Stephen Hutt

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Postdoctoral Researcher

Teaching, Learning, and Leadership Division

Contact

hutts@upenn.edu

Media Inquiries:
Contact Penn GSE Communications.

Education

Ph.D. (Computer Science) University of Colorado Boulder, 2020
M.Eng. (Computer Science and Artificial Intelligence), University of York, UK, 2015

Areas of Expertise

Machine learning
Affective computing
Educational data mining
Learning analytics
Student modeling
Student engagement
Artificial intelligence

More Information

Dr. Hutt’s website
Dr. Hutt’s Google scholar page
Penn Center for Learning Analytics

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Biography

Professional Biography

Stephen Hutt is a Postdoctoral researcher working with the Penn Center for Learning Analytics. His research sits at the intersection of Artificial Intelligence (AI), Affective Computing, and Education. Stephen’s research is focused on how AI techniques can be used to improve educational software and experiences, with a special focus on the fair treatment of students who are members of underrepresented groups.

Prior to joining Penn GSE, Stephen was a graduate student at the University of Colorado Boulder in the Institute of Cognitive Science. While at CU, his research considered cost-effective and scalable eye-tracking solutions that could then be used to detect and respond to student engagement within educational software.

Research Interests and Current Projects

We collect more data on students throughout their educational journey than ever before. How can we harness this data, along with increased computation possibilities, to create dynamic educational experiences that support every learner?

Dr. Hutt’s dissertation work involved using commercial off the shelf eye-trackers (retailing for ~100 USD) to detect and respond to student engagement in classrooms. Decades of eye gaze research has explored the links between eye gaze, engagement, and learning; however, due to the high cost of research-grade eye tracking, this work has hitherto been relegated to the confines of the lab. Through a series of studies, his work went from examining initial feasibility to a closed-loop attention-aware tutoring system that used gaze data to monitor and respond to student engagement levels and subsequently improve learning.

His current projects explore alternate ways to support learners as they interact with learning technology. He is leveraging data mining and AI techniques to better understand how students self-regulate their learning as well as how they respond to different scaffolds and interventions. His work also considers ways to support students experiencing subject anxiety and better support students from underrepresented groups.

Publications

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).