The traditional Elo rating system (ERS), widely used as a student model in adaptive learning systems, assumes unidimensionality (i.e., all items measure a single ability or skill), limiting its ability to handle multidimensional data common in educational contexts. In response, several multidimensional extensions of the Elo rating system have been…

Algorithmic bias is a pressing concern in educational data mining (EDM), as it risks amplifying inequities in learning outcomes. The Area Between ROC Curves (ABROCA) metric is frequently used to measure discrepancies in model performance across demographic groups to quantify overall model fairness. However, its skewed distribution--especially when…

Educators evaluate student knowledge using knowledge component (KC) models that map assessment questions to KCs. Still, designing KC models for large question banks remains an insurmountable challenge for instructors who need to analyze each question by hand. The growing use of Generative AI in education is expected only to aggravate this chronic…

Knowledge tracing (KT) models predict how students will perform on future interactions, given a sequence of prior responses. Modern approaches to KT leverage "deep learning" techniques to produce more accurate predictions, potentially making personalized learning paths more efficacious for learners. Many papers on the topic of KT focus…

The growing demand for microcredentials in education and workforce development necessitates scalable, accurate, and fair assessment systems for both soft and hard skills based on students' lived experience narratives. Existing approaches struggle with the complexities of hierarchical credentialing and the mitigation of algorithmic bias related to…

The aim of this paper is to provide tools to teachers for monitoring student work and understanding practices in order to help student and possibly adapt exercises in the future. In the context of an online programming learning platform, we propose to study the attempts (i.e., submitted programs) of the students for each exercise by using…

Curricular design in higher education significantly impacts student success and institutional performance. However, academic programs' complexity--shaped by pass rates, prerequisite dependencies, and course repeat policies--creates challenges for administrators. This paper presents a method for modeling curricular pathways including development of…

Knowledge Tracing (KT) models predicting student performance in intelligent tutoring systems have been successfully deployed in several educational domains. However, their usage in open-ended programming problems poses multiple challenges due to the complexity of the programming code and a complex interplay between syntax and logic requirements…

Knowledge tracing models have enabled a range of intelligent tutoring systems to provide feedback to students. However, existing methods for knowledge tracing in learning sciences are predominantly reliant on statistical data and instructor-defined knowledge components, making it challenging to integrate AI-generated educational content with…