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Orji, Fidelia A.; Vassileva, Julita – Journal of Educational Computing Research, 2023
There is a dearth of knowledge on how persuasiveness of influence strategies affects students' behaviours when using online educational systems. Persuasiveness is a term used in describing a system's capability to motivate desired behaviour. Most existing approaches for assessing the persuasiveness of a system are based on subjective measures…
Descriptors: Influences, Student Behavior, Artificial Intelligence, Electronic Learning
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
Hunte, Melissa R.; McCormick, Samantha; Shah, Maitree; Lau, Clarissa; Jang, Eunice Eunhee – Assessment in Education: Principles, Policy & Practice, 2021
Children's oral language proficiency (OLP) is integral for developing literacy skills. Storytelling or retelling is often used by parents and educators to elicit children's OLP, yet it is less commonly used for assessment purposes. Leveraged by natural language processing and machine learning, this study examined the extent to which computational…
Descriptors: Scores, Natural Language Processing, Oral Language, Language Proficiency
Cui, Ying; Chen, Fu; Shiri, Ali – Information and Learning Sciences, 2020
Purpose: This study aims to investigate the feasibility of developing general predictive models for using the learning management system (LMS) data to predict student performances in various courses. The authors focused on examining three practical but important questions: are there a common set of student activity variables that predict student…
Descriptors: Foreign Countries, Identification, At Risk Students, Prediction