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Aidan Doyle; Pragnya Sridhar; Arav Agarwal; Jaromir Savelka; Majd Sakr – Journal of Computer Assisted Learning, 2025
Background: In computing education, educators are constantly faced with the challenge of developing new curricula, including learning objectives (LOs), while ensuring that existing courses remain relevant. Large language models (LLMs) were shown to successfully generate a wide spectrum of natural language artefacts in computing education.…
Descriptors: Computer Science Education, Artificial Intelligence, Learning Objectives, Curriculum Development
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Gerti Pishtari; María Jesús Rodríguez-Triana; Luis P. Prieto; Adolfo Ruiz-Calleja; Terje Väljataga – Journal of Computer Assisted Learning, 2024
Background: In the field of Learning Design, it is common that researchers analyse manually design artefacts created by practitioners, using pedagogically-grounded approaches (e.g., Bloom's Taxonomy), both to understand and later to support practitioners' design practices. Automatizing these high-level pedagogically-grounded analyses would enable…
Descriptors: Electronic Learning, Instructional Design, Active Learning, Inquiry
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Umar Alkafaween; Ibrahim Albluwi; Paul Denny – Journal of Computer Assisted Learning, 2025
Background: Automatically graded programming assignments provide instant feedback to students and significantly reduce manual grading time for instructors. However, creating comprehensive suites of test cases for programming problems within automatic graders can be time-consuming and complex. The effort needed to define test suites may deter some…
Descriptors: Automation, Grading, Introductory Courses, Programming
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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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Pieter Vanneste; Kim Dekeyser; Luis Alberto Pinos Ullauri; Dries Debeer; Frederik Cornillie; Fien Depaepe; Annelies Raes; Wim Van den Noortgate; Sameh Said-Metwaly – Journal of Computer Assisted Learning, 2024
Background: Augmented reality (AR) is receiving increasing interest as a tool to create an interactive and motivating learning environment. Yet, it is unclear how instructional support affects performance in AR. Objectives: This study sought to explore how varying the instructional support in AR can affect performance-related behaviours of…
Descriptors: Computer Simulation, Artificial Intelligence, Cognitive Ability, Student Behavior
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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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Angxuan Chen; Yuyue Zhang; Jiyou Jia; Min Liang; Yingying Cha; Cher Ping Lim – Journal of Computer Assisted Learning, 2025
Background: Language assessment plays a pivotal role in language education, serving as a bridge between students' understanding and educators' instructional approaches. Recently, advancements in Artificial Intelligence (AI) technologies have introduced transformative possibilities for automating and personalising language assessments. Objectives:…
Descriptors: Artificial Intelligence, Technology Uses in Education, Computer Assisted Testing, Language Tests
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Brna, Paul; Cox, R. – Journal of Computer Assisted Learning, 1998
Discussion of learner-centered design focuses on the development of switchEr, a specific learning environment changed to an intelligent learning environment by switching from one external representation (ER) to another. Topics include user-centered design; the role of artificial intelligence; and the development of effective educational computing…
Descriptors: Artificial Intelligence, Computer Assisted Instruction, Computer System Design, Educational Environment
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Ross, Peter – Journal of Computer Assisted Learning, 1987
Discusses intelligent tutoring systems (ITS), one application of artificial intelligence to computers used in education. Basic designs of ITSs are described; examples are given including PROUST, GREATERP, and the use of simulation with ITSs; protocol analysis is discussed; and 38 prototype ITSs are listed. (LRW)
Descriptors: Artificial Intelligence, Computer Assisted Instruction, Computer Simulation, Computer System Design