Faculty Profiles
Luc Paquette is an assistant professor in the department of curriculum & instruction. He completed his PhD in computer science at the University of Sherbrooke where he studied the design knowledge representations for intelligent tutoring systems and the use of those representations to automatically generate pedagogical content. After his PhD, professor Paquette worked as a post-doctoral research associate at Teachers College, Columbia University where he used educational data mining techniques and knowledge engineering techniques to study the behavior of students using digital learning environment.
Ph.D., Computer Science, University of Sherbrooke, 2013
Professor Paquette is interested in the study of students' behaviors as they use digital learning environments. To achieve this objective, he applies educational data mining, learning analytics and knowledge engineering approaches to identify and model pedagogically relevant behaviors. Professor Paquette has studied multiple different behaviors, including misuse of digital learning environments, emotions, self-regulated learning and collaborative learning, across varied environments, including Intelligent Tutoring Systems, educational games, online courses and scientific simulation microworlds. He is interested in the usage of models of student behavior to study the relationship between those behaviors and learning outcomes, improve the design of digital learning environments and support the teachers’ usage of those environments in their classroom.
Zhang, Y., Paquette, L., Baker, R., Ocumpaugh, J., Bosch, P., Munshi, A., & Biswas, G. (2020). The Relationship Between Confusion and Metacognitive Strategies in Betty’s Brain. Proceedings of the 10th International Conference on Learning Analytics & Knowledge.
Andres, A., Ocumpaugh, J., Baker, R., Slater, S., Paquette, L., Jiang, Y., Karumbaiah, S., Bosch, P., Munshi, A., Moore, A., & Biswas, G. (2019). Affect Sequences and Learning in Betty's Brain. Proceedings of the 9th International Conference on Learning Analytics & Knowledge ( pp. 383-390).
Andres, J., Rodrigo, M., Baker, R., Paquette, L., Shute, V., & Ventura, M. (2015). Analyzing Student Action Sequences and Affect While Playing Physics Playground. Proceedings of the International Workshop on Affect, Meta-Affect, Data and Learning.
Baker, R., DeFalco, J., Paquette, L., Georgoulas, V., Rowe, J., Mott, B., & Lester, J. (2015). Motivational Feedback Designs for Frustration in a Simulation-based Combat Medic Training Environment. ( pp. 81-88). Proceedings of the 3rd Annual GIFT Users Symposium.
Beaulieu, G., Paquette, L., & Mayers, A. (2015). Un modèle pour la gestion automatisé de buts de succès dans les tuteurs par traçage de modèle. Proceedings of the 7th Conference on Environnement Intelligent pour l'Apprentissage Humain.
Crossley, S., McNamara, D., Baker, R., Wang, Y., Paquette, L., Barnes, T., & Bergner, Y. (2015). Language to Completion: Success in Educational Data Mining Massive Open Online Class. Proceedings of the 8th International Conference on Educational Data Mining.
Jiang, Y., Baker, R., Paquette, L., San Pedro, M., & Heffernan, N. (2015). Learning, Moment-by-Moment and Over the Long Term. Proceedings of the 17th International Conference on Artificial Intelligence in Education.
Jiang, Y., Paquette, L., Baker, R., & Clarke-Midura, J. (2015). Comparing Novice and Experienced Students in Virtual Performance Assessments. Proceedings of the 8th International Conference on Educational Data Mining.
Kai, S., Paquette, L., Baker, R., Bosch, N., D'Mello, S., Ocumpaugh, J., Shute, V., & Ventura, M. (2015). A Comparison of Face-based and Interaction-based Affect Detectors in Physics Playground. Proceedings of the 8th International Conference on Educational Data Mining.
Paquette, L., Baker, R., de Carvalho, A., & Ocumpaugh, J. (2015). Cross-System Transfer of Machine Learned and Knowledge Engineered Models of Gaming the System. Proceedings of the 23rd Conference on User Modelling, Adaptation and Personalization.
Paquette, L., Lebeau, J., Beaulieu, G., & Mayers, A. (2015). Designing a Knowledge Representation Approach for the Generation of Pedagogical Interventions by MTTs. International Journal of Artificial Intelligence in Education, 25 (1), 118-156.
Paquette, L., Ocumpaugh, J., & Baker, R. (2015). Simulating Multi-Subject Momentary Time Sampling. ( pp. 586-587). Proceedings of the 8th International Conference on Educational Data Mining.
Paquette, L., Rowe, J., Baker, R., Mott, B., Lester, J., DeFalco, J., Brawner, K., Sottilare, R., & Georgoulas, V. (2015). Sensor-Free or Sensor-Full: A Comparison of Data Modalities in Multi-Channel Affect Detection. Proceedings of the 8th International Conference on Educational Data Mining.
Wang, Y., Paquette, L., & Baker, R. (2014). A Longitudinal Study on Learner Career Advancement in MOOCs. Journal of Learning Analytics, 1 (3), 203-206.
Andres, J., Rodrigo, M., Sugay, J., Baker, R., Paquette, L., Shute, V., Ventura, M., & Small, M. (2014). An Exploratory Analysis of Confusion Among Students Using Newton's Playground. Proceedings of the 22nd International Conference on Computers in Education.
Baker, R., DeFalco, J., Ocumpaugh, J., & Paquette, L. (2014). Towards Detection of Engagement and Affect in a Simulation-Based Combat Medic Training Environment. 2nd Annual GIFT User Symposium (GIFTSym2.
