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Ani Grubišic; Ines Šaric-Grgic; Angelina Gašpar; Branko Žitko – Journal of Computer Assisted Learning, 2025
Background: Adaptive educational systems have gained increasing attention due to their ability to personalise educational content based on individual learner progress. Prior research highlights that intelligent tutoring systems (ITSs) and adaptive courseware models improve learning outcomes by dynamically adjusting instructional materials.…
Descriptors: Usability, Courseware, Natural Language Processing, Intelligent Tutoring Systems
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Hui Shi; Nuodi Zhang; Secil Caskurlu; Hunhui Na – Journal of Computer Assisted Learning, 2025
Background: The growth of online education has provided flexibility and access to a wide range of courses. However, the self-paced and often isolated nature of these courses has been associated with increased dropout and failure rates. Researchers employed machine learning approaches to identify at-risk students, but multiple issues have not been…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, At Risk Students
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Héctor J. Pijeira-Díaz; Shashank Subramanya; Janneke van de Pol; Anique de Bruin – Journal of Computer Assisted Learning, 2024
Background: When learning causal relations, completing causal diagrams enhances students' comprehension judgements to some extent. To potentially boost this effect, advances in natural language processing (NLP) enable real-time formative feedback based on the automated assessment of students' diagrams, which can involve the correctness of both the…
Descriptors: Learning Analytics, Automation, Student Evaluation, Causal Models