Selected Publications
Gardner, M., Hutt, S., Duckworth, A. L., & D’Mello, S. K. (2020). How does high school extracurricular participation predict bachelor’s degree attainment? It is complicated. Journal of Research on Adolesence, 1–16. https://doi.org/10.1111/jora.12557
Hutt, S., Krasich, K., Mills, C., Bosch, N., White, S., Brockmole, J. R., D’Mello, S. K., & D’Mello, S. K. (2019). Automated gaze-based mind wandering detection during computerized learning in classrooms. User Modeling and User-Adapted Interaction, 29(4), 821–867. https://doi.org/10.1007/s11257-019-09228-5
Hutt, S., Grafsgaard, J. F., & D’Mello, S. K. (2019). Time to scale: Generalizable affect detection for tens of thousands of students across an entire school year. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI ’19. https://doi.org/10.1145/3290605.3300726
Hutt, S., Gardner, M., Duckworth, A. L., & D’Mello, S. K. (2019). Evaluating fairness and generalizability in models predicting on-time graduation from college applications. The 12th International Conference on Educational Data Mining, 79–88.
Jensen, E., Hutt, S., & D’Mello, S. K. (2019). Generalizability of sensor-free affect detection models in a longitudinal dataset of tens of thousands of students. The 12th International Conference on Educational Data Mining, 324–329.
Galla, B. M., Shulman, E. P., Plummer, B. D., Gardner, M., Hutt, S. J., Goyer, J. P., D’Mello, S. K., Finn, A. S., & Duckworth, A. L. (2019). Why high school grades are better predictors of on-time college graduation than are admissions test scores: The roles of self-regulation and cognitive ability. American Educational Research Journal, 56(6), 2077–2115. https://doi.org/10.3102/0002831219843292
Stone, C., Quirk, A., Gardener, M., Hutt, S., Duckworth, A. L., & D’Mello, S. K. (2019). Language as thought: Using natural language processing to model noncognitive traits that predict college success. Proceedings of the 9th International Conference on Learning Analytics & Knowledge, 320–329. https://doi.org/10.1145/3303772.3303801
Hutt, S., Gardener, M., Kamentz, D., Duckworth, A. L., & D’Mello, S. K. (2018). Prospectively predicting 4-year college graduation from student applications. Proceedings of the 8th International Conference on Learning Analytics and Knowledge, 280–289. https://doi.org/10.1145/3170358.3170395
Krasich, K., McManus, R., Hutt, S., Faber, M., D’Mello, S. K., & Brockmole, J. R. (2018). Gaze-based signatures of mind wandering during real-world scene processing. Journal of Experimental Psychology: General, 147(8), 1111. https://doi.org/10.1037/xge0000411
Krasich, K., Hutt, S., Mills, C., Spann, C. A., Brockmole, J. R., & D’Mello, S. K. (2018). “Mind” TS: Testing a brief mindfulness intervention with an intelligent tutoring system. In Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and Lecture notes in bioinformatics): Vol. 10948 LNAI. https://doi.org/10.1007/978-3-319-93846-2_32
Hutt, S., Mills, C., Bosch, N., Krasich, K., Brockmole, J. R., & D’Mello, S. K. (2017). “Out of the Fr-Eye-ing Pan”: Towards Gaze-Based Models of Attention During Learning with Technology in the Classroom. Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, 94–103. https://doi.org/10.1145/3079628.3079669
Hutt, S., Mills, C., White, S., Donnelly, P. J., & D’Mello, S. K. (2016). The eyes have it: gaze-based detection of mind wandering during learning with an intelligent tutoring system. In T. Barnes, M. Chi, & M. Feng (Eds.), The 9th International Conference on Educational Data Mining (pp. 86–93).