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Luc Paquette

Biography

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.

Key Professional Appointments

  • Assistant Professor, Curriculum and Instruction, University of Illinois, Urbana-Champaign
  • Assistant Professor, National Center for Supercomputing Applications (NCSA), University of Illinois, Urbana-Champaign
Education

Ph.D., Computer Science, Université de Sherbrooke, 2013

Research & Service

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.

Publications

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 >

Baker, R. S., Nasiar, N., Ocumpaugh, J. L., Hutt, S., Andres, J. M. A. L., Slater, S., Schofield, M., Moore, A., Paquette, L., Munshi, A., & Biswas, G. (2021). Affect-Targeted Interviews for Understanding Student Frustration. In I. Roll, D. McNamara, S. Sosnovsky, R. Luckin, & V. Dimitrova (Eds.), Artificial Intelligence in Education - 22nd International Conference, AIED 2021, Proceedings (pp. 52-63). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12748 LNAI). Springer.  link >

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 >

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.

Paquette, L., Grant, T., Zhang, Y., Biswas, G., & Baker, R. (2021). Using Epistemic Networks to Analyze Self-regulated Learning in an Open-Ended Problem-Solving Environment. In A. R. Ruis, & S. B. Lee (Eds.), Advances in Quantitative Ethnography - Second International Conference, ICQE 2020, Proceedings (pp. 185-201). (Communications in Computer and Information Science; Vol. 1312). Springer.  link >

Pinto, J. D., Zhang, Y., Paquette, L., & Fan, A. X. (2021). Investigating Elements of Student Persistence in an Introductory Computer Science Course. CEUR Workshop Proceedings, 3051.

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 >

Haniya, S., & Paquette, L. (2020). Understanding learner participation at scale: How and why. E-Learning and Digital Media, 17(3), 236-252.  link >

Henderson, N., Rowe, J., Paquette, L., Baker, R. S., & Lester, J. (2020). Improving affect detection in game-based learning with multimodal data fusion. 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. 228-239). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12163 LNAI). Springer.  link >

Munshi, A., Mishra, S., Zhang, N., Paquette, L., Ocumpaugh, J., Baker, R., & Biswas, G. (2020). Modeling the Relationships Between Basic and Achievement Emotions in Computer-Based Learning Environments. In I. I. Bittencourt, M. Cukurova, R. Luckin, K. Muldner, & E. Millán (Eds.), Artificial Intelligence in Education: 21st International Conference, AIED 2020, Ifrane, Morocco, July 6–10, 2020, Proceedings, Part I (pp. 411-422). (Lecture Notes in Computer Science; Vol. 12163). Springer.  link >

Paquette, L., & Romero, C. (2020). Joint proceedings of the EDM 2019 workshops. CEUR Workshop Proceedings, 2592.

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 >

Paquette, L., Ocumpaugh, J., Li, Z., Andres, A., & Baker, R. (2020). Who's learning? Using demographics in EDM research. Journal of Educational Data Mining, 12(3), 1-30.  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 >

Paquette, L., & Baker, R. S. (2019). Comparing machine learning to knowledge engineering for student behavior modeling: a case study in gaming the system. Interactive Learning Environments, 27(5-6), 585-597.  link >

Rowe, J., Mott, B., Paquette, L., & Lee, S. (2019). EDM & games: Leveling up engaged learning with data-rich analytics. In C. F. Lynch, A. Merceron, M. Desmarais, & R. Nkambou (Eds.), EDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining (pp. 775-776). (EDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining). 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.  link >

DeFalco, J. A., Rowe, J. P., Paquette, L., Georgoulas-Sherry, V., Brawner, K., Mott, B. W., Baker, R. S., & Lester, J. C. (2018). Detecting and Addressing Frustration in a Serious Game for Military Training. International Journal of Artificial Intelligence in Education, 28(2), 152-193.  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 >

Jiang, Y., Clarke-Midura, J., Baker, R. S., Paquette, L., & Keller, B. (2018). How Immersive Virtual Environments Foster Self-Regulated Learning. In R. Zheng (Ed.), Digital Technologies and Instructional Design for Personalized Learning (pp. 28-54). IGI Global.  link >

