Facilitating Classroom Orchestration Using EEG to Detect the Cognitive States of Learners
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.
KeywordsCognitive skills measurement Electroencephalography Short-time fourier transform Classification
This research was supported by Information Technology Industry Development Agency (ITIDA) and Smart & Creative Solutions (SMACRS).
- 1.Badawi, A., Shawky, D.: The need for a paradigm shift in CSCL tools. In: Computing Conference, pp. 1131–1135. IEEE (2017)Google Scholar
- 2.Shawky, D., Badawi, A.: Reinforcement learning-based adaptive learning system. In: 3rd International Conference on Advanced Machine Learning Technologies and Applications (2018)Google Scholar
- 3.Kearsley, G., Shneiderman, B.: Engagement theory: a framework for technology-based teaching and learning. Educ. Technol. 38(5), 20–23 (1998)Google Scholar
- 6.Shawky, D., Badawi, A., Said, T., Hozayin, R.: Affordances of computer-supported collaborative learning platforms: a systematic review. In: International Conference on Interactive Collaborative Learning (ICL), pp. 633–651. IEEE (2014)Google Scholar
- 7.Said, T., Shawky, D., Badawi, A.: Identifying knowledge-building phases in computer supported collaborative learning: a review. In: International Conference on Interactive Collaborative Learning (ICL), pp. 608–614. IEEE (2015)Google Scholar
- 8.Fahmy, A., Said, Y., Shawky, D., Badawi, A.: Collaborate-it: a tool for promoting knowledge building in face-to-face collaborative learning. In: 15th International Conference on Information Technology Based Higher Education and Training (ITHET), pp. 1–6. IEEE (2016)Google Scholar
- 9.Savelainen, A.: An introduction to EEG artifacts. In: Independent Research Projects in Applied Mathematics (2010)Google Scholar
- 13.Mohammadpour, M., Mozaffari, S.: Classification of EEG-based attention for brain computer interface. In: 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS). IEEE (2017)Google Scholar
- 14.Ghosh, P., Mazumder, A., Bhattacharyya, S., Tibarewala, D.N.: An EEG study on working memory and cognition. In: Proceedings of the 2nd International Conference on Perception and Machine Intelligence, pp. 21–26. ACM (2015)Google Scholar
- 18.Lascari, L.D., Pera, R.J.: Wireless radio system optimization by persistent spectrum analysis. Google Patents (2015)Google Scholar
- 19.Djamal, E.C., Pangestu, D.P., Dewi, D.A.: EEG-based recognition of attention state using wavelet and support vector machine. In: 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA), pp. 139–144. IEEE (2016)Google Scholar