Models for Classifying Cognitive Load Using Physiological Data in Healthcare Context: A Scoping Review

Background: In healthcare education, understanding and managing cognitive load is crucial for enhancing learning outcomes for students, healthcare professionals, patients, and the general public. Despite numerous studies developing cognitive load classification models, there is a lack of comprehensive guidelines on how to effectively utilize these models in healthcare education. This study reviews cognitive load classification models using physiological data to provide insights and guidelines for their development in healthcare contexts. Methods: A scoping review was conducted on studies published between 2015 and 2024, identified through databases including Scopus, Web of Science, PubMed, EMBASE, and PsycINFO. The search terms included “cognitive load,” “physiology,” “data,” and “classification.” Inclusion criteria were peer-reviewed journal articles in English, focused on the healthcare context, utilizing experimental physiological data, and developing classification models. After screening 351 articles, ten studies met the inclusion criteria and were analyzed in detail. Results: Task design predominantly focused on measuring intrinsic cognitive load by adjusting task difficulty. Data collection mainly utilized EEG (electroencephalogram) and body movement data. SVM (support vector machine) algorithms were the most frequently used for model development, with cross-validation and feature selection employed to prevent overfitting. This study derived the importance of clearly defining cognitive load types, designing appropriate tasks, establishing reliable ground truths with multiple indicators, and selecting contextually relevant data. Conclusions: This study provides a comprehensive analysis of cognitive load classification models using physiological data in healthcare education, offering valuable guidelines for their development. Despite the study’s limitations, including a small number of analyzed papers and limited diversity in educational contexts, it offers critical insights for using and developing cognitive load classification in healthcare education. Future research should explore the applicability of these models across diverse educational settings and populations, aiming to enhance the effectiveness of healthcare education and ultimately improve learning and healthcare outcomes.