Juan D. Pinto

16 papers B 4Journal 7Unranked 4
YearRankTypeTitle / Venue / Authors
2025 B conf
A Constraints-Based Approach to Fully Interpretable Neural Networks for Detecting Learner Behaviors.
EDM
Juan D. Pinto, Luc Paquette
2025 J jnl
A constraints-based approach to fully interpretable neural networks for detecting learner behaviors.
CoRR
Juan D. Pinto, Luc Paquette
2025 B conf
The 2nd Human-Centric eXplainable AI in Education (HEXED) Workshop.
EDM
Vinitra Swamy, Jakub Kuzilek, Juan D. Pinto, Luc Paquette, Tanja Käser, Qianhui Liu, Lea Cohausz
2024 J jnl
Deep Learning for Educational Data Science.
CoRR
Juan D. Pinto, Luc Paquette
2024 B conf
Human-Centric eXplainable AI in Education (HEXED) Workshop.
EDM
Juan D. Pinto, Luc Paquette, Vinitra Swamy, Tanja Käser, Qianhui Liu, Lea Cohausz
2024 B conf
Intrinsically Interpretable Artificial Neural Networks for Learner Modeling.
EDM
Juan D. Pinto, Luc Paquette, Nigel Bosch
2024 — ed.
Joint Proceedings of the Human-Centric eXplainable AI in Education and the Leveraging Large Language Models for Next Generation Educational Technologies Workshops (HEXED-L3MNGET 2024) co-located with 17th International Conference on Educational Data Mining (EDM 2024), Atlanta, Georgia, USA, July 14, 2024.
HEXED/L3MNGET@EDM
Juan D. Pinto, Eamon Worden, Anthony Botelho, Lea Cohausz, Clayton Cohn, Mingyu Feng, Neil T. Heffernan, Arto Hellas, Lan Jiang, David Joyner, Tanja Käser, Juho Kim, Andrew S. Lan, Chenglu Li, Joshua Littenberg-Tobias, Qianhui Liu, Christopher MacLellan, Steven Moore, Maciej Pankiewicz, Luc Paquette, Zach A. Pardos, Anna N. Rafferty, Adish Singla, Shashank Sonkar, Vinitra Swamy, Rose E. Wang, Candace A. Walkington
2024 J jnl
Large language models for whole-learner support: opportunities and challenges.
Frontiers Artif. Intell.
Amogh Mannekote, Adam Davies, Juan D. Pinto, Shan Zhang, Daniel Olds, Noah L. Schroeder, Blair Lehman, Diego Zapata-Rivera, ChengXiang Zhai
2024 — conf
Preface.
HEXED/L3MNGET@EDM
Juan D. Pinto, Luc Paquette, Vinitra Swamy, Tanja Käser, Qianhui Liu, Lea Cohausz
2024 — conf
Towards a Unified Framework for Evaluating Explanations.
HEXED/L3MNGET@EDM
Juan D. Pinto, Luc Paquette
2024 J jnl
Towards a Unified Framework for Evaluating Explanations.
CoRR
Juan D. Pinto, Luc Paquette
2023 J jnl
Combining latent profile analysis and programming traces to understand novices' differences in debugging.
Educ. Inf. Technol.
Yingbin Zhang, Luc Paquette, Juan D. Pinto, Qianhui Liu, Aysa Xuemo Fan
2023 — conf
Investigating the Relationship Between Programming Experience and Debugging Behaviors in an Introductory Computer Science Course.
ICQE
Juan D. Pinto, Qianhui Liu, Luc Paquette, Yingbin Zhang, Aysa Xuemo Fan
2023 J jnl
Utilizing programming traces to explore and model the dimensions of novices' code-writing skill.
Comput. Appl. Eng. Educ.
Yingbin Zhang, Luc Paquette, Juan D. Pinto, Aysa Xuemo Fan
2021 — conf
Investigating Elements of Student Persistence in an Introductory Computer Science Course (Full Paper).
EDM (Workshops)
Juan D. Pinto, Yingbin Zhang, Luc Paquette, Aysa Xuemo Fan
2020 J jnl
Exemplifying Computational Thinking Scenarios in the Age of COVID-19: Examining the Pandemic's Effects in a Project-Based MOOC.
Comput. Sci. Eng.
Juan D. Pinto, Chris Quintana, Rebecca M. Quintana, Sharon Broude Geva, Dirk Colbry