdf] [data, stimuli, and registration]

Carvalho, P.F., & Goldstone, R.L. (2021). The most efficient sequence of study depends on the type of test. Applied Cognitive Psychology, 35(1), 82-97. https://doi.org/10.1002/acp.3740 [link] [pdf] [data and stimuli]

Hou, X., Carvalho, P. F., & Koedinger, K. R. (2021) Drinking Our Own Champagne: Analyzing the Impact of Learning-by-doing Resources in an E-learning Course. In Companion Proceedings of the 11th International Conference on Learning Analytics & Knowledge (LAK21). [pdf]

2020 and earlier

Carvalho, P.F., Sana, F., Yan, V.X. (2020). Self-regulated spacing in a massive open online course is related to better learning outcomes. npj Science of Learning, 5(1), 1-7 https://doi.org/10.1038/s41539-020-0061-1 [link]

Richey, J. E., Lobczowski, N. G., Carvalho, P. F., & Koedinger, K. (2020). Comprehensive Views of Math Learners: A Case for Modeling and Supporting Non-math Factors in Adaptive Math Software. In International Conference on Artificial Intelligence in Education (pp. 460-471). Springer, Cham. [pdf]

Motz, B., Carvalho, P., & Fyfe, E. (2020, August). A Preliminary Taxonomy of A/B: Education Experiments with Different Inferences and Scopes. Educational A/B Testing at Scale A Virtual Workshop at Learning @ Scale 2020. [pdf]

Chounta, I. & Carvalho, P.F. (2019). Square it up! How to model step duration when predicting student performance. In Proceedings of the 9th International Learning Analytics & Knowledge Conference. [pdf]

Thaker, K., Carvalho, P.F., & Koedinger, K.R. (2019). Comprehension Factor Analysis: Modeling student's reading behaviour. In Proceedings of the 9th International Learning Analytics & Knowledge (LAK19) Conference. [pdf]

Koedinger, K.R., Stamper, J., & Carvalho, P.F. (2019). Sharing and Reusing Data and Analytic Methods with LearnSphere. In Companion Proceedings 9th International Conference on Learning Analytics & Knowledge (LAK19).

Stamper, J., Carvalho, P.F., Moore, S., & Koedinger, K.R. (2019). Tigris: An Online Workflow Tool for Sharing Educational Data and Analytic Methods. In Companion Proceedings 9th International Conference on Learning Analytics & Knowledge (LAK19). 

Carvalho, P.F. & Goldstone, R.L. (2019). When Does Interleaving Practice Improve Learning? In Dunlosky, J. & Rawson, K. (Eds). Cambridge University Handbook on Cognition and Education (pp. 411-436). Cambridge University Press. [link] [pdf]

Motz, B.A., & Carvalho, P.F. (2019). Not whether, but where: Scaling-up how we think about effects and relationships in natural educational contexts. In Companion Proceedings 9th International Conference on Learning Analytics & Knowledge (LAK19). doi: 10.13140/RG.2.2.30825.34407

Motz, B.A., Carvalho, P.F., de Leeuw, J.R., & Goldstone, R.L. (2018). Embedding Experiments: Staking Causal Inference in Authentic Educational Contexts. Journal of Learning Analytics, 5(2), 47–59  [link] [pdf]

Carvalho, P.F., Vales, C., Fausey, C.M., & Smith, L.B. (2018). Novel names extend for how long preschool children sample visual information. Journal of Experimental Child Psychology, 168, 1-8 [link] [pdf] [data and stimuli]

Carvalho, P.F. (2018). Understanding the Dynamics of Learning: The Case for Studying Interactions. In T.T. Rogers, M. Rau, X. Zhu, & C. W. Kalish (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (pp. 51-52). Austin, TX: Cognitive Science Society. [pdf]

Carvalho, P.F., Manke, K.J, & Koedinger, K.R. (2018). Not all Active Learning is Equal: Predicting and Explaining Improves Transfer Relative to Answering Practice Questions. In T.T. Rogers, M. Rau, X. Zhu, & C. W. Kalish (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (pp. 1458-1463). Austin, TX: Cognitive Science Society. [pdf] [data & stimuli]

