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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

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Education

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

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Publications

Haniya, S., & Paquette, L. (2020). Understanding learner participation at scale: How and why. E-Learning and Digital Media, 17(3), 236-252. https://doi.org/10.1177/2042753019900963  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. https://doi.org/10.1007/978-3-030-52237-7_19  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 Nature Switzerland AG. https://doi.org/10.1007/978-3-030-52237-7_33  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. https://doi.org/10.4018/978-1-5225-9304-1.ch019  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. https://doi.org/10.1145/3375462.3375518  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,. https://doi.org/10.1145/3303772.3303807  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. https://doi.org/10.1080/10494820.2019.1610450  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. https://doi.org/10.18608/jla.2018.52.6  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. https://doi.org/10.1007/s40593-017-0152-1  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-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-319-93843-1_15  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. https://doi.org/10.4018/978-1-5225-3940-7.ch002  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. https://doi.org/10.1016/j.cedpsych.2018.08.004  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. https://doi.org/10.1145/3209219.3209241  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-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-319-93846-2_47  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.

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. https://doi.org/10.4324/9781315697048-25  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-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-319-61425-0_20  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-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-319-61425-0_23  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. https://doi.org/10.1145/3027385.3029430  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. https://doi.org/10.1002/9781118998205.ch4  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. https://doi.org/10.1145/2883851.2883931  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. https://doi.org/10.1145/2883851.2883934  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-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-319-19773-9_84  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-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-319-20267-9_15  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. https://doi.org/10.1007/s40593-014-0030-z  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-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-319-07221-0_1  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. https://doi.org/10.18608/jla.2014.13.23  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). https://doi.org/10.1007/978-3-642-39112-5-151  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). https://doi.org/10.1007/978-3-642-39112-5-71  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). https://doi.org/10.1007/978-3-642-30950-2_26  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). https://doi.org/10.1007/978-3-642-31454-4_28  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 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6738 LNAI). https://doi.org/10.1007/978-3-642-21869-9_123  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). https://doi.org/10.1007/978-3-642-21869-9_90  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). https://doi.org/10.1007/978-3-642-13437-1_30  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.). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6095 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-13437-1_107  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-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-642-14363-2_19  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). https://doi.org/10.1007/978-3-642-13437-1_34  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). https://doi.org/10.1007/978-3-642-02868-7_3  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. https://doi.org/10.3233/978-1-60750-028-5-731  link >

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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.

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)

Methods of Educational Inquiry (EPSY 573)

Methods of Educational Inquiry (SPED 550)

Profile Picture for Luc Paquette

Assistant Professor, Curriculum & Instruction

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Office

383 Education Building
1310 S. Sixth St.
Champaign, IL 61820

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