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Wen Chiang Lim; Neil T. Heffernan; Adam Sales – Grantee Submission, 2025
As online learning platforms become more popular and deeply integrated into education, understanding their effectiveness and what drives that effectiveness becomes increasingly important. While there is extensive prior research illustrating the benefits of intelligent tutoring systems (ITS) for student learning, there is comparatively less focus…
Descriptors: Intelligent Tutoring Systems, Computer Uses in Education, Prompting, Reports
Sonsoles Lopez-Pernas; Kamila Misiejuk; Rogers Kaliisa; Mohammed Saqr – IEEE Transactions on Learning Technologies, 2025
Despite the growing use of large language models (LLMs) in educational contexts, there is no evidence on how these can be operationalized by students to generate custom datasets suitable for teaching and learning. Moreover, in the context of network science, little is known about whether LLMs can replicate real-life network properties. This study…
Descriptors: Students, Artificial Intelligence, Man Machine Systems, Interaction
Lili Aunimo; Janne Kauttonen; Marko Vahtola; Salla Huttunen – Journal of Computing in Higher Education, 2025
Institutions of higher education possess large amounts of learning-related data in their student registers and learning management systems (LMS). This data can be mined to gain insights into study paths, study styles and possible bottlenecks on the study paths. In this study, we focused on creating a predictive model for study completion time…
Descriptors: Data Collection, Learning Management Systems, Study Habits, Time on Task
Anuradha Peramunugamage; Uditha W. Ratnayake; Shironica P. Karunanayaka; Ellen Francine Barbosa; William Simão de Deus; Chulantha L. Jayawardena; R. K. J. de Silva – Journal of Learning for Development, 2025
Interactions among students in online learning environments are difficult to monitor but can be crucial for their academic performance. Moodle is one of the best and most popular online learning platforms, where its log records can reveal important information on students' engagement and the respective performance. This study examines the degree…
Descriptors: Cooperative Learning, Interaction, Electronic Learning, Learning Management Systems
Imhof, Christof; Comsa, Ioan-Sorin; Hlosta, Martin; Parsaeifard, Behnam; Moser, Ivan; Bergamin, Per – IEEE Transactions on Learning Technologies, 2023
Procrastination, the irrational delay of tasks, is a common occurrence in online learning. Potential negative consequences include a higher risk of drop-outs, increased stress, and reduced mood. Due to the rise of learning management systems (LMS) and learning analytics (LA), indicators of such behavior can be detected, enabling predictions of…
Descriptors: Prediction, Time Management, Electronic Learning, Artificial Intelligence
Bruce Parsons; John H. Curry – TechTrends: Linking Research and Practice to Improve Learning, 2024
This article investigates an artificial intelligence language model, ChatGPT, and its ability to complete graduate-level instructional design assignments. The approach subjected ChatGPT to a needs, task, and learner analysis for a 12th-grade media literacy module and benchmarked its performance by expert evaluation and measurements via grading…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Technology, Instructional Design
Sonja Kleter; Uwe Matzat; Rianne Conijn – IEEE Transactions on Learning Technologies, 2024
Much of learning analytics research has focused on factors influencing model generalizability of predictive models for academic performance. The degree of model generalizability across courses may depend on aspects, such as the similarity of the course setup, course material, the student cohort, or the teacher. Which of these contextual factors…
Descriptors: Prediction, Models, Academic Achievement, Learning Analytics
Gabbay, Hagit; Cohen, Anat – International Educational Data Mining Society, 2023
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…
Descriptors: MOOCs, Feedback (Response), Teaching Methods, Learning Strategies
Shi, Yang; Schmucker, Robin; Chi, Min; Barnes, Tiffany; Price, Thomas – International Educational Data Mining Society, 2023
Knowledge components (KCs) have many applications. In computing education, knowing the demonstration of specific KCs has been challenging. This paper introduces an entirely data-driven approach for: (1) discovering KCs; and (2) demonstrating KCs, using students' actual code submissions. Our system is based on two expected properties of KCs: (1)…
Descriptors: Computer Science Education, Data Analysis, Programming, Coding
Alam, Md. I.; Malone, Lauren; Nadolny, Larysa; Brown, Michael; Cervato, Cinzia – Journal of Computer Assisted Learning, 2023
Background: The substantial growth in gamification research has connected gamified learning to enhanced engagement, improved performance, and greater motivation. Similar to gamification, personalized learning analytics dashboards can enhance student engagement. Objectives: This study explores the student experiences and academic achievements using…
Descriptors: Academic Achievement, Game Based Learning, Introductory Courses, STEM Education
Er, Erkan – Online Submission, 2022
Time management is an important self-regulation strategy that can improve student learning and lead to higher performance. Students who can manage their time effectively are more likely to exhibit consistent engagement in learning activities and to complete course assignments in a timely manner. Well planning of the study time is an essential part…
Descriptors: Programming, Time Management, Computer Science Education, Integrated Learning Systems
Edwards, John; Hart, Kaden; Shrestha, Raj – Journal of Educational Data Mining, 2023
Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as…
Descriptors: Data Analysis, Computer Science Education, Learning Analytics, Research Methodology

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