Prof. Dr. Rosa Blakeman-Lavelle

Schmid, J., Lavelle-Hill, R., & Sudeck, G. (2026). Linking Individual Motives to the Type of Exercise and Sport Activity: Toward Recommendations for Optimal Activity Matching Through a Machine Learning Approach. 23(6), 840–850. https://doi.org/10.1123/jpah.2025-0414

Fütterer, Tim, Campos, Diego G., Gfrörer, Thomas, Lavelle-Hill, Rosa, Murayama, Kou, & Scherer, Ronny. (2026). AI tools for systematic literature reviews and meta-analyses in educational psychology: An overview and a practical guide. Learning and Individual Differences, 126. https://doi.org/10.1016/j.lindif.2025.102849

Lavelle-Hill, Rosa, Smith, Gavin, & Murayama, Kou. (2025). Bridging Traditional-Statistics and Machine-Learning Approaches in Psychology: Navigating Small Samples, Measurement Error, Nonindependent Observations, and Missing Data. Advances in Methods and Practices in Psychological Science, 8. https://doi.org/10.1177/25152459251345696

Lavelle-Hill, R., Smith, G., Deininger, H., & Murayama, K. (2025). An explainable artificial intelligence handbook for psychologists: Methods, opportunities, and challenges. Psychological Methods. https://doi.org/10.1037/met0000772

Jach, Hayley K., Cools, Roshan, Frisvold, Alex, Grubb, Michael A., Hartley, Catherine A., Hartmann, Jochen, Hunter, Laura, Jia, Ruonan, de Lange, Floris P., Larisch, Ruby, Lavelle-Hill, Rosa, Levyi, Ifat, Li, Yutong, van Lieshout, Lieke L.F., Nussenbaum, Kate, Ravaioli, Silvio, Wang, Siyu, Wilson, Robert, Woodford, Michael, et al. (2024). Individual differences in information demand have a low dimensional structure predicted by some curiosity traits. Proceedings of the National Academy of Sciences of the United States of America, 121. https://doi.org/10.1073/pnas.2415236121

Lavelle-Hill, Rosa, Frenzel, Anne C., Goetz, Thomas, Lichtenfeld, Stephanie, Marsh, Herbert W., Pekrun, Reinhard, Sakaki, Michiko, Smith, Gavin, & Murayama, Kou. (2024). How the Predictors of Math Achievement Change Over Time: A Longitudinal Machine Learning Approach. Journal of Educational Psychology, 116, 1383–1403. https://doi.org/10.1037/edu0000863

Bardach, Lisa, Röhl, Sebastian, Oczlon, Sophie, Schumacher, Aki, Lüftenegger, Marko, Lavelle-Hill, Rosa, Schwarzenthal, Miriam, & Zitzmann, Steffen. (2024). Cultural Diversity Climate in School: A Meta-Analytic Review of Its Relationships With Intergroup, Academic, and Socioemotional Outcomes. Psychological Bulletin, 150, 1397–1439. https://doi.org/10.1037/bul0000454

Lavelle-Hill, Rosa, Harvey, John, Smith, Gavin, Mazumder, Anjali, Ellis, Madeleine, Mwantimwa, Kelefa, & Goulding, James. (2022). Using mobile money data and call detail records to explore the risks of urban migration in Tanzania. EPJ Data Science, 11. https://doi.org/10.1140/epjds/s13688-022-00340-y

Lavelle-Hill, Rosa, Smith, Gavin, Mazumder, Anjali, Landman, Todd, & Goulding, James. (2021). Machine learning methods for “wicked” problems: exploring the complex drivers of modern slavery. Humanities and Social Sciences Communications, 8. https://doi.org/10.1057/s41599-021-00938-z

Lavelle-Hill, Rosa, Goulding, James, Smith, Gavin, Clarke, David D., & Bibby, Peter A. (2020). Psychological and demographic predictors of plastic bag consumption in transaction data. Journal of Environmental Psychology, 72. https://doi.org/10.1016/j.jenvp.2020.101473

Tomas, Carmen, Whitt, Emma, Lavelle-Hill, Rosa, & Severn, Katie. (2019). Modeling Holistic Marks With Analytic Rubrics. Frontiers in Education, 4. https://doi.org/10.3389/feduc.2019.00089

Lavelle-Hill, R., Einsiedler, J., Appel, M., Harpviken, L. L., & Zettler, I. (2026, January 1). Psychology of predictability: using machine learning and personality psychology to understand heterogeneity in algorithm prediction error. Personality and Individual Differences.

Deininger, Hannah, Parrisius, Cora, Lavelle-Hill, Rosa, Meurers, Detmar, Trautwein, Ulrich, Nagengast, Benjamin, & Kasneci, Gjergji. (2025). Who Did What to Succeed? Individual Differences in Which Learning Behaviors Are Linked to Achievement. 15th International Conference on Learning Analytics and Knowledge Lak 2025, 771–782. https://doi.org/10.1145/3706468.3706571

Deininger, Hannah, Lavelle-Hill, Rosa, Parrisius, Cora, Pieronczyk, Ines, Colling, Leona, Meurers, Detmar, Trautwein, Ulrich, Nagengast, Benjamin, & Kasneci, Gjergji. (2023). Can You Solve This on the First Try? – Understanding Exercise Field Performance in an Intelligent Tutoring System. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 13916 LNAI, 565–576. https://doi.org/10.1007/978-3-031-36272-9_46

Campos, Diego G., Fütterer, Tim, Gfrörer, Thomas, Lavelle-Hill, Rosa, Murayama, Kou, König, Lars, Hecht, Martin, Zitzmann, Steffen, & Scherer, Ronny. (2024). Screening Smarter, Not Harder: A Comparative Analysis of Machine Learning Screening Algorithms and Heuristic Stopping Criteria for Systematic Reviews in Educational Research. Educational Psychology Review, 36. https://doi.org/10.1007/s10648-024-09862-5