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Ean Teng Khor; Dave Darshan – International Journal of Information and Learning Technology, 2024
Purpose: This study leverages social network analysis (SNA) to visualise the way students interacted with online resources and uses the data obtained from SNA as features for supervised machine learning algorithms to predict whether a student will successfully complete a course. Design/methodology/approach: The exploration and visualisation of the…
Descriptors: Prediction, Academic Achievement, Electronic Learning, Artificial Intelligence
Kenneth K. Wong; Spencer Davis – Annenberg Institute for School Reform at Brown University, 2023
The Cobb Teaching & Learning System (CTLS) is a digital learning initiative developed for and by the Cobb County School District (CCSD) in Georgia. CTLS became a crucial initiative used by the district to maintain student academic progress during the COVID-19 pandemic. Adopting a mixed-methods approach, this case study seeks to analyze CTLS's…
Descriptors: Electronic Learning, Teacher Collaboration, Educational Technology, Data Use
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Yury Rishko; Diana Boboshko; Evgeniya Eliseeva; Aleksandr Malkin; Dmitrii Treistar – SAGE Open, 2025
Discussion of the effectiveness of distance learning as a means of delivering higher education programs at classical universities has been ongoing for the past decade. The article presents the findings of a study of changes in academic performance of university students, covering the period from fall 2018 to fall 2023. This period included a rapid…
Descriptors: Outcomes of Education, Educational Change, Electronic Learning, Online Courses
Varun Mandalapu – ProQuest LLC, 2021
Educational data mining focuses on exploring increasingly large-scale data from educational settings, such as Learning Management Systems (LMS), and developing computational methods to understand students' behaviors and learning settings better. There has been a multitude of research dedicated to studying the student learning process, leading to…
Descriptors: Models, Student Behavior, Learning Management Systems, Data Use
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Huang, Anna Y. Q.; Lu, Owen H. T.; Huang, Jeff C. H.; Yin, C. J.; Yang, Stephen J. H. – Interactive Learning Environments, 2020
In order to enhance the experience of learning, many educators applied learning analytics in a classroom, the major principle of learning analytics is targeting at-risk student and given timely intervention according to the results of student behavior analysis. However, when researchers applied machine learning to train a risk identifying model,…
Descriptors: Academic Achievement, Data Use, Learning Analytics, Classification
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Devlin, Maura; Bushey, Heather – Change: The Magazine of Higher Learning, 2019
With national graduation rates that range from 32% among open enrollment institutions to 66% among private not-for-profits, higher education institutions have a moral imperative to improve the success of their students. Interventions to support today's students must consider the student holistically, be just-in-time, and come from a…
Descriptors: Data Use, Academic Achievement, Outcomes of Education, Holistic Approach
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Rao, A. Ravishshankar – Advances in Engineering Education, 2020
Studies show that a significant fraction of students graduating from high schools in the U.S. is ill prepared for college and careers. Some problems include weak grounding in math and writing, lack of motivation, and insufficient conscientiousness. Academic institutions are under pressure to improve student retention and graduate rates, whereas…
Descriptors: Learner Engagement, Student Motivation, Prediction, Academic Achievement