Carvalho, P.F., Gao, M., Motz, B.A., & Koedinger, K.R. (2018). Analyzing the relative learning benefits of completing required activities and optional readings in online courses. In Proceedings of the 11th International Conference on Educational Data Mining. Buffalo, NY. [pdf]

Chounta, I. A., Carvalho, P.F. (2018). Will time tell? Exploring the relationship between step duration and student performance. In Kay, J. & Luckin, R. (Eds.), Rethinking Learning in the Digital Age: Making the Learning Sciences Count, 13th International Conference of the Learning Sciences (ICLS) 2018, Volume 2 (pp. 993-996). London, UK: International Society of the Learning Sciences. [pdf]

Carvalho, P.F., & Goldstone, R.L (2017). The sequence of study changes what information is attended to, encoded and remembered during category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 43(11), 1699-1719 [link] [pdf] [data & stimuli]

Carvalho, P.F., & Goldstone, R.L. (2017). Zebras and antelopes: category sparsity as the result of the relations between objects and within categories. Language, Cognition, and Neuroscience, 32(8) 944-946. [Commentary on Perry & Lupyan (2016) “Recognizing a zebra from its stripes and the stripes from ‘zebra’: the role of verbal labels in selecting category relevant information”] [Link] [pdf]

Motz, B.A, de Leeuw, J.R., Carvalho, P.F., Liang, K.L., & Goldstone, R.L. (2017). A Dissociation between Engagement and Learning: Enthusiastic Instructions Fail to Reliably Improve Performance on a Memory Task. PLOS ONE 12(7): e0181775 [link] [pdf] [data and stimuli]

Meagher, B.J., Carvalho, P.F., Goldstone, R.L., & Nosofsky, R.M. (2017). Organized Simultaneous Displays Facilitate Learning of Complex Natural Science Categories. Psychonomic Bulletin & Review, 24(6), 1987-1994 [link] [pdf] [data]

Carvalho, P.F., McLaughlin, E. A., & Koedinger, K.R. (2017). Is there an explicit learning bias? Students beliefs, behaviors and learning outcomes.  In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (pp 204-209). Austin TX: Cognitive Science Society. [pdf]

Carvalho, P.F. & Goldstone, R.L. (2017). The most efficient sequence of study depends on the type of test. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (pp 198-203). Austin TX: Cognitive Science Society. [pdf] 

Goldstone, R. L., Kersten, A., & Carvalho, P. F. (2017).  Categorization and Concepts. In J. Wixted (Ed.) Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, Fourth Edition, Volume Three: Language & Thought.  (pp. 275-317). New Jersey: Wiley. [pdf]

Carvalho, P.F.*, Braithwaite, D. W.*, de Leeuw, J.R., Motz, B.A., & Goldstone, R.L. (2016) An in vivo study of self-regulated study sequencing in Introductory Psychology courses, PLOS ONE 11(3). [Link] [pdf] [data & materials] *equal author contribution

Finch, D. D., Carvalho, P.F., & Goldstone, R.L. (2016). Variability in category learning: The Effect of Context Change and Item Variation on Knowledge Generalization. In A. Papafragou, D. Grodner, D. Mirman, & J.C. Trueswell (Eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society (pp 2327-2332). Austin TX: Cognitive Science Society.  [pdf] 

Carvalho, P.F. & Goldstone, R.L. (2016). Human Perceptual Learning and Categorization. In Murphy R.A., & Honey, R.C. (Eds). The Wiley Handbook on The Cognitive Neuroscience of Learning (pp. 223-248). Chichester, West Sussex, UK: John Wiley & Sons Ltd. [pdf]

Carvalho, P.F., & Goldstone, R.L. (2015). What you learn is more than what you see: What can sequencing effects tell us about inductive category learning? Frontiers in Psychology, 6(505), 1-12. [Link] [pdf] [supplemental material]

