Enhancing the Learning Experience Using Real-Time Cognitive Evaluation

Chaouachi, Maher; Jraidi, Imène; Lajoie, Susanne P.; Frasson, Claude

doi:10.18178/ijiet.2019.9.10.1287

Cited by 4 publications

(3 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent studies demonstrated that machine learning approaches, where a linear classifier is trained to discriminate between high and low levels of attention based on EEG features, can achieve a very high level of classification accuracy (eg, Chen et al, 2017). In a recent example, an adaptive tutoring system based on EEG measures of cognitive engagement and load had a positive impact on learning outcomes (Chaouachi et al, 2019). However, other studies reported that attention classifiers are not easily generalizable across students (Dhindsa et al, 2019) and are not predictive of how student engagement is rated by annotators (Booth et al, 2018).…”

Section: Eeg-facilitated Attention Aware Systemsmentioning

confidence: 99%

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

Davidesco

Glaser

Stevenson

et al. 2023

Brit J Educational Tech

View full text Add to dashboard Cite

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 NotesWhat 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.

show abstract

Section: Eeg-facilitated Attention Aware Systemsmentioning

confidence: 99%

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

Davidesco

Glaser

Stevenson

et al. 2023

Brit J Educational Tech

View full text Add to dashboard Cite

show abstract

“…For the workload index, if the slope value is between −0.03 and +0.03, then the workload is considered as optimal. Otherwise, if the slope value is above 0.03, the learner is considered as overloaded, and if the slope is below −0.03, the learner is considered as underloaded [91]. Moreover, if the learner is both mentally engaged and has an optimal workload, then the learner's mental state is considered positive.…”

Section: Eeg Recordingmentioning

confidence: 99%

Understanding Clinical Reasoning through Visual Scanpath and Brain Activity Analysis

et al. 2022

Self Cite

View full text Add to dashboard Cite

This paper presents an experimental study that analyzes learners’ visual behaviour and brain activity in clinical reasoning. An acquisition protocol was defined to record eye tracking and EEG data from 15 participants as they interact with a computer-based learning environment called Amnesia, a medical simulation system that assesses the analytical skills of novice medicine students while they solve patient cases. We use gaze data to assess learners’ visual focus and present our methodology to track learners’ reasoning process through scanpath pattern analysis. We also describe our methodology for examining learners’ cognitive states using mental engagement and workload neural indexes. Finally, we discuss the relationship between gaze path information and EEG and how our analyses can lead to new forms of clinical diagnostic reasoning assessment.

show abstract

“…40,[42][43][44]46,47,[52][53][54][55][56][57][58][59][60][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81]84,86,[88][89][90][91][92]96], while the remainder (specifically 17, 21%) were conference papers…”

mentioning

confidence: 99%

Ratio Indexes Based on Spectral Electroencephalographic Brainwaves for Assessment of Mental Involvement: A Systematic Review

Marcantoni,

Assogna,

Del Borrello

et al. 2023

Sensors

View full text Add to dashboard Cite

Background: This review systematically examined the scientific literature about electroencephalogram-derived ratio indexes used to assess human mental involvement, in order to deduce what they are, how they are defined and used, and what their best fields of application are. (2) Methods: The review was carried out according to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. (3) Results: From the search query, 82 documents resulted. The majority (82%) were classified as related to mental strain, while 12% were classified as related to sensory and emotion aspects, and 6% to movement. The electroencephalographic electrode montage used was low-density in 13%, high-density in 6% and very-low-density in 81% of documents. The most used electrode positions for computation of involvement indexes were in the frontal and prefrontal cortex. Overall, 37 different formulations of involvement indexes were found. None of them could be directly related to a specific field of application. (4) Conclusions: Standardization in the definition of these indexes is missing, both in the considered frequency bands and in the exploited electrodes. Future research may focus on the development of indexes with a unique definition to monitor and characterize mental involvement.

show abstract

Enhancing the Learning Experience Using Real-Time Cognitive Evaluation

Cited by 4 publications

References 51 publications

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

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

Understanding Clinical Reasoning through Visual Scanpath and Brain Activity Analysis

Ratio Indexes Based on Spectral Electroencephalographic Brainwaves for Assessment of Mental Involvement: A Systematic Review

Contact Info

Product

Resources

About