20 citations · 22 across the 3 of their papers we have counts for
3 papers
cs.CY2024
Deep Learning for Educational Data Science
Juan D. Pinto, Luc Paquette
With the ever-growing presence of deep artificial neural networks in every facet of modern life, a growing body of researchers in educational data science -- a field consisting of…
cs.CL2023★ 2 cited
Exploring the Potential of Large Language Models in Generating Code-Tracing Questions for Introductory Programming Courses
Aysa Xuemo Fan, Ranran Haoran Zhang, Luc Paquette +1
In this paper, we explore the application of large language models (LLMs) for generating code-tracing questions in introductory programming courses. We designed targeted prompts fo…
cs.LG2023★ 20 cited
Sequential pattern mining in educational data: The application context, potential, strengths, and limitations
Yingbin Zhang, Luc Paquette
Increasingly, researchers have suggested the benefits of temporal analysis to improve our understanding of the learning process. Sequential pattern mining (SPM), as a pattern recog…