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Semi-Automating the Scoping Review Process: Is It Worthwhile? A Methodological Evaluation
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Shan Zhang; Chris Palaguachi; Marcin Pitera; Chris Davis Jaldi; Noah L. Schroeder; Anthony F. Botelho; Jessica R. Gladstone – Educational Psychology Review, 2024
Systematic reviews are a time-consuming yet effective approach to understanding research trends. While researchers have investigated how to speed up the process of screening studies for potential inclusion, few have focused on to what extent we can use algorithms to extract data instead of human coders. In this study, we explore to what extent…
Descriptors: Bibliometrics, Meta Analysis, Research Methodology, Evaluation Methods
Screening Smarter, Not Harder: A Comparative Analysis of Machine Learning Screening Algorithms and Heuristic Stopping Criteria for Systematic Reviews in Educational Research
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Diego G. Campos; Tim Fütterer; Thomas Gfrörer; Rosa Lavelle-Hill; Kou Murayama; Lars König; Martin Hecht; Steffen Zitzmann; Ronny Scherer – Educational Psychology Review, 2024
Systematic reviews and meta-analyses are crucial for advancing research, yet they are time-consuming and resource-demanding. Although machine learning and natural language processing algorithms may reduce this time and these resources, their performance has not been tested in education and educational psychology, and there is a lack of clear…
Descriptors: Artificial Intelligence, Algorithms, Computer System Design, Natural Language Processing
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