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This study systematically reviews the transformative role of Tutoring Systems, encompassing Intelligent Tutoring Systems (ITS) and Robot Tutoring Systems (RTS), in addressing global educational challenges through advanced technologies. As…
Due to the rapidly rising popularity of Massive Open Online Courses (MOOCs), there is a growing demand for scalable automated support technologies for student learning. Transferring traditional educational resources to online contexts has…
Graph machine learning has been extensively studied in both academia and industry. However, in the literature, most existing graph machine learning models are designed to conduct training with data samples in a random order, which may…
University curriculum, both on a campus level and on a per-major level, are affected in a complex way by many decisions of many administrators and faculty over time. As universities across the United States share an urgency to significantly…
Recently, continual graph learning has been increasingly adopted for diverse graph-structured data processing tasks in non-stationary environments. Despite its promising learning capability, current studies on continual graph learning…
Hypergraph can capture complex and higher-order dependencies among learners and learning resources in personalized educational recommender systems. Many existing hypergraph-based recommendation approaches underexplored the dynamic…
The purpose of this study is to explore students' backtracking patterns in using a digital textbook and reveal the relationship between backtracking behaviors and academic performance as well as learning styles. The study was carried out…
In this paper, we study the problem of MOOC quality evaluation which is essential for improving the course materials, promoting students' learning efficiency, and benefiting user services. While achieving promising performances, current…
This study addresses the challenges of tracking and analyzing students' learning trajectories, particularly the issue of inadequate knowledge coverage in course assessments. Traditional assessment tools often fail to fully cover course…
Graphs are essential representations in the professions and education concerning the science, technology, engineering, and mathematics (STEM) disciplines. Beyond their academic relevance, graphs find extensive utility in everyday scenarios,…
Graphs are growing rapidly, along with the number of distinct label categories associated with them. Applications like e-commerce, healthcare, recommendation systems, and various social media platforms are rapidly moving towards graph…
The prediction of student performance and the analysis of students' learning behavior play an important role in enhancing online courses. By analysing a massive amount of clickstream data that captures student behavior, educators can gain…
This paper introduces novel methods for preparing and analyzing student interaction data extracted from course management systems like Moodle to facilitate process mining, like the creation of graphs that show the process flow. Such graphs…
In humans and animals, curriculum learning -- presenting data in a curated order - is critical to rapid learning and effective pedagogy. Yet in machine learning, curricula are not widely used and empirically often yield only moderate…
The rapid advancement in data-driven research has increased the demand for effective graph data analysis. However, real-world data often exhibits class imbalance, leading to poor performance of machine learning models. To overcome this…
Academic performance prediction aims to leverage student-related information to predict their future academic outcomes, which is beneficial to numerous educational applications, such as personalized teaching and academic early warning. In…
Analyzing student behavior in educational scenarios is crucial for enhancing teaching quality and student engagement. Existing AI-based models often rely on classroom video footage to identify and analyze student behavior. While these…
Flipped Classrooms (FC) are a promising teaching strategy, where students engage with the learning material before attending face-to-face sessions. While pre-class activities are critical for course success, many students struggle to engage…
This paper addresses a key challenge in Educational Data Mining, namely to model student behavioral trajectories in order to provide a means for identifying students most at-risk, with the goal of providing supportive interventions. While…
Learning analytics research increasingly studies classroom learning with AI-based systems through rich contextual data from outside these systems, especially student-teacher interactions. One key challenge in leveraging such data is…