Abstract
Learning analytics applications exploit data about learners. In addition to cognitive and physical data about learners, recent research is motivated by the correlation between data on the physiological dimension and the cognitive performance. In order to collect physiological data, different types of physiological sensors can be used, among which the popularization of brain-computer interfaces using wearable EEG sensors has sparked the interest of research in the field of education. The affective, attentional or motivational state of an individual can be determined using physiological parameters. The research question to be investigated is: How accurate are the algorithms (e.g., classifying attentional states) implemented in wearable EEG devices in a learning setting? This paper proposes a concurrent validity approach to verify the accuracy of the algorithms integrated in EEG devices, i.e., comparing the classifying output against the response of EEG band power. For this purpose, a wearable EEG device of NeuroSky has been deployed and attention was taken as a physiological metric of interest. In order to investigate the accuracy of NeuroSky’s attention algorithms in the context of learning, an experiment was conducted with 23 subjects, with mean age 24.17 ± 3.68, who utilized a pedagogical agent to learn Java syntax while having their attention measured by the NeuroSky’s EEG device. The results of the experiment support the claim that the device does in fact represent a user’s attention accurately in a learning setting.
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Notes
- 1.
https://store.neurosky.com/pages/mindwave (Accessed on 3rd February 2020).
References
Aftanas, L., Golocheikine, S.: Human anterior and frontal midline theta and lower alpha reflect emotionally positive state and internalized attention: high-resolution EEG investigation of meditation (2001)
Banquet, J.-P., Sailhan, M.: Quantified EEG spectral analysis of sleep and Transcendental Meditation (1974)
Başar, E.: A review of alpha activity in integrative brain function: fundamental physiology, sensory coding, cognition and pathology (2012)
Bosch, N., et al.: Using video to automatically detect learner affect in computer-enabled classrooms (2016)
Bosch, N., D’Mello, S.: The affective experience of novice computer programmers. Int. J. Artif. Intell. Educ. 27(1), 181–206 (2015). https://doi.org/10.1007/s40593-015-0069-5
Crowley, K., et al.: Evaluating a brain-computer interface to categorise human emotional response (2010)
D’Mello, S.: Emotional learning analytics. In: Handbook of Learning Analytics (2017)
Fairclough, S.H.: Fundamentals of physiological computing (2009)
Farnsworth, B.: Top 14 EEG Hardware Companies [Ranked]
Fortenbacher, A., et al.: Sensor Based Adaptive Learning-Lessons Learned (2019)
Gould, I.C., et al.: Indexing the graded allocation of visuospatial attention using anticipatory alpha oscillations (2011)
Guich, S.M., et al.: Effect of attention on frontal distribution of delta activity and cerebral metabolic rate in schizophrenia (1989)
Harmony, T.: The functional significance of delta oscillations in cognitive processing (2013)
Herrmann, C.S., et al.: EEG oscillations: from correlation to causality (2016)
Herrmann, C.S., Knight, R.T.: Mechanisms of human attention: event-related potentials and oscillations (2001)
Hoppe, H.U.: Computational methods for the analysis of learning and knowledge building communities (2017)
Jensen, O., et al.: Temporal coding organized by coupled alpha and gamma oscillations prioritize visual processing (2014)
Knight, S., Buckinghamm Shum, S.: Theory and learning analytics (2017)
Linden, M., Habib, T., Radojevic, V.: A controlled study of the effects of EEG biofeedback on cognition and behavior of children with attention deficit disorder and learning disabilities. Biofeedback Self-Regulation 21, 297 (1996). https://doi.org/10.1007/BF02214740
Liu, N.-H., et al.: Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors (2013)
Maskeliunas, R., et al.: Consumer grade EEG devices: are they usable for control tasks? (2016)
NeuroSky Inc.: eSense(tm) Meters, ATTENTION eSense. http://developer.neurosky.com/docs/doku.php?id=esenses\%20tm
Norman, D., Shallice, T.: Attention to action: willed and automatic control of behavior (1986)
Oken, B.S., et al.: Vigilance, alertness, or sustained attention: physiological basis and measurement (2006)
Pope, A.T., Stephens, C.L., Gilleade, K.: Biocybernetic adaptation as biofeedback training method. In: Fairclough, S.H., Gilleade, K. (eds.) Advances in Physiological Computing. HIS, pp. 91–115. Springer, London (2014). https://doi.org/10.1007/978-1-4471-6392-3_5
Ray, W.J., Cole, H.W.: EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes (1985)
Raz, A., Buhle, J.: Typologies of attentional networks (2006)
Rebolledo-Mendez, G., et al.: Assessing NeuroSky’s usability to detect attention levels in an assessment exercise. In: Jacko, J.A. (ed.) HCI 2009. LNCS, vol. 5610, pp. 149–158. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02574-7_17
Renard, Y., et al.: OpenViBE: an open-source software platform to design, test, and use brain–computer interfaces in real and virtual environments (2010)
Sałabun, W.: Processing and spectral analysis of the raw EEG signal from the MindWave (2014)
Siemens, G., et al.: Open Learning Analytics: an integrated & modularized platform. Open University Press (2011)
Sinha, T., et al.: Your click decides your fate: Inferring information processing and attrition behavior from mooc video clickstream interactions (2014)
Classification of EEG signals in a brain-computer interface system. Institutt for Datateknikk og Informasjonsvitenskap (2011)
Diagnostic and Statistical Manual of Mental Disorders (1994)
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Bitner, R., Le, NT., Pinkwart, N. (2020). A Concurrent Validity Approach for EEG-Based Feature Classification Algorithms in Learning Analytics. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2020. Lecture Notes in Computer Science(), vol 12496. Springer, Cham. https://doi.org/10.1007/978-3-030-63007-2_44
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