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Facilitating Classroom Orchestration Using EEG to Detect the Cognitive States of Learners

  • Zainab Mohamed
  • Mohamed El Halaby
  • Tamer Said
  • Doaa ShawkyEmail author
  • Ashraf Badawi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)

Abstract

Technology, when successfully integrated in a classroom environment can help redefine and facilitate the role of the teacher. Classroom orchestration is an approach to Technology Enhanced Learning that emphasizes attention to the challenges of classroom use of technology, with a particular focus on supporting teachers’ roles. The automatic detection of learners’ cognitive profiles is an important step towards adaptive learning, where the learning material are adapted to match that of the learners in order to enhance the learning outcome. Electroencephalogram (EEG) is a methodology that monitors the electric activity in the brain. It has been utilized in several applications including, for example, detecting the subject’s emotional and cognitive states. In this paper, an approach for detecting two basic cognitive skills that affect learning using EEG signals is proposed. These skills include focused attention and working memory. The proposed approach consists of the following main steps. First, subjects undergo a cognitive assessment test that stimulates and measures their full cognitive profiles while putting on a 14-channel wearable EEG headset. Second, only the scores of the two cognitive skills aforementioned above are extracted and used to encode the two targets for a classification problem. Third, the collected EEG data are analyzed and a number of time and frequency-domain features are extracted. Fourth, several classifiers were trained to be able to correctly classify and predict three levels (low, average, and high) of the measured cognitive skills. The classification accuracies that were obtained for the focused attention and working memory were 90% and 87%, respectively, which indicates the suitability of the proposed approach for the detection of these two skills. This could be used as a first step towards adaptive learning where adaptation is to be done according to the predicted levels of focused attention and working memory.

Keywords

Cognitive skills measurement Electroencephalography Short-time fourier transform Classification 

Notes

Acknowledgments

This research was supported by Information Technology Industry Development Agency (ITIDA) and Smart & Creative Solutions (SMACRS).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zainab Mohamed
    • 1
  • Mohamed El Halaby
    • 2
  • Tamer Said
    • 1
  • Doaa Shawky
    • 3
    Email author
  • Ashraf Badawi
    • 1
  1. 1.Center for Learning TechnologiesUniversity of Science and Technology, Zewail CityGizaEgypt
  2. 2.Mathematics Department, Faculty of ScienceCairo UniversityGizaEgypt
  3. 3.Engineering Mathematics Department, Faculty of EngineeringCairo UniversityGizaEgypt

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