Publications

Papers by year of publication

2024

Fan, A.X., Liu, Q., Paquette, L., & Pinto, J. (2024). Using LLM-Based Filtering to Develop Reliable Coding Schemes for Rare Debugging Strategies. In Proceedings of Advances in Quantitative Ethnography (ICQE 2024) (pp. 136-151). https://doi.org/10.1007/978-3-031-76335-9_10
Lawrence, L., Mercier, M., Parks, T.T., Bosch, N., & Paquette, L. (2024). Accuracy and effectiveness of an orchestration tool on instructors’ interventions and groups’ collaboration. Computers and Education Open, 7, 100203. https://doi.org/10.1016/j.caeo.2024.100203
Liu, Q., Pinto, J.D., & Paquette, L. (2024). Applications of Explainable AI (XAI) in Education. In Trust and Inclusion in AI-Mediated Education: Where Human Learning Meets Learning Machines (pp. 93-109). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-64487-0_5
Pinto, J., & Paquette, L. (2024). Deep Learning for Educational Data Science. In Trust and Inclusion in AI-Mediated Education: Where Human Learning Meets Learning Machines (pp. 111-139). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-64487-0_6
Pinto, J.D., & Paquette, L. (2024). Towards a Unified Framework for Evaluating Explanation. Presented at HEXED Workshop @ EDM 24. https://arxiv.org/abs/2405.14016
Pinto, J., Paquette, L., Swamy, V., Käser, T., Liu, Q., & Cohausz (2024). Human-Centric eXplainable AI in Education. In Proceedings of the 17th Educational Data Mining Conference (pp. 1030-1033). https://doi.org/10.5281/zenodo.12730045
Verstege, S., Zhang, Y., Wierenga, P., Paquette, L., & Diederen, J. (2023). Using Sequential Pattern Mining to Understand How Students Use Guidance While Doing Scientific Calculations. Technology, Knowledge and Learning, 29(2), 897-920. https://doi.org/10.1007/s10758-023-09677-3
Zhang, J., & Paquette, L. (2024). An Exploratory Analysis of Students' Problem-Solving Strategies in the Water Cycle Game. In Proceedings of the 17th Educational Data Mining Conference (pp. 828-834). https://doi.org/10.5281/zenodo.12729964
Zhang, Y., Paquette, L., & Bosch, N. (2024). Conditional and Marginal Strengths of Affect Transitions During Computer-Based Learning. International Journal of Artificial Intelligence in Education, 1-29. https://doi.org/10.1007/s40593-024-00430-0
Zhang, Y., Paquette, L., & Bosch, N. (2024). Using Permutation Tests to Identify Statistically Sound and Nonredundant Sequential Patterns in Educational Even Sequences. Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/10769986241248772
Zhang, Y., Paquette, L., & Hu, X. (2024). Academic procrastination, incentivized and self-selected spaced practice, and quiz performance in an online programming problem system: An intensive longitudinal investigation. Computers & Education. https://doi.org/10.1016/j.compedu.2024.105029
Zhang, Y., Ye, Y., Paquette, L., & Hu, X. (2024). Investigating the reliability of aggregate measurements of learning process data: From theory to practice. Journal of Computer Assisted Learning. https://doi.org/10.1111/jcal.12951
Zhou, Y., & Paquette, L. (2024). Investigating Student Interest in a Minecraft-Based Learning Environment: A Changepoint Detection Analysis. In Proceedings of the 17th Educational Data Mining Conference (pp. 396-404). https://doi.org/10.5281/zenodo.12729844

2023

Baker, R. S., Hutt, S., Bosch, N., Ocumpaugh, J., Biswas, G., Paquette, L., Andres, J.M.A., Nasiar, N., & Munshi, A. (2023). Detector-driven classroom interviewing: focusing qualitative researcher time by selecting cases in situ. Educational Technology Resarch and Development, Special Issue on Methodologies for Research on Educational Technology: Emerging Appraoches. https://doi.org/10.1007/s11423-023-10324-y
Fan, A., Zhang, H., Paquette, L., & Zhang, R. (2023). Exploring the Potential of Large Language Models in Generating Code-Tracing Questions for Introductory Programming Courses. Findings of the Association for Computational Linguistics: EMNLP 2023. https://doi.org/10.18653/v1/2023.findings-emnlp.496
Liu, Q., & Paquette, L. (2023). Using submission log data to investigate novice programmers’ employment of debugging strategies. In LAK23: 13th International Learning Analytics and Knowledge Conference (pp. 637-643). https://doi.org/10.1145/3576050.3576094
Munshi, A., Biswas, G., Baker, R., Ocumpaugh, J., Hutt, S., & Paquette, L. (2023). Analysing adaptive scaffolds that help students develop self‐regulated learning behaviours. Journal of Computer Assisted Learning, 39(2), 351-368. https://doi.org/10.1111/jcal.12761
Pinto, J.D., Liu, Q., Paquette, L., Zhang, Y., & Fan, A. (2023). Investigating the Relationship Between Programming Experience and Debugging Behaviors in an Introductory Computer Science Course. ICQE 2023: Advances in Quantitative Ethnography, 125-139. https://doi.org/10.1007/978-3-031-47014-1_9
Zhang, Y. & Paquette, L. (2023). Sequential pattern mining in educational data: The application context, potential, strengths, and limitations. In Educational Data Science: Essentials, Approaches, and Tendencies: Proactive Education based on Empirical Big Data Evidence (pp. 219-254). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-0026-8_6
Zhang, Y., Paquette, L., Pinto, J. D., & Fan, A. X. (2023). Utilizing programming traces to explore and model the dimensions of novices' code‐writing skill. Computer Applications in Engineering Education. https://doi.org/10.1002/cae.22622
Zhang, Y., Paquette, L., Pinto, J. D., Liu, Q., & Fan, A. X. (2023). Combining latent profile analysis and programming traces to understand novices’ differences in debugging. Education and Information Technologies, 28(4), 4673-4701. https://doi.org/10.1007/s10639-022-11343-7
Zhang, Y., Pinto, J. D., Fan, A. X., & Paquette, L. (2023). Using problem similarity- and order- based weighting to model learner performance in introductory computer science problems. Journal of Educational Data Mining, 15(1), 63-99. https://doi.org/10.5281/zenodo.7646789
Zhang, Y. , Paquette, L., Baker, R.S., Bosch, N., Ocumpaugh, J., & Biswas, G. (2023). 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, 38(2), 777-800. https://doi.org/10.1007/s10212-022-00616-x

