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Meriem Zerkouk; Miloud Mihoubi; Belkacem Chikhaoui; Shengrui Wang – Education and Information Technologies, 2024
School dropout is a significant issue in distance learning, and early detection is crucial for addressing the problem. Our study aims to create a binary classification model that anticipates students' activity levels based on their current achievements and engagement on a Canadian Distance learning Platform. Predicting student dropout, a common…
Descriptors: Artificial Intelligence, Dropouts, Prediction, Distance Education
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Hoa-Huy Nguyen; Kien Do Trung; Loc Nguyen Duc; Long Dang Hoang; Phong Tran Ba; Viet Anh Nguyen – Education and Information Technologies, 2024
This article presents the results of an experiment in personalizing course content and learning activity model tailored for online courses based on students' learning styles. The main research objectives are to design and pilot a model to determine students' learning styles to create personalized online courses. The study also addressed an…
Descriptors: Models, Online Courses, Cognitive Style, Classification
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Yangyang Luo; Xibin Han; Chaoyang Zhang – Asia Pacific Education Review, 2024
Learning outcomes can be predicted with machine learning algorithms that assess students' online behavior data. However, there have been few generalized predictive models for a large number of blended courses in different disciplines and in different cohorts. In this study, we examined learning outcomes in terms of learning data in all of the…
Descriptors: Prediction, Learning Management Systems, Blended Learning, Classification
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Xiaoxiao Liu; Jiahua Liu; Carrie Demmans Epp; Ying Cui – Educational Technology Research and Development, 2025
Parental involvement is essential to children's learning engagement activities and academic performance. Much research revolves around the impact of parental involvement on students' academic performance or the relationship between student engagement and grades. However, few studies have used process data to examine the relationship between…
Descriptors: Parent Participation, Parent Child Relationship, Learner Engagement, Academic Achievement
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Owen Henkel; Hannah Horne-Robinson; Maria Dyshel; Greg Thompson; Ralph Abboud; Nabil Al Nahin Ch; Baptiste Moreau-Pernet; Kirk Vanacore – Journal of Learning Analytics, 2025
This paper introduces AMMORE, a new dataset of 53,000 math open-response question-answer pairs from Rori, a mathematics learning platform used by middle and high school students in several African countries. Using this dataset, we conducted two experiments to evaluate the use of large language models (LLM) for grading particularly challenging…
Descriptors: Learning Analytics, Learning Management Systems, Mathematics Instruction, Middle School Students
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Jamiu Adekunle Idowu – International Journal of Artificial Intelligence in Education, 2024
This systematic literature review investigates the fairness of machine learning algorithms in educational settings, focusing on recent studies and their proposed solutions to address biases. Applications analyzed include student dropout prediction, performance prediction, forum post classification, and recommender systems. We identify common…
Descriptors: Algorithms, Dropouts, Prediction, Academic Achievement
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Mohd Fazil; Angelica Rísquez; Claire Halpin – Journal of Learning Analytics, 2024
Technology-enhanced learning supported by virtual learning environments (VLEs) facilitates tutors and students. VLE platforms contain a wealth of information that can be used to mine insight regarding students' learning behaviour and relationships between behaviour and academic performance, as well as to model data-driven decision-making. This…
Descriptors: Learning Analytics, Learning Management Systems, Learning Processes, Decision Making
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Arnon Hershkovitz; Norbert Noster; Hans-Stefan Siller; Michal Tabach – ZDM: Mathematics Education, 2024
Learning Analytics is concerned with the use of data collected in educational settings to support learning processes. We take a Learning Analytics approach to study the use of immediate feedback in digital classification tasks in mathematics. Feedback serves as an opportunity for learning, however its mere existence does not guarantee its use and…
Descriptors: Learning Analytics, Classification, Geometry, Mathematics Instruction
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Min-Chi Chiu; Gwo-Jen Hwang; Lu-Ho Hsia; Fong-Ming Shyu – Interactive Learning Environments, 2024
In a conventional art course, it is important for a teacher to provide feedback and guidance to individual students based on their learning status. However, it is challenging for teachers to provide immediate feedback to students without any aid. The advancement of artificial intelligence (AI) has provided a possible solution to cope with this…
Descriptors: Art Education, Artificial Intelligence, Teaching Methods, Comparative Analysis