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Download full textAshish Gurung; Jionghao Lin; Zhongtian Huang; Conrad Borchers; Ryan S. Baker; Vincent Aleven; Kenneth R. Koedinger – International Educational Data Mining Society, 2025
Prior work has developed a range of automated measures ("detectors") of student self-regulation and engagement from student log data. These measures have been successfully used to make discoveries about student learning. Here, we extend this line of research to an underexplored aspect of self-regulation: students' decisions about when to…
Descriptors: Decision Making, Computer Software, Tutoring, Electronic Learning
Andreea Dutulescu; Stefan Ruseti; Mihai Dascalu; Danielle McNamara – International Educational Data Mining Society, 2025
The assessment of student responses to learning-strategy prompts, such as self-explanation, summarization, and paraphrasing, is essential for evaluating cognitive engagement and comprehension. However, manual scoring is resource-intensive, limiting its scalability in educational settings. This study investigates the use of Large Language Models…
Descriptors: Scoring, Computational Linguistics, Computer Software, Artificial Intelligence
Pranjli Khanna; Kaleb Mathieu; Kole Norberg; Husni Almoubayyed; Stephen E. Fancsali – International Educational Data Mining Society, 2025
Recent research on more comprehensive models of student learning in adaptive math learning software used an indicator of student reading ability to predict students' tendencies to engage in behaviors associated with so-called "gaming the system." Using data from Carnegie Learning's MATHia adaptive learning software, we replicate the…
Descriptors: Computer Software, Computer Uses in Education, Reading Difficulties, Reading Skills
