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The Applications of Machine Learning in Computational Thinking Assessments: A Scoping Review
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Bin Tan; Hao-Yue Jin; Maria Cutumisu – Computer Science Education, 2024
Background and Context: Computational thinking (CT) has been increasingly added to K-12 curricula, prompting teachers to grade more and more CT artifacts. This has led to a rise in automated CT assessment tools. Objective: This study examines the scope and characteristics of publications that use machine learning (ML) approaches to assess…
Descriptors: Computation, Thinking Skills, Artificial Intelligence, Student Evaluation
Using Machine Learning to Predict UK and Japanese Secondary Students' Life Satisfaction in PISA 2018
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Zexuan Pan; Maria Cutumisu – British Journal of Educational Psychology, 2024
Background: Life satisfaction is a key component of students' subjective well-being due to its impact on academic achievement and lifelong health. Although previous studies have investigated life satisfaction through different lenses, few of them employed machine learning (ML) approaches. Objective: Using ML algorithms, the current study predicts…
Descriptors: Artificial Intelligence, Secondary School Students, Life Satisfaction, Foreign Countries
Employing Machine Learning to Predict Medical Trainees' Psychophysiological Responses and Self- and Socially-Shared Regulated Learning Strategies While Completing Medical Simulations
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Matthew Moreno; Keerat Grewal; Maria Cutumisu; Jason M. Harley – Educational Psychology Review, 2025
Medical simulations allow medical trainees to work within teams to develop their self-regulated learning (SRL) and socially shared regulated learning (SSRL) skills. These skills are imperative in optimizing performance and teamwork and could be reflected in physiological responses given by learners. This study examines how medical trainees'…
Descriptors: Artificial Intelligence, Technology Uses in Education, Prediction, Algorithms
Employing Machine Learning to Predict Medical Trainees' Psychophysiological Responses and Self- and Socially-Shared Regulated Learning Strategies While Completing Medical Simulations
Peer reviewed Peer reviewed
Direct linkDirect link
Matthew Moreno; Keerat Grewal; Maria Cutumisu; Jason M. Harley – Educational Psychology Review, 2025
Medical simulations allow medical trainees to work within teams to develop their self-regulated learning (SRL) and socially shared regulated learning (SSRL) skills. These skills are imperative in optimizing performance and teamwork and could be reflected in physiological responses given by learners. This study examines how medical trainees'…
Descriptors: Artificial Intelligence, Technology Uses in Education, Prediction, Algorithms