Motz, B. (2024). Concentration toward the mode: Estimating changes in the shape of a distribution of student data. Journal of School Psychology, 107, 101364. doi: 10.1016/j.jsp.2024.101364
Rust, M. & Motz, B. (preprint). Incorporating an LMS learning analytic into proactive advising: Validity and use in a randomized experiment. EdArXiv. doi: 10.35542/osf.io/sjw2b
Motz, B., Kruschke, J. K., Hetrick, W. P., James, T., & Puce, A. (preprint). Expectations for rhythmic sounds increase bottom-up visual attention. PsyArXiv. doi: 10.31234/osf.io/b2uaj
Motz, B., Üner, Ö, Jankowski, H., Christie, M., Burgas, K., del Blanco Orobitg, D., & McDaniel, M. (2023). Terracotta: A tool for conducting experimental research on student learning. Behavior Research Methods. doi: 10.3758/s13428-023-02164-8
Motz, B., Quick, J., Brooks, C., Bergner, Y., Gray, G., Lang, C., Li, W., & Marmolejo-Ramos, F. (2023). A LAK of direction: Misalignment between the goals of learning analytics and its research scholarship. Journal of Learning Analytics, 10(2), 1-13. doi: 10.18608/jla.2023.7913
Motz, B. & Morrone, A. (2023). Wild brooms and learning analytics. Journal of Computing in Higher Education. doi: 10.1007/s12528-023-09353-6
Quick, J., Motz, B., & Morrone, A. (2023). Lost in translation: Determining the generalizability of temporal models across course contexts. Proceedings of the 13th International Conference on Learning Analytics & Knowledge (LAK20). doi: 10.1145/3576050.3576092
Motz, B., Fyfe, E., & Guba, T. (2022). Learning to call bullsh*t via induction: Categorization training improves critical thinking performance. Journal of Applied Research in Memory and Cognition. doi: 10.1037/mac0000053
de Leeuw, J., Motz, B., Fyfe, E., Carvalho, P., & Goldstone, R. (2022). Generalizability, transferability, and the practice-to-practice gap. Commentary in response to T. Yarkoni, The Generalizability Crisis. Behavioral and Brain Sciences, 45, e11. doi: 10.1017/S0140525X21000406
Motz, B., Goldstone, R., Busey, T., & Prather, R. (2021). Visual search asymmetry due to the relative magnitude represented by number symbols. Vision, 5(3), 42. doi: 10.3390/vision5030042
Motz, B., Canning, E., Green, D., Mallon, M., & Quick, J. (2021). The influence of automated praise on behavior and performance. Technology, Mind, and Behavior, 2(3). doi: 10.1037/tmb0000042
Fyfe, E., de Leeuw, J. R., Carvalho, P. F., Goldstone, R., Sherman, J., [42 others] & Motz, B. (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), 1-24. doi: 10.1177/25152459211027575
Motz, B., Mallon, M., & Quick, J. (2021). Automated educative nudges to reduce missed assignments in college. IEEE Transactions on Learning Technologies, 14(2), 186-200. doi: 10.1109/TLT.2021.3064613
Jaggars, S. Motz, B., Rivera, M., Heckler, A., Quick, J., Hance, E., & Karwisch, C. (2021). Digital divides at the University: Lessons learned from the COVID-19 Emergency Transition. Aaron Horn, Ed., Midwest Higher Education Compact (MHEC), Minneapolis, MN.
Motz, B., Quick, J., Wernert, J., & Miles, T. (2021). A pandemic of busywork: Increased online coursework following the transition to remote instruction is associated with reduced academic achievement. Online Learning, 25(1), 70-85. doi: 10.24059/olj.v25i1.2475
Quick, J., Motz, B., Israel, J., & Kaetzel, J. (2020). What college students say, and what they do: Aligning self-regulated learning theory with behavioral logs. Proceedings of the 10th International Conference on Learning Analytics & Knowledge (LAK20). doi: 10.1145/3375462.3375516
Eyink, J.*, Motz, B.*, Heltzel, G., & Liddell, T. (2020). Self-regulated studying behavior, and the social norms that influence it. Journal of Applied Social Psychology, 50(1), 10-21. doi: 10.1111/jasp.12637 (* co-first authors)
Motz, B. (August 23, 2019). Principles for the responsible design of automated student support. EDUCAUSE Review, Transforming Higher Ed Blog. Link to article
Motz, B. & Carvalho, P. (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., Quick, J., Schroeder, N., Zook, J., & Gunkel, M. (2019). The validity and utility of activity logs as a measure of student engagement. In Proceedings 9th International Conference on Learning Analytics & Knowledge (LAK19). doi: 10.1145/3303772.3303789
Motz, B., Carvalho, P., de Leeuw, J., & Goldstone, R. (2018). Embedding experiments: Staking causal inference in authentic educational contexts. Journal of Learning Analytics, 5(2), 47-59. doi: 10.18608/jla.2018.52.4
Motz, B., Busey, T., Rickert, M., & Landy, D. (2018). Finding topics in enrollment data. Proceedings of the 11th International Conference on Educational Data Mining. Buffalo, New York. https://eric.ed.gov/?id=ED593218
Carvalho, P., Gao, M., Motz, B., & Koedinger, K. (2018). Analyzing the relative learning benefits of completing required activities and optional readings in online courses. Proceedings of the 11th International Conference on Educational Data Mining. Buffalo, New York.https://eric.ed.gov/?id=ED593230
Motz, B., de Leeuw, J., Carvalho, P., Liang, K., & Goldstone, R. (2017). A dissociation between engagement and learning: Enthusiastic instructions fail to reliably improve performance on a memory task. PLoS ONE, 12(7): e0181775. doi: 10.1371/journal.pone.0181775
(Winner of the Center for Open Science Preregistration Challenge Award)
[Want more? The rest are listed in Ben's CV]
