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Nigel Bosch

Biography

I am an Assistant Professor in the School of Information Sciences (iSchool) at the University of Illinois Urbana-Champaign, with a joint appointment in the Department of Educational Psychology. I am also a faculty affiliate at the National Center for Supercomputing Applications (NCSA), and a faculty affiliate in Illinois Informatics.

I completed my PhD in Computer Science at the University of Notre Dame. Subsequently, I was a postdoctoral researcher at the National Center for Supercomputing Applications.

Key Professional Appointments

  • Assistant Professor, School of Information Sciences, University of Illinois, Urbana-Champaign
  • Assistant Professor, Educational Psychology, University of Illinois, Urbana-Champaign
  • Assistant Professor, National Center for Supercomputing Applications (NCSA), University of Illinois, Urbana-Champaign
Links

Personal website

Research & Service

My research focuses primarily (though not exclusively) on machine learning and human–computer interaction applications in education. My work has included automatic measurement of emotion during computer programming education, studying metacognition in online courses via natural language processing, and other topics related to learning and affective computing.

My research is supported by grants from the National Science Foundation (NSF), Institute of Education Sciences (IES), and the University of Illinois. Papers resulting from my research have been published at journals and peer-reviewed conferences, where I also regularly present on my research.

Publications

Belitz, C., Ocumpaugh, J., Ritter, S., Baker, R. S., Fancsali, S. E., & Bosch, N. (Accepted/In press). Constructing categories: Moving beyond protected classes in algorithmic fairness. Journal of the Association for Information Science and Technology.  link >

Bosch, N., & D'Mello, S. K. (2022). Can Computers Outperform Humans in Detecting User Zone-Outs? Implications for Intelligent Interfaces. ACM Transactions on Computer-Human Interaction, 29(2), [10].  link >

Denny, P., Becker, B. A., Bosch, N., Prather, J., Reeves, B., & Whalley, J. (2022). Novice Reflections during the Transition to a New Programming Language. In SIGCSE 2022 - Proceedings of the 53rd ACM Technical Symposium on Computer Science Education (pp. 948-954). (SIGCSE 2022 - Proceedings of the 53rd ACM Technical Symposium on Computer Science Education; Vol. 1). Association for Computing Machinery, Inc.  link >

Hur, P., & Bosch, N. (2022). Tracking Individuals in Classroom Videos via Post-processing OpenPose Data. In LAK 2022 - Conference Proceedings: Learning Analytics for Transition, Disruption and Social Change - 12th International Conference on Learning Analytics and Knowledge (pp. 465-471). (ACM International Conference Proceeding Series). Association for Computing Machinery.  link >

Zhang, Y., Paquette, L., Baker, R. S., Bosch, N., Ocumpaugh, J., & Biswas, G. (Accepted/In press). How are feelings of difficulty and familiarity linked to learning behaviors and gains in a complex science learning task? European Journal of Psychology of Education.  link >

Zhang, Y., Paquette, L., Bosch, N., Ocumpaugh, J., Biswas, G., Hutt, S., & Baker, R. S. (2022). The evolution of metacognitive strategy use in an open-ended learning environment: Do prior domain knowledge and motivation play a role? Contemporary Educational Psychology, 69, [102064].  link >

Belitz, C., Jiang, L., & Bosch, N. (2021). Automating Procedurally Fair Feature Selection in Machine Learning. In AIES 2021 - Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (pp. 379-389). (AIES 2021 - Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society). Association for Computing Machinery, Inc.  link >

Bosch, N., & D'Mello, S. K. (2021). Automatic Detection of Mind Wandering from Video in the Lab and in the Classroom. IEEE Transactions on Affective Computing, 12(4), 974-988.  link >

Bosch, N. (2021). AutoML feature engineering for student modeling yields high accuracy, but limited interpretability. Journal of Educational Data Mining, 13(2), 55-79.  link >

Bosch, N. (2021). Identifying supportive student factors for mindset interventions: A two-model machine learning approach. Computers and Education, 167, [104190].  link >

Bosch, N. (2021). Investigating SMART models of self-regulation and their impact on learning. In Proceedings of the 14th International Conference on Educational Data Mining (pp. 580-587)

Bosch, N. (2021). Predictive sequential pattern mining via interpretable convolutional neural networks. In Proceedings of the 14th International Conference on Educational Data Mining International Educational Data Mining Society.