2022

Hutt, S., Baker, R. S., Ocumpaugh, J., Munshi, A., Andres, J. M. A. L., Karumbaiah, S., Slater, S., Biswas, G., Paquette, L., Bosch, N., & van Velsen, M. (2022). Quick red fox: an app supporting a new paradigm in qualitative research on AIED for STEM. Artificial Intelligence in STEM Education: The Paradigmatic Shifts in Research, Education, and Technology (pp. 319-332). CRC Press. https://doi.org/10.1201/9781003181187-26
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. https://doi.org/10.1016/j.cedpsych.2022.102064

2021

Baker, R. S., Nasiar, N., Ocumpaugh, J. L., Hutt, S., Andres, J. M., Slater, S., Schofield, M., Moore, A., Paquette, L., Munshi, A. & Biswas, G. (2021, June). Affect-targeted interviews for understanding student frustration. In International Conference on Artificial Intelligence in Education (pp. 52-63). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-78292-4_5
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. https://doi.org/10.5281/zenodo.5048423
Bosch, N., Zhang, Y., Paquette, L., Baker, R., Ocumpaugh, J., & Biswas, G. (2021, May). Students’ verbalized metacognition during computerized learning. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-12). https://doi.org/10.1145/3411764.3445809
Hutt, S., Ocumpaugh, J., Andres, J.M.A.L., Munshi, A., Bosch, N., Baker, R. S., Zhang, Y., Paquette, L., Slater, S. & Biswas, G. (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 (Vol. 43, No. 43). https://escholarship.org/uc/item/36s0d7bv
Hutt, S., Ocumpaugh, J., Andres, J.M.A.L., Bosch, N., Paquette, L., Biswas, G., & Baker, R. S. (2021). Investigating SMART Models of Self-Regulation and Their Impact on Learning. In Proceedings of the 14th Educational Data Mining Conference (pp. 580-587).
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 Proceedings of the 29th international conference on computers in education (pp. 69-74).
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 Advances in Quantitative Ethnography: Second International Conference, ICQE 2020 (pp. 185-201). Springer International Publishing. https://doi.org/10.1007/978-3-030-67788-6_13
Pinto, J. D., Zhang, Y., Paquette, L., & Fan, A. X. (2021). Investigating elements of student persistence in an introductory computer science course. In 5th Educational Data Mining in Computer Science Education (CSEDM) Workshop.
Zhang, Y., & Paquette, L. (2021). Mining sequential patterns with high usage variation. In Proceedings of the 14th Educational Data Mining Conference (pp. 704-707).
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. https://doi.org/10.18608/jla.2021.7161

