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    Area of Science:

    • Cognitive Science
    • Neuroscience
    • Educational Technology

    Background:

    • Confusion, though often viewed negatively, can be a beneficial affective state for learning.
    • Resolving confusion leads to deeper understanding and cognitive equilibrium restoration.
    • Quantifying and intervening in learning-induced confusion is a key research area.

    Purpose of the Study:

    • To explore the induction of confusion states in learners.
    • To investigate the feasibility of detecting confusion using electroencephalography (EEG).
    • To establish a foundation for an EEG-based Brain-Computer Interface (BCI) for monitoring and intervening in learning confusion.

    Main Methods:

    • EEG data were collected from 16 participants using an Emotiv headset.
    • Confusion was induced using Raven's Standard Progressive Matrices tests.
    • An end-to-end deep learning method was proposed for EEG analysis to detect confusion.

    Main Results:

    • The study achieved a 71.36% accuracy in classifying confused versus unconfused states.
    • This preliminary study shows promising results for EEG-based confusion detection.
    • The proposed deep learning approach demonstrated effectiveness in analyzing EEG data for affective states.

    Conclusions:

    • EEG can be utilized to detect confusion states during learning tasks.
    • The developed method provides a viable approach for monitoring learner confusion.
    • This research is a significant first step towards developing adaptive learning systems and BCIs.