Fourth Industrial Review (4차산업연구)

Korea Fourth Industrial Association (한국4차산업학회)
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A Study on the Development of Adaptive Learning System through EEG-based Learning Achievement Prediction

  • Jinwoo, KIM (Dept. of E-learning, Graduate School, Korea National Open University) ;
  • Hosung, WOO (Dept. of E-learning, Graduate School, Korea National Open University)
  • Received : 2023.02.14
  • Accepted : 2023.03.23
  • Published : 2023.03.31
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Abstract

Purpose - By designing a PEF(Personalized Education Feedback) system for real-time prediction of learning achievement and motivation through real-time EEG analysis of learners, this system provides some modules of a personalized adaptive learning system. By applying these modules to e-learning and offline learning, they motivate learners and improve the quality of learning progress and effective learning outcomes can be achieved for immersive self-directed learning Research design, data, and methodology - EEG data were collected simultaneously as the English test was given to the experimenters, and the correlation between the correct answer result and the EEG data was learned with a machine learning algorithm and the predictive model was evaluated.. Result - In model performance evaluation, both artificial neural networks(ANNs) and support vector machines(SVMs) showed high accuracy of more than 91%. Conclusion - This research provides some modules of personalized adaptive learning systems that can more efficiently complete by designing a PEF system for real-time learning achievement prediction and learning motivation through an adaptive learning system based on real-time EEG analysis of learners. The implication of this initial research is to verify hypothetical situations for the development of an adaptive learning system through EEG analysis-based learning achievement prediction.

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References

  1. Aci, C. I., Kaya, M., & Mishchenko, Y. (2019). Distinguishing mental attention states of humans via an EEG-based passive BCI using machine learning methods. Expert Systems with Applications, 134, 153-166. https://doi.org/10.1016/j.eswa.2019.05.057
  2. Al-Nafjan, A., & Aldayel, M. (2022). Predict Students' Attention in Online Learning Using EEG Data. Sustainability, 14(11), 6553. https://doi.org/10.3390/su14116553
  3. Bashir, F., Ali, A., Soomro, T. A., Marouf, M., Bilal, M., & Chowdhry, B. S. (2021). Electroencephalogram (EEG) Signals for Modern Educational Research. In Innovative Education Technologies for 21st Century Teaching and Learning (pp. 149-171). CRC Press.
  4. Bevilacqua, D., Davidesco, I., Wan, L., Chaloner, K., Rowland, J., Ding, M., ... & Dikker, S. (2019). Brain-to-brain synchrony and learning outcomes vary by student-teacher dynamics: Evidence from a real-world classroom electroencephalography study. Journal of cognitive neuroscience, 31(3), 401-411. https://doi.org/10.1162/jocn_a_01274
  5. Chen, C. M., & Wang, J. Y. (2018). Effects of online synchronous instruction with an attention monitoring and alarm mechanism on sustained attention and learning performance. Interactive Learning Environments, 26(4), 427-443. https://doi.org/10.1080/10494820.2017.1341938
  6. Koelstra, S., Muhl, C., Soleymani, M., Lee, J. S., Yazdani, A., Ebrahimi, T., ... & Patras, I. (2011). Deap: A database for emotion analysis; using physiological signals. IEEE transactions on affective computing, 3(1), 18-31. https://doi.org/10.1109/T-AFFC.2011.15
  7. Lee, H., Shin, D., & Shin, D. (2019). A research on the emotion classification and precision improvement of EEG (Electroencephalogram) data using machine learning algorithm. Journal of Internet Computing and Services, 20(5), 27-36. https://doi.org/10.7472/JKSII.2019.20.5.27
  8. Lim, W. L., Sourina, O., & Wang, L. P. (2018). STEW: Simultaneous task EEG workload data set. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(11), 2106-2114. https://doi.org/10.1109/TNSRE.2018.2872924
  9. Lin, F. R., & Kao, C. M. (2018). Mental effort detection using EEG data in E-learning contexts. Computers & Education, 122, 63-79. https://doi.org/10.1016/j.compedu.2018.03.020
  10. Ramirez-Moreno, M. A., Diaz-Padilla, M., Valenzuela-Gomez, K. D., Vargas-Martinez, A., Tudon-Martinez, J. C., Morales-Menendez, R., ... & Lozoya-Santos, J. D. J. (2021). Eeg-based tool for prediction of university students' cognitive performance in the classroom. Brain Sciences, 11(6), 698 https://doi.org/10.3390/brainsci11060698
  11. Vesin, B., Mangaroska, K., & Giannakos, M. (2018). Learning in smart environments: user-centered design and analytics of an adaptive learning system. Smart Learning Environments, 5, 1-21. https://doi.org/10.1186/s40561-017-0050-x
  12. Xu, J., & Zhong, B. (2018). Review on portable EEG technology in educational research. Computers in Human Behavior, 81, 340-349.