Jiang, Y., Clarke-Midura, J., Keller, B., Baker, R. S., Paquette, L., & Ocumpaugh, J. (2018). Note-taking and science inquiry in an open-ended learning environment. Contemporary Educational Psychology, 55, 12-29.  link >

Munshi, A., Rajendran, R., Penn, J. O., Biswas, G., Baker, R. S., & Paquette, L. (2018). Modeling learners' cognitive and affective states to scaffold srl in open-ended learning environments. In UMAP 2018 - Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (pp. 131-138). (UMAP 2018 - Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization). Association for Computing Machinery, Inc.  link >

Paquette, L., Baker, R. S., & Moskal, M. (2018). A system-general model for the detection of gaming the system behavior in CTAT and LearnSphere. In R. Luckin, K. Porayska-Pomsta, B. du Boulay, M. Mavrikis, C. Penstein Rosé, B. McLaren, R. Martinez-Maldonado, & H. U. Hoppe (Eds.), Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings (pp. 257-260). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10948 LNAI). 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 >

Biswas, G., Baker, R., & Paquette, L. (2017). Data Mining Methods for Assessing Self-Regulated Learning. In D. H. Schunk, & J. A. Greene (Eds.), Handbook of Self-Regulation of Learning and Performance (2 ed., pp. 388-403). Routledge.  link >

Hu, X., Barnes, T., Hershkovitz, A., & Paquette, L. (2017). Preface. Proceedings of the 10th International Conference on Educational Data Mining, EDM 2017, ii.

Hu, X., Barnes, T., Hershkovitz, A., & Paquette, L. (2017). Preface. Proceedings of the 10th International Conference on Educational Data Mining, EDM 2017, ii.

Kai, S., Andres, J. M. L., Paquette, L., Baker, R. S., Molnar, K., Watkins, H., & Moore, M. (2017). Predicting student retention from behavior in an online orientation course. 250-255. Paper presented at 10th International Conference on Educational Data Mining, EDM 2017, Wuhan, China.

Ocumpaugh, J., Andres, J. M., Baker, R., DeFalco, J., Paquette, L., Rowe, J., Mott, B., Lester, J., Georgoulas, V., Brawner, K., & Sottilare, R. (2017). Affect dynamics in military trainees using vMedic: From engaged concentration to boredom to confusion. 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. 238-249). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10331 LNAI). Springer.  link >

Paquette, L., & Baker, R. S. (2017). Variations of gaming behaviors across populations of students and across learning environments. 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. 274-286). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10331 LNAI). Springer.  link >

Wang, Y., Baker, R. S., & Paquette, L. (2017). Behavioral predictors of MOOC post-course development. CEUR Workshop Proceedings, 1967, 100-111.

Wang, Y., Davis, D., Chen, G., & Paquette, L. (2017). Workshop on integrated learning analytics of MOOC post-course development. In LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data (pp. 506-507). (ACM International Conference Proceeding Series). Association for Computing Machinery.  link >

Wang, Y., Davis, D., Chen, G., & Paquette, L. (2017). Workshop on integrated learning analytics of MOOC post-course development. CEUR Workshop Proceedings, 1967, 95-99.

Baker, R. S., Wang, Y., Paquette, L., Aleven, V., Popescu, O., Sewall, J., Rosé, C., Tomar, G. S., Ferschke, O., Zhang, J., Cennamo, M. J., Ogden, S., Condit, T., Diaz, J., Crossley, S., McNamara, D. S., Comer, D. K., Lynch, C. F., Brown, R., ... Bergner, Y. (2016). Educational Data Mining: A MOOC Experience. In S. ElAtia, D. Ipperciel, & O. R. Zaïane (Eds.), Data Mining And Learning Analytics: Applications in Educational Research (pp. 55-66). Wiley-Blackwell.  link >

Crossley, S., Mcnamara, D. S., Paquette, L., Baker, R. S., & Dascalu, M. (2016). Combining click-Stream data with NLP tools to better understand MOOC completion. In LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation (pp. 6-14). (ACM International Conference Proceeding Series; Vol. 25-29-April-2016). Association for Computing Machinery.  link >

Malkiewich, L., Baker, R. S., Shute, V., Kai, S., & Paquette, L. (2016). Classifying behavior to elucidate elegant problem solving in an educational game. 448-453. Paper presented at 9th International Conference on Educational Data Mining, EDM 2016, Raleigh, United States.

