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Felicitas Macgilchrist; Juliane Jarke – Discourse: Studies in the Cultural Politics of Education, 2025
AI-powered predictive systems have high margins of error. However, data visualisations of algorithmic systems in education and other social fields tend to visualise certainty, thus invisibilising the underlying approximations and uncertainties of the algorithmic systems and the social settings in which these systems operate. This paper draws on a…
Descriptors: Educational Technology, Visual Aids, Learning Management Systems, Prediction
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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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Gisu Sanem Öztas; Gökhan Akçapinar – Educational Technology & Society, 2025
This study aimed to develop a prediction model to classify students based on their academic procrastination tendencies, which were measured and classified as low and high using a self-report tool developed based on the students' assignment submission behaviours logged in the learning management system's database. The students' temporal learning…
Descriptors: Time Management, Student Behavior, Online Courses, Learning Management Systems
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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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Hai Li; Wanli Xing; Chenglu Li; Wangda Zhu; Simon Woodhead – Journal of Learning Analytics, 2025
Knowledge tracing (KT) is a method to evaluate a student's knowledge state (KS) based on their historical problem-solving records by predicting the next answer's binary correctness. Although widely applied to closed-ended questions, it lacks a detailed option tracing (OT) method for assessing multiple-choice questions (MCQs). This paper introduces…
Descriptors: Mathematics Tests, Multiple Choice Tests, Computer Assisted Testing, Problem Solving
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Yuanlan Jiang; Jian-E Peng – Computer Assisted Language Learning, 2025
Language learner engagement, which is receiving increased attention, has predominantly focused on offline classroom contexts, while learner engagement in language Massive Open Online Courses (LMOOCs) remains under-explored. This study was conducted on a College English MOOC with the purpose of examining learner engagement and its relations with…
Descriptors: Learner Engagement, Personal Autonomy, Second Language Learning, Second Language Instruction