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Showing 1 to 15 of 35 results Save | Export
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Lottridge, Susan; Woolf, Sherri; Young, Mackenzie; Jafari, Amir; Ormerod, Chris – Journal of Computer Assisted Learning, 2023
Background: Deep learning methods, where models do not use explicit features and instead rely on implicit features estimated during model training, suffer from an explainability problem. In text classification, saliency maps that reflect the importance of words in prediction are one approach toward explainability. However, little is known about…
Descriptors: Documentation, Learning Strategies, Models, Prediction
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Xiong, Yao; Schunn, Christian D.; Wu, Yong – Journal of Computer Assisted Learning, 2023
Background: For peer assessment, reliability (i.e., consistency in ratings across peers) and validity (i.e., consistency of peer ratings with instructors or experts) are frequently examined in the research literature to address a central concern of instructors and students. Although the average levels are generally promising, both reliability and…
Descriptors: Peer Evaluation, Computer Assisted Testing, Test Reliability, Test Validity
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Yingbin Zhang; Yafei Ye; Luc Paquette; Yibo Wang; Xiaoyong Hu – Journal of Computer Assisted Learning, 2024
Background: Learning analytics (LA) research often aggregates learning process data to extract measurements indicating constructs of interest. However, the warranty that such aggregation will produce reliable measurements has not been explicitly examined. The reliability evidence of aggregate measurements has rarely been reported, leaving an…
Descriptors: Learning Analytics, Learning Processes, Test Reliability, Psychometrics
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Zirou Lin; Hanbing Yan; Li Zhao – Journal of Computer Assisted Learning, 2024
Background: Peer assessment has played an important role in large-scale online learning, as it helps promote the effectiveness of learners' online learning. However, with the emergence of numerical grades and textual feedback generated by peers, it is necessary to detect the reliability of the large amount of peer assessment data, and then develop…
Descriptors: Peer Evaluation, Automation, Grading, Models
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Dominic Lohr; Hieke Keuning; Natalie Kiesler – Journal of Computer Assisted Learning, 2025
Background: Feedback as one of the most influential factors for learning has been subject to a great body of research. It plays a key role in the development of educational technology systems and is traditionally rooted in deterministic feedback defined by experts and their experience. However, with the rise of generative AI and especially large…
Descriptors: College Students, Programming, Artificial Intelligence, Feedback (Response)
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Blaženka Divjak; Barbi Svetec; Damir Horvat – Journal of Computer Assisted Learning, 2024
Background: Sound learning design should be based on the constructive alignment of intended learning outcomes (LOs), teaching and learning activities and formative and summative assessment. Assessment validity strongly relies on its alignment with LOs. Valid and reliable formative assessment can be analysed as a predictor of students' academic…
Descriptors: Automation, Formative Evaluation, Test Validity, Test Reliability
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Arshad, Arooj; Ghazal, Saima; Saleem, Noshina; Hanan, Mian Ahmad; Arshad, Muhammad Haseeb – Journal of Computer Assisted Learning, 2022
Background: In this technologically advanced era, media literacy is necessary to effectively evaluate the information and understand various biases inherent in media messages. Several media literacy (ML) tools are available; however, we need generic and objective tools that can be applied to all forms of media messages. Objectives: The current…
Descriptors: Media Literacy, Foreign Countries, Measurement Techniques, Measures (Individuals)
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Wen Xin Zhang; John J. H. Lin; Ying-Shao Hsu – Journal of Computer Assisted Learning, 2025
Background Study: Assessing learners' inquiry-based skills is challenging as social, political, and technological dimensions must be considered. The advanced development of artificial intelligence (AI) makes it possible to address these challenges and shape the next generation of science education. Objectives: The present study evaluated the SSI…
Descriptors: Artificial Intelligence, Computer Assisted Testing, Inquiry, Active Learning
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Schüler, Anne; Merkt, Martin – Journal of Computer Assisted Learning, 2021
In two experiments, the multimedia contradiction paradigm was used to investigate whether learners map information conveyed through the audio and the picture track of a video. In Experiment 1 (N = 85), the information conveyed through the audio track and the picture track was always consistent (control group) or was made inconsistent by changing…
Descriptors: Video Technology, Cognitive Processes, Multimedia Materials, Eye Movements
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Yue Huang; Joshua Wilson – Journal of Computer Assisted Learning, 2025
Background: Automated writing evaluation (AWE) systems, used as formative assessment tools in writing classrooms, are promising for enhancing instruction and improving student performance. Although meta-analytic evidence supports AWE's effectiveness in various contexts, research on its effectiveness in the U.S. K-12 setting has lagged behind its…
Descriptors: Writing Evaluation, Writing Skills, Writing Tests, Writing Instruction
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Tang, Yuk Ming; Ng, George Wing Yiu; Chia, Nam Hung; So, Eric Hang Kwong; Wu, Chun Ho; Ip, Wai Hung – Journal of Computer Assisted Learning, 2021
Nowadays, patients' safety is the top priority for medical services around the world. However, it is believed that many of the adverse events in hospitals are preventable. Type and screen (T&S) procedures require intense practical training by each medical practitioner in each hospital. This study applied an interactive Virtual Reality (VR)…
Descriptors: Computer Simulation, Medical Education, Readiness, Physicians
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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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Kevin Ackermans; Marjoke Bakker; Pierre Gorissen; Anne-Marieke Loon; Marijke Kral; Gino Camp – Journal of Computer Assisted Learning, 2024
Background: A practical test that measures the information and communication technology (ICT) skills students need for effectively using ICT in primary education has yet to be developed (Oh et al., 2021). This paper reports on the development, validation, and reliability of a test measuring primary school students' ICT skills required for…
Descriptors: Test Construction, Test Validity, Measures (Individuals), Elementary School Students
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Nikola Ebenbeck; Morten Bastian; Andreas Mühling; Markus Gebhardt – Journal of Computer Assisted Learning, 2024
Background: Computerised adaptive tests (CATs) are tests that provide personalised, efficient and accurate measurement while reducing testing time, depending on the desired level of precision. Schools have different types of assessments that can benefit from a significant reduction in testing time to varying degrees, depending on the area of…
Descriptors: Computer Assisted Testing, Elementary Secondary Education, Public Schools, Special Schools
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Mohammad Nayef Ayasrah; Mohamad Ahmad Saleem Khasawneh; Mazen Omar Almulla; Amoura Hassan Aboutaleb – Journal of Computer Assisted Learning, 2025
Background: One area that has been dramatically changed by artificial intelligence (AI) is educational environments. Chatbots, Recommender Systems, Adaptive Learning Systems and Large Language Models have been emerging as practical tools for facilitating learning. However, using such tools appropriately is challenging. In this regard, the…
Descriptors: Test Construction, Test Validity, Test Reliability, Rating Scales
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