The increasing use of complex machine learning models in education has led to concerns about their interpretability, which in turn has spurred interest in developing explainability techniques that are both faithful to the model's inner workings and intelligible to human end-users. In this paper, we describe a novel approach to creating a…

Feature engineering plays a critical role in the development of machine learning systems for educational contexts, yet its impact on student trust remains understudied. Traditional approaches have focused primarily on optimizing model performance through expert-crafted features, while the emergence of AutoML offers automated alternatives for…

Understanding the relationship between student behaviors and learning outcomes is crucial for designing effective collaborative learning environments. However, collaborative learning analytics poses significant challenges, not only due to the complex interplay between collaborative problem-solving and collaborative dialogue but also due to the…

Digital textbooks are widely used in various educational contexts, such as university courses and online lectures. Such textbooks yield learning log data that have been used in numerous educational data mining (EDM) studies for student behavior analysis and performance prediction. However, these studies have faced challenges in integrating…

Student modeling is central to many educational technologies as it enables predicting future learning outcomes and designing targeted instructional strategies. However, open-ended learning domains pose challenges for accurately modeling students due to the diverse behaviors and a large space of possible misconceptions. To approach these…

In computer science education timely help seeking during large programming projects is essential for student success. Help-seeking in typical courses happens in office hours and through online forums. In this research, we analyze students coding activities and help requests to understand the interaction between these activities. We collected…

Engaged and disengaged behaviors have been studied across a variety of educational contexts. However, tools to analyze engagement typically require custom-coding and calibration for a system. This limits engagement detection to systems where experts are available to study patterns and build detectors. This work studies a new approach to classify…

Ensuring the integrity of results in online learning and assessment tools is a challenge, due to the lack of direct supervision increasing the risk of fraud. We propose and evaluate a machine learning-based method for detecting anomalous behaviour in an online retrieval practice task, using an XGBoost classifier trained on keystroke dynamics and…

We report on a study in which we examined the work habits of six students who agreed to use do not disturb on their phone while working on programming assignments. Two students tried do not disturb, and quickly quit using it. Three out of four remaining student participants were more productive while using do not disturb when working on their…

Research into "gaming the system" behavior in intelligent tutoring systems (ITS) has been around for almost two decades, and detection has been developed for many ITSs. Machine learning models can detect this behavior in both real-time and in historical data. However, intelligent tutoring system designs often change over time, in terms…

Online learning has enhanced accessibility in education, but also poses significant challenges in maintaining academic integrity during online exams, particularly when students are prohibited from accessing unauthorized resources through the Internet. Nonetheless, students must remain connected to the Internet in order to take the online exam.…

This paper presents an in-depth analysis of student behavior and score prediction in the ASSISTments online learning platform. We address four research questions related to the impact of tutoring materials, skill mastery, feature extraction, and graph representation learning. To investigate the impact of tutoring materials, we analyze the…

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…

Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against some students and possible harmful long-term…

Educational data mining increasingly leverages enrollment data for higher education applications. However, these data describe final end-of-semester course selections, not the often complex enrollment activities leading up to a finalized schedule. Fine-grain transaction data of student waitlist, add, and drop actions during academic semester…