Bosch, N., Zhang, Y., Paquette, L., Baker, R. S., Ocumpaugh, J., & Biswas, G. (2021). Students' verbalized metacognition during computerized learning. In CHI 2021 - Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths (Conference on Human Factors in Computing Systems - Proceedings). Association for Computing Machinery.  link >

Bosch, N., & Paquette, L. (2021). What's next? sequence length and impossible loops in state transition measurement. Journal of Educational Data Mining, 13(1), 1-23.  link >

Bosch, N. (2021). Who's stopping you? Using microanalysis to explore the impact of science anxiety on self-regulated learning operations. In Proceedings of the Annual Meeting of the Cognitive Science Society (pp. 1409-1415)

Fairbairn, C. E., & Bosch, N. (2021). A new generation of transdermal alcohol biosensing technology: practical applications, machine -learning analytics and questions for future research. Addiction, 116(10), 2912-2920.  link >

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].  link >

Hickman, L., Bosch, N., Ng, V., Saef, R., Tay, L., & Woo, S. E. (Accepted/In press). Automated video interview personality assessments: Reliability, validity, and generalizability investigations. Journal of Applied Psychology.  link >

Hickman, L., Saef, R., Ng, V., Woo, S. E., Tay, L., & Bosch, N. (Accepted/In press). Developing and evaluating language-based machine learning algorithms for inferring applicant personality in video interviews. Human Resource Management Journal.  link >

Lee, H. J., Hur, P., Bhat, S. P., & Bosch, N. (2021). Promoting Self-regulated Learning in Online Learning by Triggering Tailored Interventions. CEUR Workshop Proceedings, 3051.

Ocumpaugh, J., Hutt, S., Andres, J. M. A. L., Baker, R. S., Biswas, G., Bosch, N., Paquette, L., & Munshi, A. (2021). Using Qualitative Data from Targeted Interviews to Inform Rapid AIED Development. In M. M. T. Rodrigo, S. Iyer, A. Mitrovic, H. N. H. Cheng, D. Kohen-Vacs, C. Matuk, A. Palalas, R. Rajenran, K. Seta, & J. Wang (Eds.), 29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings (pp. 69-74). (29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings; Vol. 1). Asia-Pacific Society for Computers in Education.

Williams-Dobosz, D., Jeng, A., Azevedo, R. F. L., Bosch, N., Ray, C., & Perry, M. (2021). Ask for Help: Online Help-Seeking and Help-Giving as Indicators of Cognitive and Social Presence for Students Underrepresented in Chemistry. Journal of Chemical Education, 98(12), 3693-3703.  link >

Williams-Dobosz, D., Azevedo, R. F. L., Jeng, A., Thakkar, V., Bhat, S., Bosch, N., & Perry, M. (2021). A social network analysis of online engagement for college students traditionally underrepresented in STEM. In LAK 2021 Conference Proceedings - The Impact we Make: The Contributions of Learning Analytics to Learning, 11th International Conference on Learning Analytics and Knowledge (pp. 207-215). (ACM International Conference Proceeding Series). Association for Computing Machinery.  link >

Zhang, Y., Paquette, L., Baker, R. S., Ocumpaugh, J., Bosch, N., Biswas, G., & Munshi, A. (2021). Can Strategic Behaviour Facilitate Confusion Resolution? The Interplay Between Confusion and Metacognitive Strategies in Betty’s Brain. Journal of Learning Analytics, 8(3), 28-44.  link >

D'Angelo, C., Dyer, E., Krist, C., Rosenberg, J., & Bosch, N. (2020). Advancing computational grounded theory for audiovisual data from mathematics classrooms. In M. Gresalfi, & I. S. Horn (Eds.), 14th International Conference of the Learning Sciences: The Interdisciplinarity of the Learning Sciences, ICLS 2020 - Conference Proceedings (pp. 2393-2394). (Computer-Supported Collaborative Learning Conference, CSCL; Vol. 4). International Society of the Learning Sciences (ISLS).  link >

Dyer, E., D'Angelo, C., Bosch, N., Krist, S., & Rosenberg, J. (2020). Analyzing learning with speech analytics and computer vision methods: Technologies, principles, and ethics. In M. Gresalfi, & I. S. Horn (Eds.), 14th International Conference of the Learning Sciences: The Interdisciplinarity of the Learning Sciences, ICLS 2020 - Conference Proceedings (pp. 2651-2653). (Computer-Supported Collaborative Learning Conference, CSCL; Vol. 5). International Society of the Learning Sciences (ISLS).

