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Monitoring Cognitive Workload in Online Videos Learning Through an EEG-Based Brain-Computer Interface

  • Yun Zhou
  • Tao Xu
  • Yanping Cai
  • Xiaojun Wu
  • Bei Dong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10295)

Abstract

Student cognitive state is one of the crucial factors determing successful learning [1]. The research community related to education and computer science has developed various approches for describing and monitoring learning cognitive states. Assessing cognitive states in digital environment makes it possible to supply adaptive instruction and personalized learning for student. This assessment has the same function as the instructor in a real-world classroom observing and adjusting the speed and contents of the lecture in line with students’ cognitive states. The goal is to refocus students’ interest and engagement, making the instruction efficiently. In recent years, increased researches have focused on various measures of cognitive states, among which physiological measures are able to monitor in a real-time, especially electroencephalography (EEG) based brain activity measures. The cognitive workload that students experience while learning instructional materials determines success in learning. In this work, we design and propose a real-time passive Brain-Computer Interaction (BCI) system to monitor the cognitive workload using EEG-based headset Emotiv Epoc+, which is feasible for working in the online digital environment like Massive Open Online Courses (MOOCs). We choose two electrodes to pick up original EEG signals, which are highly relevant to the workload. The current prototype is able to record EEG signals and classify levels of cognitive load when students watching online course videos. This prototype is based on two layers, using machine learning approaches for classification.

Keywords

Electroencephalography (EEG) Passive brain-computer interface (BCI) Cognitive workload Learning 

Notes

Acknowledgements

Authors gratefully acknowledge the National Natural Science Foundation of China (Grant No. 11372167), the Key Science and Technology Innovation Team in Shaanxi Province, China (Grant No. 2014KTC-18), and the 111 Project (Grant No. B16031).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yun Zhou
    • 1
  • Tao Xu
    • 2
  • Yanping Cai
    • 3
  • Xiaojun Wu
    • 4
  • Bei Dong
    • 4
    • 5
  1. 1.School of EducationShaanxi Normal UniversityXi’anPeople’s Republic of China
  2. 2.School of Software and MicroelectronicsNorthwestern Polytechnical UniversityXi’anPeople’s Republic of China
  3. 3.School of Mechanical EngineeringXi’an Jiaotong UniversityXi’anPeople’s Republic of China
  4. 4.Key Laboratory of Modern Teaching Technology, Ministry of EducationShaanxi Normal UniversityXi’anPeople’s Republic of China
  5. 5.School of Computer ScienceShaanxi Normal UniversityXi’anPeople’s Republic of China

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