Carvalho, P.F., Braithwaite, D. W., de Leeuw, J. R., Motz, B. A., & Goldstone, R.L. (2015). Effectiveness of Learner-Regulated Study Sequence: An in-vivo study in Introductory Psychology courses. In Noelle, D. C., Dale, R., Warlaumont, A. S., Yoshimi, J., Matlock, T., Jennings, C. D., & Maglio, P. P. (Eds.), Proceedings of the 37th Annual Meeting of the Cognitive Science Society (pp. 309-314). Austin, TX: Cognitive Science Society. [pdf]

Kost, A.S., Carvalho, P. F., Goldstone, R. L. (2015). Can You Repeat That? The Effect of Item Repetition on Interleaved and Blocked Study. In Noelle, D. C., Dale, R., Warlaumont, A. S., Yoshimi, J., Matlock, T., Jennings, C. D., & Maglio, P. P. (Eds.), Proceedings of the 37th Annual Meeting of the Cognitive Science Society (pp. 1189-1194). Austin, TX: Cognitive Science Society. [pdf]

Carvalho, P.F., & Goldstone, R.L. (2015). The benefits of interleaved and blocked study: Different tasks benefit from different schedules of study. Psychonomic Bulletin & Review, 22(1), 281-288. [Link] [pdf] [supplemental material 1] [supplemental material 2] [data]

Brunel, L., Carvalho, P. F., & Goldstone, R.L. (2015). It does belong together: Cross-modal correspondences influence cross-modal integration during perceptual learning. Frontiers in Psychology, 6(358), 1-10. [Link] [pdf]

Carvalho, P.F. & Goldstone, R.L. (2014). Effects of Interleaved and Blocked Study on Delayed Test of Category Learning Generalization. Frontiers in Psychology, 5(936), 1-10. [Link] [pdf] [data & stimuli]

Carvalho, P.F. & Goldstone, R.L. (2014). Putting category learning in order: category structure and temporal arrangement affect the benefit of interleaved over blocked study. Memory & Cognition, 42(3), 481-495. [Link] [pdf] [supplemental material 1] [data & stimuli]

Weitnauer, E., Carvalho, P.F., Goldstone, R.L., & Ritter, H. (2014). Similarity-based Ordering of Instances for Efficient Concept Learning. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (pp. 1760-1765). Austin, TX: Cognitive Science Society. [pdf]

Carvalho, P.F., & Goldstone, R.L. (2013). How to present exemplars of several categories? Interleave during active learning and block during passive learning. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 1982-1987). Austin, TX: Cognitive Science Society.  [pdf]

Carvalho, P.F., Vales, C., Fausey, C. M. & Smith, L.B. (2013). An eyetracking study of children's relational thinking: The role of labels and sustained attention. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 1988-1993). Austin, TX: Cognitive Science Society.  [pdf]

Weitnauer, E., Carvalho, P.F., Goldstone, R.L., & Ritter, H. (2013). Grouping by Similarity Helps Concept Learning. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 3747-3752). Austin, TX: Cognitive Science Society. [pdf]

Goldstone, R. L., Kersten, A., & Carvalho, P.F. (2013). Concepts and Categorization. In Weiner, I.B, Healey, A.J., & Proctor, R.W. (Eds.) Handbook of Psychology, Volume 4, Experimental Psychology, 2nd Edition (pp. 607-630). New York, NY: Wiley. [pdf]

Carvalho, P.F. & Albuquerque, P.B. (2012). Memory encoding of stimulus features in human perceptual learning. Journal of Cognitive Psychology, 24(6), 654-664. [Link] [pdf] [data & stimuli]

Carvalho, P.F., & Goldstone, R.L. (2012). Category structure modulates interleaving and blocking advantage in inductive category acquisition. In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society (pp. 186-191), Austin, TX: Cognitive Science Society. [pdf]

Hendrickson, A.T., Carvalho, P.F., & Goldstone, R.L. (2012). Going to Extremes: The influence of unsupervised categories on the mental caricaturization of faces and asymmetries in perceptual discrimination. In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society (pp. 1662-1667), Austin, TX: Cognitive Science Society. [pdf]

Carvalho, P.F., & Goldstone, R.L. (2011). Sequential similarity and comparison effects in category learning. In L. Carlson, C. Hölscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 2977- 2982). Austin, TX: Cognitive Science Society. [pdf] [data & stimuli]