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Showing 1 to 15 of 29 results Save | Export
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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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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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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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Deming Li; Wei Xing – Education and Information Technologies, 2025
The sustainable development of Internet education platforms has not been a research focus due to the continuous renewal of artificial intelligence, big data, and policies across different countries and regions. To address this gap, this paper utilizes bibliometric analysis and visualization tools to analyze the development of online education…
Descriptors: Sustainable Development, Electronic Learning, Data Analysis, Internet
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Christothea Herodotou; Sagun Shrestha; Catherine Comfort; Heshan Andrews; Paul Mulholland; Vaclav Bayer; Claire Maguire; John Lee; Miriam Fernandez – Journal of Learning Analytics, 2025
In this paper, we explore the design of a student-facing dashboard for online and distance learning with a focus on capturing and addressing specific learning needs. A participatory process involving 20 students was employed, which included a screening questionnaire and focus group discussions. The selection of data points to be displayed on the…
Descriptors: Electronic Learning, Distance Education, Student Attitudes, Educational Technology
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Seiyon M. Lee; Sami Baral; Hongming Chip Li; Li Cheng; Shan Zhang; Carly S. Thorp; Jennifer St. John; Tamisha Thompson; Neil Heffernan; Anthony F. Botelho – Journal of Educational Data Mining, 2025
Teachers often use open-ended questions to promote students' deeper understanding of the content. These questions are particularly useful in K-12 mathematics education, as they provide richer insights into students' problem-solving processes compared to closed-ended questions. However, they are also challenging to implement in educational…
Descriptors: Feedback (Response), Taxonomy, Data Analysis, Middle School Mathematics
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Andrew J. Collins; Christopher Lynch; Jim Leathrum; Gayane Grigoryan; T. Stephen Cotter; Ross Gore; Brandon Butler – Adult Learning, 2025
Higher education programs are rapidly transitioning online in support of a broader geographic base, working professionals, and, recently, emergency contingencies such as COVID-19. The flexibility of online courses makes them attractive to adult learners; as such, there is much academic discussion about online learning for adult learners and the…
Descriptors: Minicourses, Online Courses, Adult Students, Professional Education
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Mohamed Zine; Fouzi Harrou; Mohammed Terbeche; Ying Sun – Education and Information Technologies, 2025
E-learning readiness (ELR) is critical for implementing digital education strategies, particularly in developing countries where online learning faces unique challenges. This study aims to provide a concise and actionable framework for assessing and predicting ELR in Algerian universities by combining the ADKAR model with advanced machine learning…
Descriptors: Electronic Learning, Learning Readiness, Artificial Intelligence, Organizational Change
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Nick Hopwood; Tracey-Ann Palmer; Gloria Angela Koh; Mun Yee Lai; Yifei Dong; Sarah Loch; Kun Yu – International Journal of Research & Method in Education, 2025
Student emotions influence assessment task behaviour and performance but are difficult to study empirically. The study combined qualitative data from focus group interviews with 22 students and 4 teachers, with quantitative real-time learning analytics (facial expression, mouse click and keyboard strokes) to examine student emotional engagement in…
Descriptors: Psychological Patterns, Student Evaluation, Learning Analytics, Learner Engagement
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Fang-Ying Yang; Yuan-Li Liu; Shih-Chieh Chien; Yi-Wen Hung – Educational Technology & Society, 2025
In this study, an interactive science learning app on the topic of plate tectonics was developed for tablets to promote argumentative reasoning. The app guided learners through learning stages that required them to propose arguments, identify relevant evidence, acquire background knowledge, and engage in argumentative reasoning in different…
Descriptors: Abstract Reasoning, Persuasive Discourse, Visual Perception, Attention
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Shelly Xueting Ye; Jia Shi – Educational Studies, 2025
Copying words by writing and typing are two effective methods in traditional learning, but their merits for learning L2 words have seldom been compared. Because of technological advances and the growing popularity of utilising smart mobile products to acquire L2 vocabulary, this study investigated the effectiveness of writing and typing techniques…
Descriptors: Second Language Learning, Language Acquisition, Handheld Devices, Electronic Learning
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Qixuan Wu; Hyung Jae Chang; Long Ma – Journal of Advanced Academics, 2025
It is very important to identify talented students as soon as they are admitted to college so that appropriate resources are provided and allocated to them to optimize and excel in their education. Currently, this process is labor-intensive and time-consuming, as it involves manual reviews of each student's academic record. This raises the…
Descriptors: Electronic Learning, Artificial Intelligence, Technology Uses in Education, Natural Language Processing
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