Nigel Bosch

• Assistant Professor, Department of Educational Psychology
• Faculty Affiliate, National Center for Supercomputing Applications (NCSA)
• Faculty Affiliate, Illinois Informatics

• National Study of Learning Mindsets Early Career Fellowship

Nigel Bosch is an assistant professor in the School of Information Sciences and the Department of Educational Psychology at the University of Urbana-Champaign and a faculty affiliate at the National Center for Supercomputing Applications (NCSA) and Illinois Informatics. His research includes machine learning/data mining methods to study human behaviors, especially in learning contexts (learning analytics), with an emphasis on model generalization and fair treatment of users.

His research examines data such as facial expressions, audio recordings, log file records of user actions, and other sources that provide insight into learners' behaviors. Neural networks and other machine learning methods provide powerful ways to mine these data for knowledge, but can proliferate biases that are commonly found in datasets. Bosch's research also focuses on analyzing the biases in these methods, with the goal of ultimately developing fairer learning software and research methods.

He obtained his PhD in computer science at the University of Notre Dame in 2017. For two years prior to joining the iSchool, he was a postdoctoral researcher at the NCSA.
 

• Fall 2023 - Independent Study - IS389PNB
• Spring 2024 - Independent Study - IS389PNB
• Spring 2024 - Appd Machine Learn: Team Proj - IS557AC

By appointment.

Hur, P., Lee, H., Bhat, S., & Bosch, N. (2022). Using machine learning explainability methods to personalize interventions for students. Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022), 438–445. International Educational Data Mining Society.

Gurrieri, L., Fairbairn, C. E., Sayette, M. A., & Bosch, N. (2021). Alcohol narrows physical distance between strangers. Proceedings of the National Academy of Sciences, 118(20).

Belitz, C., Jiang, L., & Bosch, N. (2021). Automating procedurally fair feature selection in machine learning. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES '21). New York, NY: Association for Computing Machinery.

Bosch, N., Crues, R. W., Shaik, N., & Paquette, L. (2020). "Hello, [REDACTED]": Protecting student privacy in analyses of online discussion forums. In Proceedings of the 13th International Conference on Educational Data Mining (EDM 2020) (pp. 39–49). International Educational Data Mining Society.

Bosch, N., & Paquette, L. (2018). Metrics for discrete student models: Chance levels, comparisons, and use cases. Journal of Learning Analytics, 5(2), 86–104.

Monkaresi, H., Bosch, N., Calvo, R. A., & D'Mello, S. K. (2017). Automated detection of engagement using video-based estimation of facial expressions and heart rate. IEEE Transactions on Affective Computing, 8(1), 15–28.

Bosch, N., D'Mello, S. K., Ocumpaugh, J., Baker, R. S., & Shute, V. (2016). Using video to automatically detect learner affect in computer-enabled classrooms. ACM Transactions on Interactive Intelligent Systems (TiiS), 6(2), 17:1–17:26.

Kai, S., Paquette, L., Baker, R., Bosch, N., D'Mello, S. K., Ocumpaugh, J., ... Ventura, M. (2015). Comparison of face-based and interaction-based affect detectors in physics playground. In C. Romero, M. Pechenizkiy, J. Boticario, & O. Santos (Eds.), Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015) (pp. 77–84). International Educational Data Mining Society.

Rodeghero, P., McMillan, C., McBurney, P. W., Bosch, N., & D'Mello, S. K. (2014). Improving automated source code summarization via an eye-tracking study of programmers. Proceedings of the 36th International Conference on Software Engineering (ICSE 2014) (pp. 390–401). New York, NY: ACM.

Bosch, N., D'Mello, S. K., & Mills, C. (2013). What emotions do novices experience during their first computer programming learning session?. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED 2013) (pp. 11–20). Berlin, Heidelberg: Springer.