Abstract
In general, at the universities the lecturers use the grades to evaluate the students’ performance. However, the grades do not show the accumulated knowledge. Thus, the students can’t understand whether they have studied enough to understand all the topics of the lecture. Recently, with the advancement of IoT technology and applications, we can record students’ study for every lecture using video images in all directions (360-degree). We also can display high quality image for students. During the lecture, by the response from the students’ smart phone, the students’ learning style was recorded in the database. However, the learning style jugged by the data received from the students’ smartphone had not a good accuracy. In this paper, in order to accurately judge the students’ learning style from big video data recorded in the database, we used a neural network.
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Yamamoto, N. (2020). Judging Students’ Learning Style from Big Video Data Using Neural Network. In: Barolli, L., Okada, Y., Amato, F. (eds) Advances in Internet, Data and Web Technologies. EIDWT 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 47. Springer, Cham. https://doi.org/10.1007/978-3-030-39746-3_1
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DOI: https://doi.org/10.1007/978-3-030-39746-3_1
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