Paquette, L., Baker, R., Sao Pedro, M., Gobert, J., Rossi, L., Nakama, A., & Kauffman-Rogoff, Z. (2014). Sensor-Free Affect Detection for a Simulation-Based Science Inquiry Learning Environment. ( pp. 1-10). Proceedings of the 12th International Conference on Intelligent Tutoring Systems.
Paquette, L., de Carvalho, A., & Baker, R. (2014). Achieving a Better Understanding of how Expert Classify Students Gaming and Intelligent Tutor Through Cognitive Modeling. ( pp. 1126-1131). Proceedings of the 36th Annual Cognitive Science Conference.
Paquette, L., de Carvalho, A., Baker, R., & Ocumpaugh, J. (2014). Reengineering the Feature Distillation Process: A Case Study in the Detection of Gaming the System. ( pp. 284-287). Proceedings of the 7th International Conference on Educational Data Mining.
Sao Pedro, M., Jiang, Y., Paquette, L., Baker, R., & Gobert, J. (2014). Identifying Transfer of Inquiry Skills Across Physical Science Simulations Using Educational Data Mining. Proceedings of the 11th International Conference of the Learning Sciences.
Paquette, L., Lebeau, J., & Mayers, A. (2013). Diagnosing Errors from Off-Path Steps in Model-Tracing Tutors. Proceedings of the 16th Conference on Artificial Intelligence in Education.
Paquette, L., Lebeau, J., & Mayers, A. (2013). La plateforme Astus : Une alternative aux systèmes de productions pour la création de tuteur par traçage de modèle. Proceedings of the 6th Conference on Environnement Intelligent pour l'Apprentissage Humain.
Paquette, L., Lebeau, J., & Mayers, A. (2012). Automating the Modeling of Learners' Erroneous Behaviors in Model-Tracing Tutors. ( pp. 316-321). Proceedings of the 20th International Conference on User Modeling, Adaptation and Personalization.
Paquette, L., Lebeau, J., Beaulieu, G., & Mayers, A. (2012). Automating Next-Step Hints Generation Using ASTUS. ( pp. 201-211). Proceedings of the 11th International Conference on Intelligent Tutoring Systems.
Paquette, L., Lebeau, J., Mbungira, J., & Mayers, A. (2011). Generating Task-Specific Next-Step Hints Using Domain-Independent Structures. ( pp. 525-527). Proceedings of the 15th International Conference on Artificial Intelligence in Education.
Lebeau, J., Paquette, L., Fortin, M., & Mayers, A. (2010). An Authoring Language as a Key to Usability in a Problem-Solving ITS Framework. ( pp. 236-238). Proceedings of the 10th International Conference on Intelligent Tutoring Systems (part II.
Paquette, L., Lebeau, J., & Mayers, A. (2010). Authoring Problem-Solving Tutors: A Comparison Between ASTUS and CTAT. ( pp. 377-405). Advances in Intelligent Tutoring System.
Paquette, L., Lebeau, J., & Mayers, A. (2010). Integrating Sophisticated Domain-Independent Pedagogical Behaviors in an ITS Framework. ( pp. 248-250). Proceedings of the 10th International Conference on Intelligent Tutoring Systems (part II.
Boisvert, A., Paquette, L., Pigot, H., & Giroux, S. (2009). Design Challenges for Mobile Assistive Technologies Applied to People with Cognitive Impairments. ( pp. 17-24). Proceedings of the 7th International Conference on Smart Homes and Health Telematics.
Lebeau, J., Fortin, M., Paquette, L., & Mayers, A. (2009). From Cognitive to Pedagogical Knowledge Models in Problem-Solving ITS Frameworks. ( pp. 731-733). Proceedings of the 14th International Conference on Artificial Intelligence in Education.
Automating the Engineering of Expert-Coded Features from Student Interaction Log Data (2019).: Madison, WI.
Intro to Digital Learning Env (CI 210) Surveys the field of digital environments and their capacity to support teaching and learning. Examines theories of interactivity, immersion, learning with multi-media, and digital literacies to discuss and evaluate various digital environments. Students learn to critically assess digital environments and to create original prototypes that target a specific and important learning or teaching goal. Environments that will be discussed and experimented with in class include virtual worlds, social networks, digital classrooms, interactive exhibits, video games, and tangible technologies.
Educational Game Design (CI 437) Examines the role that physical and digital games play in learning. Focuses on how people learn through play and how game structures support educational outcomes. Principles of game design are described and students apply them to the design of original games with a specified educational objective. Students learn to prototype, playtest, and evaluate the educational content of games. Surveys and samples games in the areas of serious games, persuasive games, games for impact, etc.
Comp Prgrmmg and the Classroom (CI 438) This course will introduces educators to the theoretical, pedagogical, and practical aspects of teaching computer programming in the K-12 setting. It will explore how computer science topics and concepts can impact learning, and offer practical strategies and resources to help teachers incorporate computer programming into their practice.
Intro to Educ Data Mining (CI 507) Intensive examination of problems and trends in the subject fields.
Methods of Educational Inquiry (CI 550) Offers a graduate-level introduction to research in education, including quantitative, qualitative and mixed methods designs and approaches. Key concepts include: identifying a research problem, reviewing the literature, design and analysis, communicating evidence, and the ethics of research. Students should gain the ability to effectively evaluate and critique design/methods sections of research publications; plan and design research studies; and organize a presentation of research to an audience of peers.
Methods of Educational Inquiry (EPOL 550)
Methods of Educational Inquiry (EPSY 550)
Assistant Professor, Curriculum & Instruction
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