Fairbairn, C. E., Kang, D., & Bosch, N. (2020). Using machine learning for real-time BAC estimation from a new-generation transdermal biosensor in the laboratory. Drug and Alcohol Dependence, 216, [108205].  link >

Gliser, I., Mills, C., Bosch, N., Smith, S., Smilek, D., & Wammes, J. D. (2020). The sound of inattention: Predicting mind wandering with automatically derived features of instructor speech. In I. I. Bittencourt, M. Cukurova, R. Luckin, K. Muldner, & E. Millán (Eds.), Artificial Intelligence in Education- 21st International Conference, AIED 2020, Proceedings, Part I (pp. 204-215). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12163 LNAI). Springer.  link >

Hoang, L., Boyce, R. D., Bosch, N., Stottlemyer, B., Brochhausen, M., & Schneider, J. (2020). Automatically classifying the evidence type of drug-drug interaction research papers as a step toward computer supported evidence curation. In 2020 AMIA Annual Symposium Proceedings (Vol. 2020, pp. 554-563)

Jay, V., Henricks, G. M., Anderson, C. J., Angrave, L. C., Bosch, N., Williams-Dobosz, D., Shaik, N., Bhat, S. P., & Perry, M. (2020). Online discussion forum help-seeking behaviors of students underrepresented in STEM. In M. Gresalfi, & I. S. Horn (Eds.), 14th International Conference of the Learning Sciences: The Interdisciplinarity of the Learning Sciences, ICLS 2020 - Conference Proceedings (Vol. 2, pp. 809-810). (Computer-Supported Collaborative Learning Conference, CSCL; Vol. 2). International Society of the Learning Sciences (ISLS).  link >

Paquette, L., & Bosch, N. (2020). The Invisible Breadcrumbs of Digital Learning: How Learner Actions Inform Us of Their Experience. In M. Montebello (Ed.), Handbook of Research on Digital Learning (pp. 302-316). IGI Global.  link >

Zhang, Y., Bosch, N., Paquette, L., Munshi, A., Baker, R. S., Biswas, G., & Ocumpaugh, J. (2020). The relationship between confusion and metacognitive strategies in Betty's Brain. In LAK 2020 Conference Proceedings - Celebrating 10 years of LAK: Shaping the Future of the Field - 10th International Conference on Learning Analytics and Knowledge (pp. 276-284). (ACM International Conference Proceeding Series). Association for Computing Machinery.  link >

Andres, J. M. A. L., Paquette, L., Ocumpaugh, J., Jiang, Y., Baker, R. S., Karumbaiah, S., Slater, S., Bosch, N., Munshi, A., Moore, A., & Biswas, G. (2019). Affect sequences and learning in Betty's brain. In Proceedings of the 9th International Conference on Learning Analytics and Knowledge: Learning Analytics to Promote Inclusion and Success, LAK 2019 (pp. 383-390). (ACM International Conference Proceeding Series). Association for Computing Machinery,.  link >

Bosch, N., Huang, E., Angrave, L., & Perry, M. (2019). Modeling improvement for underrepresented minorities in online STEM education. In ACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization (pp. 327-335). (ACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization). Association for Computing Machinery, Inc.  link >

Huang, E., Valdiviejas, H., & Bosch, N. (2019). I'm Sure! Automatic Detection of Metacognition in Online Course Discussion Forums. In 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019 (pp. 241-247). [8925506] (2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019). Institute of Electrical and Electronics Engineers Inc..  link >

Hutt, S., Krasich, K., Mills, C., Bosch, N., White, S., Brockmole, J. R., & 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.  link >