2020

Bosch, N., Crues, R., Shaik, N., & Paquette, L. (2020). " Hello,[REDACTED]": Protecting Student Privacy in Analyses of Online Discussion Forums. Grantee Submission. In Proceedings of the 13th International Educational Data Mining Conference (pp. 39-49).
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
Henderson, N., Rowe, J., Paquette, L., Baker, R. S., & Lester, J. (2020). Improving affect detection in game-based learning with multimodal data fusion. In Artificial Intelligence in Education: 21st International Conference, AIED 2020, Ifrane, Morocco, July 6–10, 2020, Proceedings, Part I 21 (pp. 228-239). Springer International Publishing. https://doi.org/10.1007/978-3-030-52237-7_19
Hur, P., Bosch, N., Paquette, L., & Mercier, E. (2020). Harbingers of Collaboration? The Role of Early-Class Behaviors in Predicting Collaborative Problem Solving. In Proceedings of the 13th International Educational Data Mining Conference (pp. 104-114).
Li, T. W., & Paquette, L. (2020). Erroneous Answers Categorization for Sketching Questions in Spatial Visualization Training. In Proceedings of the 13th International Educational Data Mining Conference (pp. 148-158).
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 Artificial Intelligence in Education: 21st International Conference, AIED 2020, Ifrane, Morocco, July 6–10, 2020, Proceedings, Part I 21 (pp. 411-422). Springer International Publishing. https://doi.org/10.1007/978-3-030-52237-7_33
Paquette, L., & Bosch, N. (2020). The invisible breadcrumbs of digital learning: how learner actions inform us of their experience. In Handbook of Research on Digital Learning (pp. 302-316). IGI Global. https://doi.org/10.4018/978-1-5225-9304-1.ch019
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. https://doi.org/10.5281/zenodo.4143612
Sanyal, D., Bosch, N., & Paquette, L. (2020). Feature Selection Metrics: Similarities, Differences, and Characteristics of the Selected Models. In Proceedings of the 13th International Educational Data Mining Conference (pp. 212-223).
Zhang, Y., & Paquette, L. (2020). An effect-size-based temporal interestingness metric for sequential pattern mining. In Proceedings of the 13th International Educational Data Mining Conference (pp. 720-724).
Zhang, Y., Paquette, L., Baker, R. S., Ocumpaugh, J., Bosch, N., Munshi, A., & Biswas, G. (2020). The relationship between confusion and metacognitive strategies in Betty’s Brain. In Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (pp. 276-284). https://doi.org/10.1145/3375462.3375518

2019

Andres, J. M. A. L., Ocumpaugh, J., Baker, R. S., Slater, S., Paquette, L., Jiang, Y., … & Biswas, G. (2019, March). Affect sequences and learning in Betty’s Brain. In Proceedings of the 9th international conference on learning analytics & knowledge (pp. 383-390). https://doi.org/10.1145/3303772.3303807
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

2018

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
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, 152-193. https://doi.org/10.1007/s40593-017-0152-1
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 Artificial Intelligence in Education: 19th International Conference, AIED 2018, London, UK, June 27–30, 2018, Proceedings, Part I 19 (pp. 198-211). Springer International Publishing. https://doi.org/10.1007/978-3-319-93843-1_15
Jiang, Y., Clarke-Midura, J., Baker, R. S., Paquette, L., & Keller, B. (2018). How immersive virtual environments foster self-regulated learning. In Digital technologies and instructional design for personalized learning (pp. 28-54). IGI Global. https://doi.org/10.4018/978-1-5225-3940-7.ch002
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
Munshi, A., Rajendran, R., Ocumpaugh, J., Biswas, G., Baker, R. S., & Paquette, L. (2018, July). Modeling learners' cognitive and affective states to scaffold SRL in open-ended learning environments. In Proceedings of the 26th conference on user modeling, adaptation and personalization (pp. 131-138). https://doi.org/10.1145/3209219.3209241
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 Artificial Intelligence in Education: 19th International Conference, AIED 2018, London, UK, June 27–30, 2018, Proceedings, Part II 19 (pp. 257-260). Springer International Publishing. https://doi.org/10.1007/978-3-319-93846-2_47
Paquette, L., Bosch, N., Mercier, E., Jung, J., Shehab, S., & Tong, Y. (2018). Matching data-driven models of group interactions to video analysis of collaborative problem solving on tablet computers. International Society of the Learning Sciences, Inc.[ISLS].

2017

Biswas, G., Baker, R. S., & Paquette, L. (2017). Data mining methods for assessing self-regulated learning. In Handbook of self-regulation of learning and performance (pp. 388-403). Routledge. https://psycnet.apa.org/doi/10.4324/9781315697048-25
Bosch, N., & Paquette, L. (2017, June). Unsupervised deep autoencoders for feature extraction with educational data. In Deep learning with educational data workshop at the 10th international conference on educational data mining.
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. Proceedings of the 10th International Conference on Educational Data Mining (pp. 250-255).
Ocumpaugh, J., Andres, J. M., Baker, R., DeFalco, J., Paquette, L., Rowe, J., … & Sottilare, R. (2017). Affect dynamics in military trainees using vMedic: From engaged concentration to boredom to confusion. In Artificial Intelligence in Education: 18th International Conference, AIED 2017, Wuhan, China, June 28–July 1, 2017, Proceedings 18 (pp. 238-249). Springer International Publishing. https://doi.org/10.1007/978-3-319-61425-0_20
Paquette, L., & Baker, R. S. (2017). Variations of gaming behaviors across populations of students and across learning environments. In Artificial Intelligence in Education: 18th International Conference, AIED 2017, Wuhan, China, June 28–July 1, 2017, Proceedings 18 (pp. 274-286). Springer International Publishing. https://doi.org/10.1007/978-3-319-61425-0_23
Wang, Y., Baker, R., & Paquette, L. (2017, March). Behavioral predictors of MOOC post-course development. In Proceedings of the Workshop on Integrated Learning Analytics of MOOC Post-Course Development.