IPSJ SIG Technical Report Vol.2016-CE-137 No /12/ e β /α α β β / α A judgment method of difficulty of task for a learner using simple

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1 e β /α α β β / α A judgment method of difficulty of task for a learner using simple electroencephalograph Katsuyuki Umezawa 1 Takashi Ishida 2 Tomohiko Saito 3 Makoto Nakazawa 4 Shigeichi Hirasawa 5 Abstract: There are many causes of failure in study such as quality and difficulty of learning content, and learning proficiency. It would be possible to detect such causes by measuring browsing history, edit history, and biological information such as brain wave or eye tracking information. If the different brain waves depending on degree of difficulty of a task can be measured, the degree of difficulty of a task at the time of e-learning can be changed dynamically according to the brain waves. And we can expect to be able to get the most suitable learning effect to each student. In this study, we use a task as typing practice capable of setting of an easy task and a difficult task is used. We confirm that the β/α will become high-value if the difficult task is given, which is being talked about by a previous study, and consider about combination of the α wave and the β wave we observe more than one kinds by high and low of the frequency. Then, we show that the value of (low β wave) / (low α wave) represents the degree of difficulty of the task best. 1. Web [1] e- 1, Shonan Institute of Technology, Fujisawa, Kanagawa , Japan 2, Takasaki City University of Economics, Takasaki, Gunma , Japan 3, Tokyo City University, Setagaya, Tokyo , Japan 4, Junior College of Aizu, Aizuwakamatsu, Fukushima , Japan 5, Waseda University, Shinjuku, Tokyo , Japan Learning [2] [3] [6] [7][10] [4][5][8][9] c 2016 Information Processing Society of Japan 1

2 1 α β β/α β/α [14] β /α α β e α β β Giannitrapani [11] β β α β α β α β [12][13] α β β/α β/α [14] γ [15] γ θ 2 (θ +α )/10 γ (θ +α )/(10 γ ) [16] Table 1 1 [18] The kind of brain waves which can be acquired 3. (Hz) δ θ α (α l ) α (α h ) β (β l ) β (β h ) γ γ NeuroSky MindWave R Mobile 1 ThinkGear Connector Bluetooth ThinkGear Connector TCP/IP ThinkGear Connector NeuroSky MindWave Mobile [17] c 2016 Information Processing Society of Japan 2

Table 2 2 Brain waves at typing practice of basic cource Fig. 2 2 Experimenting high school student ID β l /α l β h /α h β l /α h β h /α l β l+h /α l+h 1 1.353 1.789 1.496 1.388 0.964 2 0.764 0.817 0.

3 Table 2 2 Brain waves at typing practice of basic cource Fig. 2 2 Experimenting high school student ID β l /α l β h /α h β l /α h β h /α l β l+h /α l+h *1 β l /α l α β [12][13] β/α 1 α β 2 α β β/α β l /α l β h /α h β l /α h β h /α l 4 (β l +β h )/(α l +α h )( β l+h /α l+h ) 5 β/α 2 3 β/α / β/α β/α *1 4 β l /α l β l /α l ID2 ID5 ID6 3 Table 3 Brain waves at typing practice of advanced cource ID β l /α l β h /α h β l /α h β h /α l β l+h /α l+h / Table 4 Value of advanced / basic ID β l /α l β h /α h β l /α h β h /α l β l+h /α l+h β h /α l ID β/α α β c 2016 Information Processing Society of Japan 3

4 3 ID2 β l /α l Fig. 3 The value of β l wave/α l wave according to the degree of difficulty at the time of typing practice by ID2 4 ID5 β l /α l Fig. 4 The value of β l wave/α l wave according to the degree of difficulty at the time of typing practice by ID5 5 ID6 β l /α l Fig. 5 The value of β l wave/α l wave according to the degree of difficulty at the time of typing practice by ID6 c 2016 Information Processing Society of Japan 4

5 5 Table 5 Required time for typing practice ID ( ) ( ) ( ) Table 6 Crrelation between Table 4 and Table 5 6 ( β l /α l )/( β l /α l ) Fig. 6 Reletion between required time difference and (advanced β l /α l ) / (basic β l /α l ) β l /α l β h /α h β l /α h β h /α l β l+h /α l+h β l /α l 0.6 ( β l /α l )/( β l /α l ) 6 ( β l /α l )/( β l /α l ) 7 6. β /α α β β / α 7 Fig. 7 ( β l /α l )/( β l /α l ) Reletion between required time for advanced mode and (advanced β l /α l ) / (basic β l /α l ) (C) 16K00491 MindWave [1] Web,, ET, vol.108(470), p.p.7 12, (2009). c 2016 Information Processing Society of Japan 5

6 [2] e-learning, ICT 14 1, p.p.31 35, ( ). [3],, (JASMIN) 2013, pp ( ). [4] Learning Analytics C 78 pp (2016.3) [5] Learning Analytics Scratch 78 pp (2016.3) [6] Learning Analytics 78 pp (2016.3) [7],,,, Learning Analytics 78 pp (2016.3) [8] Scratch 2015 ( ) [9] 2015 ( ) [10],,,, 2015, pp11-12, ( ) [11] D. Giannitrapani, The role of 13-hz activity in mentation, The EEG of Mental Activities, p.p , (1988). [12],,,,,,,, :, 10(2), p.p , (2008.5) [13] K. Yoshida, Y. Sakamoto, I. Miyaji, K. Yamada, Analysis comparison of brain waves at the learning status by simple electroencephalography, KES 2012, Proceedings, Knowledge-Based Intelligent Information and Engineering Systems, p.p , (2012). [14],,,,,. ET, 112(224), p.p , ( ) [15],,,, (DICOMO2013), p.p , ( ) [16],,,, (DICOMO2014), p.p , ( ) [17] (JASMIN) 2016, D2-1, (2016.9) [18] ThinkGear Serial Stream Guide, id=thinkgear communications protocol c 2016 Information Processing Society of Japan 6

IPSJ SIG Technical Report Vol.2017-CE-138 No /2/ e β / α A study on transition of the brain waves when executing a task using simpl

IPSJ SIG Technical Report Vol.2017-CE-138 No /2/ e β / α A study on transition of the brain waves when executing a task using simpl 1 2 3 4 5 e β / α A study on transition of the brain waves when executing a task using simple electroencephalograph Katsuyuki Umezawa 1 Takashi Ishida 2 Tomohiko Saito 3 Makoto Nakazawa 4 Shigeichi Hirasawa