Mills, C., Bosch, N., Krasich, K., & D’Mello, S. K. (2019). Reducing mind-wandering during vicarious learning from an intelligent tutoring system. In S. Isotani, E. Millán, A. Ogan, B. McLaren, P. Hastings, & R. Luckin (Eds.), Artificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings (pp. 296-307). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11625 LNAI). Springer.  link >

Wammes, J. D., Ralph, B. C. W., Mills, C., Bosch, N., Duncan, T. L., & Smilek, D. (2019). Disengagement during lectures: Media multitasking and mind wandering in university classrooms. Computers and Education, 132, 76-89.  link >

Bosch, N., & Paquette, L. (2018). Metrics for Discrete Student Models: Chance Levels, Comparisons, and Use Cases. Journal of Learning Analytics, 5(2), 86-104.  link >

Bosch, N., Wes Crues, R., Henricks, G. M., Perry, M., Angrave, L. C., Shaik, N., Bhat, S. P., & Anderson, C. J. (2018). Modeling key differences in underrepresented students' interactions with an online STEM course. In Proceedings of the Technology, Mind, and Society Conference, TechMindSociety 2018 [a6] (ACM International Conference Proceeding Series). Association for Computing Machinery.  link >

Bosch, N., Mills, C., Wammes, J. D., & Smilek, D. (2018). Quantifying classroom instructor dynamics with computer vision. In M. Mavrikis, C. Penstein Rosé, B. McLaren, H. U. Hoppe, R. Luckin, K. Porayska-Pomsta, B. du Boulay, & R. Martinez-Maldonado (Eds.), Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings (pp. 30-42). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10947 LNAI). Springer.  link >

Crues, R. W., Bosch, N., Perry, M., Angrave, L. C., Shaik, N., & Bhat, S. P. (2018). Refocusing the lens on engagement in MOOCs. In R. Luckin, S. Klemmer, & K. Koedinger (Eds.), Proceedings of the Fifth Annual ACM Conference on Learning at Scale [11] (Proceedings of the 5th Annual ACM Conference on Learning at Scale, L at S 2018). Association for Computing Machinery, Inc.  link >

Jiang, Y., Bosch, N., Baker, R. S., Paquette, L., Ocumpaugh, J., Andres, J. M. A. L., Moore, A. L., & Biswas, G. (2018). Expert feature-engineering vs. Deep neural networks: Which is better for sensor-free affect detection? In C. Penstein Rosé, R. Martínez-Maldonado, H. U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren, & B. du Boulay (Eds.), Artificial Intelligence in Education: 19th International Conference, AIED 2018, London, UK, June 27–30, 2018, Proceedings, Part I (pp. 198-211). (Lecture Notes in Computer Science; Vol. 10947). Springer.  link >

Paquette, L., Bosch, N., Mercier, E. M., Jung, J., Shehab, S., & Tong, Y. (2018). Matching data-driven models of group interactions to video analysis of collaborative problem solving on tablet computers. Proceedings of International Conference of the Learning Sciences, ICLS, 1(2018-June), 312-319.  link >

Wes Crues, R., Bosch, N., Anderson, C. J., Perry, M., Bhat, S., & Shaik, N. (2018). Who they are and what they want: Understanding the reasons for MOOC enrollment. Paper presented at 11th International Conference on Educational Data Mining, EDM 2018, Buffalo, United States.

Bosch, N., & D’Mello, S. (2017). The Affective Experience of Novice Computer Programmers. International Journal of Artificial Intelligence in Education, 27(1), 181-206.  link >

D'Mello, S. K., Mills, C., Bixler, R., & Bosch, N. (2017). Zone Out No More: Mitigating Mind Wandering During Computerized Reading. Paper presented at 2017 International Conference on Educational Data Mining, Wuhan, China.

D’Mello, S. K., Mills, C., Bixler, R., & Bosch, N. (2017). Zone out no more: Mitigating mind wandering during computerized reading. 8-15. Paper presented at 10th International Conference on Educational Data Mining, EDM 2017, Wuhan, China.

