Nigel Bosch

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

Learning analytics, user modeling, and fairness and transparency in machine learning.

• Best Paper Honorable Mention: 2025 ACM CHI conference on Human Factors in Computing Systems (CHI 2025)
• 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.
 

• Summer 2025 - Independent Study - IS589PNB

Wednesdays, noon to 1:00 pm. 

Lee, H., Stinar, F., Zong, R., Valdiviejas, H., Wang, D., & Bosch, N. (2025). Learning behaviors mediate the effect of AI-powered support for metacognitive calibration on learning outcomes. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 17:1-18. https://doi.org/10.1145/3706598.3713960

Fairbairn, C. E., Han, J., Caumiant, E. P., Benjamin, A. S., & Bosch, N. (2025). A wearable alcohol biosensor: Exploring the accuracy of transdermal drinking detection. Drug and Alcohol Dependence, 266, 112519:1-10. https://doi.org/10.1016/j.drugalcdep.2024.112519

Jiang, L., Belitz, C., & Bosch, N. (2024). Synthetic dataset generation for fairer unfairness research. Proceedings of the 14th International Conference on Learning Analytics & Knowledge (LAK ’24), 200–209. https://doi.org/10.1145/3636555.3636868

Belitz, C., Ocumpaugh, J., Ritter, S., Baker, R. S., Fancsali, S. E., & Bosch, N. (2023). Constructing categories: Moving beyond protected classes in algorithmic fairness. Journal of the Association for Information Science and Technology, 74(6), 663–668. https://doi.org/10.1002/asi.24643

Hur, P., Lee, H., Bhat, S., & Bosch, N. (2022). Using machine learning explainability methods to personalize interventions for students. In A. Mitrovic & N. Bosch (Eds.), Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022) (pp. 438–445). International Educational Data Mining Society. https://doi.org/10.5281/zenodo.6853181

Bosch, N. (2021). AutoML feature engineering for student modeling yields high accuracy, but limited interpretability. Journal of Educational Data Mining, 13(2), 55–79. https://doi.org/10.5281/zenodo.5275314

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), e2101937118:1-3. https://doi.org/10.1073/pnas.2101937118

Bosch, N., Crues, R. W., Shaik, N., & Paquette, L. (2020). “Hello, [REDACTED]”: Protecting student privacy in analyses of online discussion forums. In A. N. Rafferty, J. Whitehill, C. Romero, & V. Cavalli-Sforza (Eds.), Proceedings of the 13th International Conference on Educational Data Mining (EDM 2020) (pp. 39–49). International Educational Data Mining Society.

Huang, E., Valdiviejas, H., & Bosch, N. (2019). I’m sure! Automatic detection of metacognition in online course discussion forums. Proceedings of the 8th International Conference on Affective Computing and Intelligent Interaction (ACII 2019), 241–247. https://doi.org/10.1109/ACII.2019.8925506