Zhu, M., Bergner, Y., Zhang, Y., Baker, R., Wang, Y., & Paquette, L. (2016). Longitudinal engagement, performance, and social connectivity: A MOOC case study using exponential random graph models. In LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation (pp. 223-230). (ACM International Conference Proceeding Series; Vol. 25-29-April-2016). Association for Computing Machinery.  link >

Andres, J. M. L., Rodrigo, M. M. T., Baker, R. S., Paquette, L., Shute, V. J., & Ventura, M. (2015). Analyzing student action sequences and affect while playing physics playground. CEUR Workshop Proceedings, 1446.

Andres, J. M. L., Rodrigo, M. M. T., Baker, R. S., Paquette, L., Shute, V. J., & Ventura, M. (2015). Analyzing student action sequences and affect while playing Physics Playground. CEUR Workshop Proceedings, 1432, 24-33.

Jiang, Y., Baker, R. S., Paquette, L., Pedro, M. S., & Heffernan, N. T. (2015). Learning, moment-by-moment and over the long term. In C. Conati, N. Heffernan, A. Mitrovic, & M. Felisa Verdejo (Eds.), Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings (pp. 654-657). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9112). Springer.  link >

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.

Paquette, L., Baker, R. S., de Carvalho, A., & Ocumpaugh, J. (2015). Cross-system transfer of machine learned and knowledge engineered models of gaming the system. In K. Bontcheva, F. Ricci, O. Conlan, & S. Lawless (Eds.), User Modeling, Adaptation and Personalization - 23rd International Conference, UMAP 2015, Proceedings (pp. 189-194). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9146). Springer.  link >

Paquette, L., Lebeau, J. F., 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.  link >

Andres, J. M. L., Rodrigo, M. M. T., Sugay, J. O., Baker, R. S., Paquette, L., Shute, V. J., Ventura, M., & Small, M. (2014). An exploratory analysis of confusion among students using Newton's playground. In H. Ogata, L. Lomicka-Anderson, C-S. Chai, R. Hampel, Y. Hayashi, J. Vassileva, C-C. Liu, W. Chen, J. Hsu, Y-J. Lan, J. Mason, M. Yamada, H-Y. Shyu, A. Weerasinghe, Y-T. Wu, L. Zhang, Kinshuk, Y. Matsubara, Y. Miao, H. Ogata, S. C. Kong, M. Chang, M. S. Y. Jong, R. Kuo, R. Robson, B. Wasson, A. Kashihara, U. Cress, M. Jansen, J. Oshima, C. Yin, J. Zhang, ... C. Chinn (Eds.), Proceedings of the 22nd International Conference on Computers in Education, ICCE 2014 (pp. 65-70). (Proceedings of the 22nd International Conference on Computers in Education, ICCE 2014). Asia-Pacific Society for Computers in Education.

Paquette, L., Baker, R. S. J. D., Sao Pedro, M. A., Gobert, J. D., Rossi, L., Nakama, A., & Kauffman-Rogoff, Z. (2014). Sensor-free affect detection for a simulation-based science inquiry learning environment. In Intelligent Tutoring Systems - 12th International Conference, ITS 2014, Proceedings (pp. 1-10). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8474 LNCS). Springer.  link >

Pedro, M. S., Jiang, Y., Paquette, L., Baker, R. S., & Gobert, J. (2014). Identifying transfer of inquiry skills across physical science simulations using educational data mining. Proceedings of International Conference of the Learning Sciences, ICLS, 1(January), 222-229.

Wang, Y., Paquette, L., & Baker, R. (2014). A Longitudinal Study on Learner Career Advancement in MOOCs. Journal of Learning Analytics, 1(3), 203-206.  link >

Paquette, L., Lebeau, J. F., & Mayers, A. (2013). Authoring problem-solving ITS with ASTUS: An interactive event. In Artificial Intelligence in Education - 16th International Conference, AIED 2013, Proceedings (pp. 934-935). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7926 LNAI). Springer.  link >

Paquette, L., Lebeau, J. F., & Mayers, A. (2013). Diagnosing errors from off-path steps in model-tracing tutors. In Artificial Intelligence in Education - 16th International Conference, AIED 2013, Proceedings (pp. 611-614). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7926 LNAI). Springer.  link >