Hutt, S., Mills, C., Bosch, N., Krasich, K., Brockmole, J., & D'mello, S. (2017). Out of the Fr-"Eye"-ing Pan: Towards gaze-based models of attention during learning with technology in the classroom. In UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 94-103). (UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization). Association for Computing Machinery, Inc.  link >

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. [7373578].  link >

Stewart, A., Bosch, N., Chen, H., Donnelly, P., & D’Mello, S. (2017). Face forward: Detecting mind wandering from video during narrative film comprehension. In E. Andre, X. Hu, M. M. T. Rodrigo, B. du Boulay, & R. Baker (Eds.), Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings (pp. 359-370). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10331 LNAI). Springer.  link >

Stewart, A., Bosch, N., & D'Mello, S. K. (2017). Generalizability of Face-Based Mind Wandering Detection Across Task Contexts. Paper presented at 2017 International Conference on Educational Data Mining, Wuhan, China.

Bosch, N., D'Mello, S. K., Baker, R. S., Ocumpaugh, J., Shute, V., Ventura, M., Wang, L., & Zhao, W. (2016). Detecting student emotions in computer-enabled classrooms. IJCAI International Joint Conference on Artificial Intelligence, 2016-January, 4125-4129.

Bosch, N. (2016). Detecting student engagement: Human versus machine. In UMAP 2016 - Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization (pp. 317-320). (UMAP 2016 - Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization). Association for Computing Machinery, Inc.  link >

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, 6(2), [17].  link >

Dillon, J., Bosch, N., Chetlur, M., Wanigasekara, N., Ambrose, G. A., Sengupta, B., & D’Mello, S. K. (2016). Student Emotion, Co-occurrence, and Dropout in a MOOC Context. 353-357. Paper presented at 9th International Conference on Educational Data Mining, EDM 2016, Raleigh, United States.

D'Mello, S., Kopp, K., Bixler, R., & Bosch, N. (2016). Attending to attention: Detecting and combating Mind wandering during computerized reading. In CHI EA 2016: #chi4good - Extended Abstracts, 34th Annual CHI Conference on Human Factors in Computing Systems (pp. 1661-1669). (Conference on Human Factors in Computing Systems - Proceedings; Vol. 07-12-May-2016). Association for Computing Machinery,.  link >

Stewart, A., Chen, H., Bosch, N., Donnelly, P. J., & D'Mello, S. K. (2016). Where's your mind at? Video-based mind wandering detection during film viewing. In UMAP 2016 - Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization (pp. 295-296). (UMAP 2016 - Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization). Association for Computing Machinery, Inc.  link >

Bosch, N., Chen, H., Baker, R., Shute, V., & D'mello, S. (2015). Accuracy vs. Availability heuristic in multimodal affect detection in the wild. In ICMI 2015 - Proceedings of the 2015 ACM International Conference on Multimodal Interaction (pp. 267-274). (ICMI 2015 - Proceedings of the 2015 ACM International Conference on Multimodal Interaction). Association for Computing Machinery, Inc.  link >

Bosch, N., D'Mello, S., Baker, R., Ocumpaugh, J., Shute, V., Ventura, M., Wang, L., & Zhao, W. (2015). Automatic detection of learning-centered affective states in the wild. In IUI 2015 - Proceedings of the 20th ACM International Conference on Intelligent User Interfaces (pp. 379-388). (International Conference on Intelligent User Interfaces, Proceedings IUI; Vol. 2015-January). Association for Computing Machinery,.  link >

Bosch, N. (2015). Multimodal affect detection in the wild: Accuracy, availability, and generalizability. In ICMI 2015 - Proceedings of the 2015 ACM International Conference on Multimodal Interaction (pp. 645-649). (ICMI 2015 - Proceedings of the 2015 ACM International Conference on Multimodal Interaction). Association for Computing Machinery, Inc.  link >

Bosch, N., D’Mello, S., Baker, R., Ocumpaugh, J., & Shute, V. (2015). Temporal generalizability of face-based affect detection in noisy classroom environments. In C. Conati, N. Heffernan, A. Mitrovic, & M. Felisa Verdejo (Eds.), Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings (pp. 44-53). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9112). Springer.  link >

Chen, Y., Bosch, N., & D'Mello, S. K. (2015). Video-based Affect Detection in Noninteractive Learning Environments. Paper presented at 2015 International Conference of Educational Data Mining, Madrid, Spain.

