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Anna G. Brady – Research in Science Education, 2024
Computer-based learning environments (CBLEs) are powerful tools to support student learning. Increasingly of interest is the data that is recorded as learners interact with a CBLE. This "process data" yields opportunities for researchers to examine learners' engagement with a CBLE and explore whether specific interactions are associated…
Descriptors: Electronic Learning, Educational Environment, Data Use, Learner Engagement
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Kai Li – International Association for Development of the Information Society, 2023
Assessing students' performance in online learning could be executed not only by the traditional forms of summative assessments such as using essays, assignments, and a final exam, etc. but also by more formative assessment approaches such as interaction activities, forum posts, etc. However, it is difficult for teachers to monitor and assess…
Descriptors: Student Evaluation, Online Courses, Electronic Learning, Computer Literacy
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David P. Reid; Timothy D. Drysdale – IEEE Transactions on Learning Technologies, 2024
The designs of many student-facing learning analytics (SFLA) dashboards are insufficiently informed by educational research and lack rigorous evaluation in authentic learning contexts, including during remote laboratory practical work. In this article, we present and evaluate an SFLA dashboard designed using the principles of formative assessment…
Descriptors: Learning Analytics, Laboratory Experiments, Electronic Learning, Feedback (Response)
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Sultan A. Almelhes – SAGE Open, 2025
Many people around the globe are aiming to learn the Arabic language, even though it is difficult to learn. Therefore, many online institutions are offering the service of teaching Arabic as a second language to non-native speakers, but these institutions always encounter different issues in enhancing their performance. Thus, this study aimed to…
Descriptors: Arabic, Second Language Instruction, Electronic Learning, Data Analysis
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Safa Ridha Albo Abdullah; Ahmed Al-Azawei – International Review of Research in Open and Distributed Learning, 2025
This systematic review sheds light on the role of ontologies in predicting achievement among online learners, in order to promote their academic success. In particular, it looks at the available literature on predicting online learners' performance through ontological machine-learning techniques and, using a systematic approach, identifies the…
Descriptors: Electronic Learning, Academic Achievement, Grade Prediction, Data Analysis
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Bulathwela, Sahan; Verma, Meghana; Pérez-Ortiz, María; Yilmaz, Emine; Shawe-Taylor, John – International Educational Data Mining Society, 2022
This work explores how population-based engagement prediction can address cold-start at scale in large learning resource collections. The paper introduces: (1) VLE, a novel dataset that consists of content and video based features extracted from publicly available scientific video lectures coupled with implicit and explicit signals related to…
Descriptors: Video Technology, Lecture Method, Data Analysis, Prediction
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Korkmaz, Elif; Morali, Hasibe Sevgi – International Electronic Journal of Mathematics Education, 2022
Augmented reality (AR) helps three dimensional, virtual objects to be viewed, interactively, in a real-world setting. AR technology is used in many fields such as medicine, advertisement, military, industry, and increasingly in education. AR has an importantrole in concretizing educational platforms and achieving permanentlearning. This study aims…
Descriptors: Meta Analysis, Computer Simulation, Electronic Learning, Mathematics Education
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Johnson, Jillian C.; Olney, Andrew M. – International Educational Data Mining Society, 2022
Typical data science instruction uses generic datasets like survival rates on the Titanic, which may not be motivating for students. Will introducing real-life data science problems fill this motivational deficit? To analyze this question, we contrasted learning with generic datasets and artificial problems (Phase 1) with a community-sourced…
Descriptors: Data, Data Analysis, Interdisciplinary Approach, Student Motivation
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Yaqian Zheng; Deliang Wang; Junjie Zhang; Yanyan Li; Yaping Xu; Yaqi Zhao; Yafeng Zheng – Education and Information Technologies, 2025
Generating personalized learning pathways for e-learners is a critical issue in the field of e-learning as it plays a pivotal role in guiding learners towards the successful achievement of their learning objectives. The existing literature has proposed various methods from different perspectives to address this issue, including learner-based,…
Descriptors: Individualized Instruction, Electronic Learning, Academic Achievement, Student Educational Objectives
Michael L. Chrzan; Francis A. Pearman; Benjamin W. Domingue – Annenberg Institute for School Reform at Brown University, 2025
The increasing rate of permanent school closures in U.S. public school districts presents unprecedented challenges for administrators and communities alike. This study develops an early-warning indicator model to predict mass closure events -- defined as a district closing at least 10% of its schools -- five years in advance. Leveraging…
Descriptors: Artificial Intelligence, Electronic Learning, School Districts, School Closing
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Chaewon Lee; Lan Luo; Shelbi L. Kuhlmann; Robert D. Plumley; Abigail T. Panter; Matthew L. Bernacki; Jeffrey A. Greene; Kathleen M. Gates – Journal of Learning Analytics, 2025
The increasing use of learning management systems (LMSs) generates vast amounts of clickstream data, opening new avenues for predicting learner performance. Traditionally, LMS predictive analytics have relied on either supervised machine learning or Markov models to classify learners based on predicted learning outcomes. Machine learning excels at…
Descriptors: Electronic Learning, Prediction, Data Analysis, Artificial Intelligence
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Hwang, Gwo-Jen; Tu, Yun-Fang; Tang, Kai-Yu – International Review of Research in Open and Distributed Learning, 2022
This study reviews the journal publications of artificial intelligence-supported online learning (AIoL) in the Web of Science (WOS) database from 1997 to 2019 taking into account the contributing countries/areas, leading journals, highly cited papers, authors, research areas, research topics, roles of AIoL, and adopted artificial intelligence (AI)…
Descriptors: Artificial Intelligence, Electronic Learning, Educational Research, Data Analysis
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Jamal Kay B. Rogers; Tamara Cher R. Mercado; Ronald S. Decano – Journal of Education and Learning (EduLearn), 2025
Poor academic performance remains among the most concerning educational issues, especially in higher education and online learning. To address the concern, institutions like the University of Southeastern Philippines (USeP) leverage educational data mining (EDM) techniques to generate relevant information from learning management systems (LMS)…
Descriptors: Foreign Countries, Learning Management Systems, Academic Achievement, Data Analysis
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Kerstin Huber; Maria Bannert – Journal of Computing in Higher Education, 2024
The empirical study investigates what log files and process mining can contribute to promoting successful learning. We want to show how monitoring and evaluation of learning processes can be implemented in the educational life by analyzing log files and navigation behavior. Thus, we questioned to what extent log file analyses and process mining…
Descriptors: Learning Processes, Data Analysis, Navigation (Information Systems), Student Behavior
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Shuanghong Shen; Qi Liu; Zhenya Huang; Yonghe Zheng; Minghao Yin; Minjuan Wang; Enhong Chen – IEEE Transactions on Learning Technologies, 2024
Modern online education has the capacity to provide intelligent educational services by automatically analyzing substantial amounts of student behavioral data. Knowledge tracing (KT) is one of the fundamental tasks for student behavioral data analysis, aiming to monitor students' evolving knowledge state during their problem-solving process. In…
Descriptors: Student Behavior, Electronic Learning, Data Analysis, Models
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