Abstract
Brainwaves are the signals produced by the activities of nerve cells in the human brain. When recorded and shown on scientific instruments, they have the appearance of a wave, thus earning it its name. This study was conducted by wearing a non-invasive head-mounted brainwave detecting instrument to measure the attention level of students when attending different classes. Those results are then combined with the cognitive load scale to explore the correlation between the attention and the cognitive load of each student when attending different classes. According to the study results, a student attending English class will show a higher level of attention than one attending algorithm class. Furthermore, the learner showed lower cognitive load when reviewing previously learned content than when learning for the first time. However, the learner showed higher attention value when learning for the first time than when reviewing previously learned content.
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Kuo, Y.C., Chu, H.C., Tsai, M.C.: Effects of an integrated physiological signal-based attention-promoting and English listening system on students’ learning performance and behavioral patterns. Comput. Hum. Behav. 75, 218–227 (2017)
Campisi, P., La Rocca, D., Scarano, G.: EEG for automatic person recognition. Computer 45(7), 87–89 (2012)
Gregory, T.K., Pettus, D.C.: An electroencephalographic processing algorithm specifically intended for analysis of cerebral electrical activity. J. Clin. Monit. 2(3), 190–197 (1986)
Sanei, S., Chambers, J.A.: EEG Signal Processing. Wiley (2013)
Avery, M.: Preschool physical education: a practical approach. J. Phys. Educ. Recreation Dance 65(6), 37–39 (1994)
Hanslmayr, S., Gross, J., Klimesch, W., Shapiro, K.L.: The role of alpha oscillations in temporal attention. Brain Res. Rev. 67(1–2), 331–343 (2011)
Kuo, Y.-C., Chu, H.-C., Tsai, M.-C.: Effects of an integrated physiological signal-based attention-promoting and English listening system on students’ learning performance and behavioral patterns. Comput. Hum. Behav. 75, 218–227 (2017)
Ilgaz, H., Altun, A., Aşkar, P.: The effect of sustained attention level and contextual cueing on implicit memory performance for e-learning environments. Comput. Hum. Behav. 39, 1–7 (2014)
Mendoza, J.S., Pody, B.C., Lee, S., Kim, M., McDonough, I.M.: The effect of cellphones on attention and learning: the influences of time, distraction, and nomophobia. Comput. Hum. Behav. 86, 52–60 (2018)
Paas, F.G., Van Merriënboer, J.J.: Variability of worked examples and transfer of geometrical problem-solving skills: a cognitive-load approach. J. Educ. Psychol. 86(1), 122 (1994)
Feinberg, S., Murphy, M.: Applying cognitive load theory to the design of web-based instruction. In: Paper Presented at the Proceedings of IEEE Professional Communication Society International Professional Communication Conference and Proceedings of the 18th Annual ACM International Conference on Computer Documentation: Technology & Teamwork (2000)
Acknowledgements
This study is supported in part by Ministry of Science and Technology, Taiwan under Contract No. MOST-106-2511-S-218-001-MY3 and MOST-106-2511-S-006-001-MY3
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Cheng, SC., Cheng, YP., Huang, CH., Huang, YM. (2018). Exploring the Correlation Between Attention and Cognitive Load of Students When Attending Different Classes. In: Wu, TT., Huang, YM., Shadiev, R., Lin, L., Starčič, A. (eds) Innovative Technologies and Learning. ICITL 2018. Lecture Notes in Computer Science(), vol 11003. Springer, Cham. https://doi.org/10.1007/978-3-319-99737-7_21
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DOI: https://doi.org/10.1007/978-3-319-99737-7_21
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