Kai, S., Paquette, L., Baker, R. S., Bosch, N., D'Mello, S. K., Ocumpaugh, J., Shute, V. J., & Ventura, M. (2015). A Comparison of Face-based and Interaction-based Affect Detectors in Physics Playground. Paper presented at 2015 International Conference of Educational Data Mining, Madrid, Spain.

Mills, C., D’Mello, S., Bosch, N., & Olney, A. M. (2015). Mind wandering during learning with an intelligent tutoring system. In C. Conati, N. Heffernan, A. Mitrovic, & M. Felisa Verdejo (Eds.), Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings (pp. 267-276). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9112). Springer.  link >

Shute, V. J., D'Mello, S., Baker, R., Cho, K., Bosch, N., Ocumpaugh, J., Ventura, M., & Almeda, V. (2015). Modeling how incoming knowledge, persistence, affective states, and in-game progress influence student learning from an educational game. Computers and Education, 86, 224-235.  link >

Bosch, N., Chen, Y., & D'Mello, S. (2014). It's written on your face: Detecting affective states from facial expressions while learning computer programming. In Intelligent Tutoring Systems - 12th International Conference, ITS 2014, Proceedings (pp. 39-44). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8474 LNCS). Springer.  link >

Bosch, N., & D'Mello, S. (2014). It takes two: Momentary co-occurrence of affective states during computerized learning. In Intelligent Tutoring Systems - 12th International Conference, ITS 2014, Proceedings (pp. 638-639). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8474 LNCS). Springer.  link >

Mills, C., Bosch, N., Graesser, A., & D'Mello, S. (2014). To quit or not to quit: Predicting future behavioral disengagement from reading patterns. In Intelligent Tutoring Systems - 12th International Conference, ITS 2014, Proceedings (pp. 19-28). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8474 LNCS). Springer.  link >

Rodeghero, P., McMillan, C., McBurney, P. W., Bosch, N., & D'Mello, S. (2014). Improving automated source code summarization via an eye-tracking study of programmers. Proceedings - International Conference on Software Engineering, (1), 390-401.  link >

Bosch, N., & D'Mello, S. (2013). Programming with your heart on your sleeve: Analyzing the affective states of computer programming students. In Artificial Intelligence in Education - 16th International Conference, AIED 2013, Proceedings (pp. 908-911). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7926 LNAI). Springer.  link >

Bosch, N., & D'Mello, S. (2013). Sequential patterns of affective states of novice programmers. CEUR Workshop Proceedings, 1009, 1-10.

Bosch, N., D'Mello, S., & Mills, C. (2013). What emotions do novices experience during their first computer programming learning session? In Artificial Intelligence in Education - 16th International Conference, AIED 2013, Proceedings (pp. 11-20). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7926 LNAI). Springer.  link >

Mills, C., D'Mello, S., Lehman, B., Bosch, N., Strain, A., & Graesser, A. (2013). What makes learning fun? exploring the influence of choice and difficulty on mind wandering and engagement during learning. In Artificial Intelligence in Education - 16th International Conference, AIED 2013, Proceedings (pp. 71-80). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7926 LNAI). Springer.  link >

Courses

Concepts of Machine Learning (EPSY 395) Study of problems not considered in other courses; designed for students who excel in self-direction and intellectual curiosity. A dramatic increase in computing power has enabled new areas of data science to develop in statistical modeling and artificial intelligence, often called Machine Learning. Machine learning covers predictive and descriptive learning, and bridges theoretical and empirical ideas across disciplines. We will focus on concepts and methods for predictive learning: estimating models from data to predict unknown outcomes. Model types will include decision trees, linear models, nearest neighbor methods, and others as time permits. We will cover classification and regression using these models, as well as methods needed to handle large datasets. Lastly, we will discuss deep neural networks and other methods at the forefront of machine learning. We situate the course components in the "data science life cycle" as part of the larger set of practices in the discovery and communication of scientific findings. The course will include lectures, readings, homework assignments, exams, and a class project