Detecting fluctuations in student engagement and retention during video lectures using electroencephalography

Bibliographic Data

ID21297413
AuthorsIdo Davidesco (0000-0003-0754-5807, Department of Educational Psychology University of Connecticut Storrs Connecticut USA, corresponding author), Nina Glaser (0000-0002-1966-2720, School of Information Science & Learning Technologies University of Missouri Columbia Missouri USA), Ian H Stevenson (0000-0002-1428-5946, Department of Psychological Sciences University of Connecticut Storrs Connecticut USA), Or Dagan (0000-0002-4674-5425, Center for Neural Science New York University New York New York USA)
Year2023
Volume54
Issue6
Pages1895-1916
Publication date2023-11-01
Open AccessYes
TypeARTICLE
VenueBritish Journal of Educational Technology (JOURNAL)
Journal identifiersISSN: 0007-1013 • E-ISSN: 1467-8535
PublisherWiley (PUBLISHER • GB)
DOI10.1111/bjet.13330
OpenAlexW4366827977
LanguageEN
Citations received4
References cited40

Video lectures are commonly used in online and flipped courses, but students often find it challenging to stay engaged and retain lecture content. The current study examined to what extent the power of electroencephalography (EEG) brain activity in the theta (4–7 Hz), alpha (8–12 Hz) and beta (13–20 Hz) bands can dynamically capture fluctuations of student engagement and retention throughout pre‐recorded lectures. EEG activity was recorded from 33 college students throughout four video‐based chemistry lectures. In‐video probes were used to assess both student engagement and content retention at random moments during the video. Our findings reveal that there are significant fluctuations in self‐reported engagement throughout pre‐recorded lectures. Further, among the three frequency bands that were tested, only alpha power closely tracked fluctuations in self‐reported engagement at the individual student level. In‐lecture fluctuations in engagement were associated with content retention, but content retention itself was not well captured by EEG activity in any of the frequency bands that were examined. These findings suggest that the design of video lectures should consider fluctuations in student engagement and potentially incorporate self‐reported and physiological indicators of engagement. Future research should further investigate how EEG and other physiological engagement indicators can be used in real time to personalize online instruction. Practitioner Notes What is already known about this topic Students often find it challenging to stay engaged during online lectures and retain lecture content. Measuring engagement and retention throughout an online lecture (rather than only at its end) is important but challenging because it requires the insertion of in‐lecture questions that interrupt the learning process. Electroencephalography (EEG) could potentially provide a continuous and implicit measure of engagement and retention throughout online lectures. What this paper adds Self‐reported engagement tends to gradually decrease throughout the duration of video lectures with substantial variation both within and between students. Fluctuations in student engagement are predictive of content retention throughout video lectures. EEG power in the alpha band (8–12 Hz) dynamically tracks fluctuations in student engagement. EEG power in the alpha band significantly predicts overall lecture engagement as well as learning confidence. However, EEG power might not be sensitive to variations in post‐lecture test performance. Implications for practice and/or policy The design of online lectures should take into consideration the dynamic and idiosyncratic nature of student engagement. In‐video self‐report probes and EEG power measures can be useful sources of information on students' level of engagement during online lectures. It should be further investigated whether EEG and other physiological indicators of engagement can be used in real time to personalize online instruction.

Content (measure theory) · Electroencephalography · Knowledge retention · Mathematics education · Medical education · Student engagement · EEG and Brain-Computer Interfaces · Medicine · Mind wandering and attention · Neuroscience · Neuroscience, Education and Cognitive Function · Psychology

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