Dynamic Item Response Models extend the standard Item Response Theory (IRT) to capture temporal dynamics in learner ability. While these models have the potential to allow instructional systems to actively monitor the evolution of learner proficiency in real time, existing dynamic item response models rely on expensive inference algorithms that…

Massive Open Online Courses (MOOCs) which enable large-scale open online learning for massive users have been playing an important role in modern education for both students as well as professionals. To keep users' interest in MOOCs, recommender systems have been studied and deployed to recommend courses or videos that a user might be interested…

The use of Bayesian Knowledge Tracing (BKT) models in predicting student learning and mastery, especially in mathematics, is a well-established and proven approach in learning analytics. In this work, we report on our analysis examining the generalizability of BKT models across academic years attributed to "detector rot." We compare the…

The recent trend of embedding source code for machine learning applications also enables new opportunities in learning analytics in programming education, but which code embedding approach is most suitable for learning analytics remains an open question. A common approach to embedding source code lies in extracting syntactic information from a…

Students using self-directed learning platforms, such as Duolingo, cannot be adequately assessed relying solely on responses to standard learning exercises due to a lack of control over learners' choices in how to utilize the platform: for example, how learners choose to sequence their studying and how much they choose to revisit old material. To…

The General Data Protection Regulation (GDPR) in the European Union contains directions on how user data may be collected, stored, and when it must be deleted. As similar legislation is developed around the globe, there is the potential for repercussions across multiple fields of research, including educational data mining (EDM). Over the past two…

Knowledge tracing (KT), the task of tracking the knowledge state of each student over time, has been assessed actively by artificial intelligence researchers. Recent reports have described that Deep-IRT, which combines Item Response Theory (IRT) with a deep learning model, provides superior performance. It can express the abilities of each student…

Prompting students to generate constructed responses as they read provides a window into the processes and strategies that they use to make sense of complex text. In this study, Markov models examined the extent to which: (1) patterns of strategies; and (2) strategy combinations could be used to inform computational models of students' text…

Interactive simulations allow students to independently explore scientific phenomena and ideally infer the underlying principles through their exploration. Effectively using such environments is challenging for many students and therefore, adaptive guidance has the potential to improve student learning. Providing effective support is, however,…

Spatial analytics receive increased attention in educational data mining. A critical issue in stop detection (i.e., the automatic extraction of timestamped and located stops in the movement of individuals) is a lack of validation of stop accuracy to represent phenomena of interest. Next to a radius that an actor does not exceed for a certain…

In studies that generate course recommendations based on similarity, the typical enrollment data used for model training consists only of one record per student-course pair. In this study, we explore and quantify the additional signal present in course transaction data, which includes a more granular account of student administrative interactions…

In MOOCs for programming, Automated Testing and Feedback (ATF) systems are frequently integrated, providing learners with immediate feedback on code assignments. The analysis of the large amounts of trace data collected by these systems may provide insights into learners' patterns of utilizing the automated feedback, which is crucial for the…

Ten years after the announcement of the "rise of the super experiment" at Educational Data Mining 2012, challenges to implementing "internet scale" educational experiments often persist for educational technology providers, especially when they seek to test substantive instructional interventions. Studies that deploy and test…

In an Intelligent Tutoring System (ITS), problem (or question) difficulty is one of the most critical parameters, directly impacting problem design, test paper organization, result analysis, and even the fairness guarantee. However, it is very difficult to evaluate the problem difficulty by organized pre-tests or by expertise, because these…

Knowledge tracing algorithms such as Bayesian Knowledge Tracing (BKT) can provide students and teachers with helpful information about their progress towards learning objectives. Despite the popularity of BKT in the research community, the algorithm is not widely adopted in educational practice. This may be due to skepticism from users and…