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Ley, Tobias; Tammets, Kairit; Pishtari, Gerti; Chejara, Pankaj; Kasepalu, Reet; Khalil, Mohammad; Saar, Merike; Tuvi, Iiris; Väljataga, Terje; Wasson, Barbara – Journal of Computer Assisted Learning, 2023
Background: With increased use of artificial intelligence in the classroom, there is now a need to better understand the complementarity of intelligent learning technology and teachers to produce effective instruction. Objective: The paper reviews the current research on intelligent learning technology designed to make models of student learning…
Descriptors: Artificial Intelligence, Technology Uses in Education, Learning Analytics, Instructional Effectiveness
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Elena Drugova; Irina Zhuravleva; Ulyana Zakharova; Adel Latipov – Journal of Computer Assisted Learning, 2024
Background: Driven by the ongoing need to provide high-quality learning and teaching, universities recently have shown an increased interest in using learning analytics (LA) for improving learning design (LD). However, the evidence of such improvements is scarce, and the maturity of such research is unclear. Objectives: This study is aimed to…
Descriptors: Learning Analytics, Instructional Design, Higher Education, Instructional Improvement
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Adelson de Araujo; Pantelis M. Papadopoulos; Susan McKenney; Ton de Jong – Journal of Computer Assisted Learning, 2024
Background: Sustaining productive student-student dialogue in online collaborative inquiry learning is challenging, and teacher support is limited when needed in multiple groups simultaneously. Collaborative conversational agents (CCAs) have been used in the past to support student dialogue. Yet, research is needed to reveal the characteristics…
Descriptors: Learning Analytics, Computer Mediated Communication, Artificial Intelligence, Dialogs (Language)
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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
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Lahza, Hatim; Khosravi, Hassan; Demartini, Gianluca – Journal of Computer Assisted Learning, 2023
Background: The use of crowdsourcing in a pedagogically supported form to partner with learners in developing novel content is emerging as a viable approach for engaging students in higher-order learning at scale. However, how students behave in this form of crowdsourcing, referred to as learnersourcing, is still insufficiently explored.…
Descriptors: Learning Analytics, Learning Strategies, Electronic Learning, Independent Study
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Luzhen Tang; Kejie Shen; Huixiao Le; Yuan Shen; Shufang Tan; Yueying Zhao; Torsten Juelich; Xinyu Li; Dragan Gaševic; Yizhou Fan – Journal of Computer Assisted Learning, 2024
Background: Learners' writing skills are critical to their academic and professional development. Previous studies have shown that learners' self-assessment during writing is essential for assessing their writing products and monitoring their writing processes. However, conducting practical self-assessments of writing remains challenging for…
Descriptors: Self Evaluation (Individuals), Formative Evaluation, Writing Assignments, Writing Skills