Paquette, L., Lebeau, J. F., Beaulieu, G., & Mayers, A. (2012). Automating next-step hints generation using ASTUS. In Intelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings (pp. 201-211). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7315 LNCS).  link >

Paquette, L., Lebeau, J. F., & Mayers, A. (2012). Automating the modeling of learners' erroneous behaviors in model-tracing tutors. In User Modeling, Adaptation, and Personalization - 20th International Conference, UMAP 2012, Proceedings (pp. 316-321). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7379 LNCS).  link >

Lebeau, J. F., Paquette, L., & Mayers, A. (2011). Authoring step-based ITS with ASTUS: An interactive event. In Artificial Intelligence in Education - 15th International Conference, AIED 2011 (pp. 622). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6738 LNAI).  link >

Paquette, L., Lebeau, J. F., Mbungira, J. P., & Mayers, A. (2011). Generating task-specific next-step hints using domain-independent structures. In Artificial Intelligence in Education - 15th International Conference, AIED 2011 (pp. 525-527). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6738 LNAI).  link >

Lebeau, J. F., Paquette, L., Fortin, M., & Mayers, A. (2010). An authoring language as a key to usability in a problem-solving ITS framework. In Intelligent Tutoring Systems - 10th International Conference, ITS 2010, Proceedings (PART 2 ed., pp. 236-238). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6095 LNCS, No. PART 2).  link >

Lebeau, J. F., Paquette, L., & Mayers, A. (2010). Authoring problem-solving ITS with ASTUS. In Intelligent Tutoring Systems - 10th International Conference, ITS 2010, Proceedings (PART 2 ed., pp. 450). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6095 LNCS, No. PART 2).  link >

Paquette, L., Lebeau, J. F., & Mayers, A. (2010). Authoring Problem-Solving Tutors: A Comparison between ASTUS and CTAT. In R. Nkambou, J. Bourdeau, & R. Mizoguchi (Eds.), Advances in Intelligent Tutoring Systems (pp. 377-405). (Studies in Computational Intelligence; Vol. 308). Springer.  link >

Paquette, L., Lebeau, J. F., & Mayers, A. (2010). Integrating sophisticated domain-independent pedagogical behaviors in an ITS framework. In Intelligent Tutoring Systems - 10th International Conference, ITS 2010, Proceedings (PART 2 ed., pp. 248-250). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6095 LNCS, No. PART 2).  link >

Boisvert, A. A., Paquette, L., Pigot, H., & Giroux, S. (2009). Design challenges for mobile assistive technologies applied to people with cognitive impairments. In Ambient Assistive Health and Wellness Management in the Heart of the City - 7th International Conference on Smart Homes and Health Telematics, ICOST 2009, Proceedings (pp. 17-24). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5597 LNCS).  link >

Lebeau, J. F., Fortin, M., Paquette, L., & Mayers, A. (2009). From cognitive to pedagogical knowledge models in problem-solving ITS frameworks. In Frontiers in Artificial Intelligence and Applications (1 ed., pp. 731-733). (Frontiers in Artificial Intelligence and Applications; Vol. 200, No. 1). IOS Press.  link >

Courses

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.

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. Synchronous attendance required. Moodle LMS.

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. Synchronous attendance required. Moodle LMS.

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 CS for CS Teachers (CI 480) This course introduces the core concepts of computer science and computer programming for students to gain experience creating programs using text-based programming languages. It also provides opportunities for students to reflect on how they experience learning those concepts and how this might impact teaching high school students. Students will learn about the fundamentals of how programs are executed and how to store and process data using computers. They will be introduced to the concepts of algorithms, algorithm execution time, and the core concepts of object-oriented programming.

Data Struc for CS Teachers (CI 487) Teaches the fundamentals of data structures and provides opportunities for students to reflect on the importance of data structure knowledge when teaching computer science to high school students. Students will learn the fundamentals of how computers store collections of data, the advantages and disadvantages of different data structures and the importance of selecting the appropriate data representation when designing computer programs. Students will learn how to program various common data structures. Students will develop their computer programming abilities and learn computer programming concepts that are important when developing efficient and reusable data structures. Students will increase their knowledge of object-oriented programming through learning about inheritance and generic data types. Students will learn about dynamic memory management. 4 undergraduate hours. 4 graduate hours.