Neuroimaging tools in multimedia learning: a systematic review

ABSTRACT

This study aims to conduct a systematic review of studies on neuroimaging measurements used in multimedia learning research. The particular aim of the review is to explore how cognitive processes in multimedia learning are studied with relevant variables through neuroimaging technology. Studies that met the inclusive criteria were selected and analyzed with the data entry tool. Forty articles were reviewed based on the research questions about the research characteristics, the type of learning environments, the variables, the types of cognitive load, the other cognitive load measurements, the types of neuroimaging measures, the techniques that should be known in the field of neuroimaging to study cognitive load in multimedia learning. The results revealed that most of the studies preferred using both subjective and other objective measures to assess cognitive load in addition to neuroimaging measures. The studies examined learning outcomes, cognitive processes, and some other variables besides measuring cognitive load. The most striking observation to emerge from the analysis is that Electroencephalography, Functional Magnetic Resonance Imaging, Functional Near-Infrared Spectroscopy, and Transcranial Doppler Ultrasonography have been found as the most preferred neuroimaging tools utilized in multimedia learning research. Research results were interpreted, and several gaps in research relating to multimedia learning were identified.

KEYWORDS:

• Neuroimaging
• multimedia learning
• cognitive load
• EEG
• fMRI
• fNIRS

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Notes on contributors

Pinar Ozel

Pınar Özel received her BS degree in Electrical-Electronics Engineering from Erciyes University, Kayseri, in 2007, and MS degree in Biomedical Engineering from Bogazici University, İstanbul in 2011. She obtained her Ph.D. degree in Biomedical Engineering from Istanbul University in 2019. As a professional experience, she worked as a product manager. Her research areas are signal processing and machine learning techniques to biomedical signals.

Duygu Mutlu-Bayraktar

Duygu Mutlu Bayraktar received her Ph.D. in Computer Education and Instructional Technology Department from Marmara University in July 2014. She obtained her master’s degree in Computer Education and Instructional Technology Department from Hacettepe University in June 2010. Her research interests include multimedia learning, instructional design, eye-tracking, cognitive science, and human–computer interaction.

Tugba Altan

Tugba Altan, Ph.D., is a research assistant at the Department of Educational Sciences in the Faculty of Education at Kahramanmaras Sutcu Imam University, Turkey. Her research focuses on designing and evaluating multimedia learning environments such as textbooks and mobile apps in K12 and teacher education, integrating massively multiplayer online role-playing games into K12 education, and using mobile technologies in teacher education.

Veysel Coskun

Veysel Coskun received his Ph.D. degree in Computer Education and Instructional Technology from Marmara University in January 2018. He obtained his master’s degree in Computer Education and Instructional Technology from Sakarya University in June 2009. His research interests include human–computer interaction, multimedia learning, computer science education, and instructional design.

Ali Olamat

Ali Olamat received his bachelor’s degree in Biomedical Engineering from Hashemite University, Zarqa-Jordan, in 2007. He received his master’s degree in Biomedical Engineering from the University of Luebeck, Luebeck – Germany, in 2010. He received his Ph.D. in Biomedical Engineering from Istanbul University, Istanbul-Turkey, in 2019. His research interests include Signal and Systems, Digital Signal Processing, Imaging and Image processing, Numerical methods and Mathematics for Engineering, Machine-Learning and Deep-Learning.