Confusion detection using cognitive ability tests

Dakoure, Caroline; Benlamine, Mohamed S.; Frasson, Claude

doi:10.32473/flairs.v34i1.128474

Cited by 2 publications

(1 citation statement)

References 13 publications

(15 reference statements)

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“…In one of the most recent publications we read, there's a significant progress. Dakoure et al [7] built an effective model with EEG signals, which can not only predict confusion, but also break it down into three levels (low, medium, high confusion). By recording and analyzing the EEG signals of ten participants when solving five series of different cognitive exercised, they extracted the power spectra from these cleaned EEG signals and used them as input to train the models.…”

Section: Literature Reviewmentioning

confidence: 99%

Study on the human-brain confusion levels and corresponding EEG levels

Luo,

Zhou,

Yang

et al. 2023

ACE

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Confusion is a state in which an individual is not clear about the situation at hand, does not understand the logic of the matter, or cannot arrive at a reasoned outcome. When people are confused, they often make poor decisions or even fail to make decisions. In this paper, we tested and validated our two models using the collected EEG signals of ten students while watching online courses of different difficulty levels. Both the LSTM model and the DNN model obtained an accuracy rate close to 70% in the validation part, but they showed different characteristics. In the end, we compare and summarize the results of the two models and try to generalize them to other domains in the future.

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Section: Literature Reviewmentioning

confidence: 99%

Study on the human-brain confusion levels and corresponding EEG levels

Luo,

Zhou,

Yang

et al. 2023

ACE

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A Systematic Review of Electroencephalography-Based Emotion Recognition of Confusion Using Artificial Intelligence

Ganepola,

Maduranga,

Tilwari

et al. 2024

Signals

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Confusion emotion in a learning environment can motivate the learner, but prolonged confusion hinders the learning process. Recognizing confused learners is possible; nevertheless, finding them requires a lot of time and effort. Due to certain restrictions imposed by the settings of an online learning environment, the recognition of confused students is a big challenge for educators. Therefore, novel technologies are necessary to handle such crucial difficulties. Lately, Electroencephalography (EEG)-based emotion recognition systems have been rising in popularity in the domain of Education Technology. Such systems have been utilized to recognize the confusion emotion of learners. Numerous studies have been conducted to recognize confusion emotion through this system since 2013, and because of this, a systematic review of the methodologies, feature sets, and utilized classifiers is a timely necessity. This article presents the findings of the review conducted to achieve this requirement. We summarized the published literature in terms of the utilized datasets, feature preprocessing, feature types for model training, and deployed classifiers in terms of shallow machine learning and deep learning-based algorithms. Moreover, the article presents a comparison of the prediction accuracies of the classifiers and illustrates the existing research gaps in confusion emotion recognition systems. Future study directions for potential research are also suggested to overcome existing gaps.

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Confusion detection using cognitive ability tests

Cited by 2 publications

References 13 publications

Study on the human-brain confusion levels and corresponding EEG levels

Study on the human-brain confusion levels and corresponding EEG levels

A Systematic Review of Electroencephalography-Based Emotion Recognition of Confusion Using Artificial Intelligence

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