Learning logs collected by digital educational systems, increasingly deployed in educational settings, include clickstream logs recorded through page transitions in teaching materials and digital marker logs recorded by drawing a marker. A challenge with these learning logs is their low temporal and spatial resolutions. This paper proposes a…

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…

Predictive analytics is widely used in various domains, including education, to inform decision-making and improve outcomes. However, many predictive models are proprietary and inaccessible for evaluation or modification by researchers and practitioners, limiting their accountability and ethical design. Moreover, predictive models are often opaque…

Quality Assurance (QA) aims to ensure and enhance educational quality, promote accountability, and foster sustainable improvement and is considered a crucial element for higher education systems in a world of constant change, increased competitiveness, technological innovation, and rising costs. In the last several years, quality assurance in…

To support online learners at a large scale, extensive studies have adopted machine learning (ML) techniques to analyze students' artifacts and predict their learning outcomes automatically. However, limited attention has been paid to the fairness of prediction with ML in educational settings. This study intends to fill the gap by introducing a…

This paper introduces CoCo, a system designed to support and encourage collaborative learning among colocated students sharing a single mobile device. It provides teachers with the possibility to create digital lessons, configure parameters for collaborative activities such as the number of students and tablets, and monitor students' progress. On…

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…

Personalized problem selection enhances student practice in tutoring systems. Prior research has focused on transparent problem selection that supports learner control but rarely engages learners in selecting practice materials. We explored how different levels of control (i.e., full AI control, shared control, and full learner control), combined…

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…

Exploring new or emerging research domains or subdomains can become overwhelming due to the magnitude of available resources and the high speed at which articles are published. As such, a tool that curates the information and underlines central entities, both authors and articles from a given research context, is highly desirable. Starting from…

The Jeong (2020) study found that greater use of backward and depth-first processing was associated with higher scores on students' argument maps and that analysis of only the first five nodes students placed in their maps predicted map scores. This study utilized the jMAP tool and algorithms developed in the Jeong (2020) study to determine if the…

The goal of knowledge tracing is to track the state of a student's knowledge as it evolves over time. This plays a fundamental role in understanding the learning process and is a key task in the development of an intelligent tutoring system. In this paper we propose a novel approach to knowledge tracing that combines techniques from matrix…

Supervised machine learning has become one of the most important methods for developing educational and intelligent tutoring software; it is the backbone of many educational data mining methods for estimating knowledge, emotion, and other aspects of learning. Hence, in order to ensure optimal utilization of computing resources and effective…

Emotions are vital to learning success, especially in online learning environments. They make the difference between learning success and failure. Unfortunately, learners' emotional state is still rarely considered in online learning and teaching, although it is an important driver of learning success. This paper reports a work-in-progress…