single-rb.php

JRM Vol.38 No.1 pp. 266-275
(2026)

Paper:

Case Report of a Trend Analysis of Students’ Knowledge and Ability Changes at the End of the Second Year of Study in the Faculty of Science and Engineering, Tokushima Bunri University

Jiro Morimoto ORCID Icon, Yoshikazu Yamamoto, Junji Kawata ORCID Icon, Yoshio Kaji ORCID Icon, Mineo Higuchi ORCID Icon, Hisanori Amano, and Shoichiro Fujisawa ORCID Icon

Tokushima Bunri University
8-53 Hamano-cho, Takamatsu, Kagawa 760-8542, Japan

Received:
June 27, 2025
Accepted:
October 8, 2025
Published:
February 20, 2026
Keywords:
questionnaire survey, polychoric correlation coefficient, multiple correspondence analysis
Abstract

The Department of Electronics and Information Engineering, Faculty of Science and Engineering, Tokushima Bunri University, offers a combination of lectures, exercises, and experiments in related subjects. This helps students to acquire expertise in computer hardware and software, as well as the ability to learn continuously and use their initiative in taking actions. At the end of each semester, questionnaires are conducted for all subjects to assess students’ level of understanding and the appropriateness of teaching methods. Instructors use this information to assess the status of each subject and strive to improve students’ understanding. In addition, to understand the trends and characteristics of the improvement in students’ abilities and understanding, this study investigates the situation at the end of the second year of study. A questionnaire survey is conducted to ascertain the status of improvement in knowledge and abilities. Based on the obtained response data, the polychoric correlation coefficient is calculated and the data are analyzed using multiple correspondence analysis.

Cite this article as:
J. Morimoto, Y. Yamamoto, J. Kawata, Y. Kaji, M. Higuchi, H. Amano, and S. Fujisawa, “Case Report of a Trend Analysis of Students’ Knowledge and Ability Changes at the End of the Second Year of Study in the Faculty of Science and Engineering, Tokushima Bunri University,” J. Robot. Mechatron., Vol.38 No.1, pp. 266-275, 2026.
Data files:
References
  1. [1] R. Alfanz, R. K. Hendrianto, and A. H. A. M. Siagian, “Predicting student performance through data mining: A case study in Sultan Ageng Tirtayasa University,” J. Adv. Comput. Intell. Intell. Inform., Vol.27, No.6, pp. 1159-1167, 2023. https://doi.org/10.20965/jaciii.2023.p1159
  2. [2] Y. Chen, “Early warning of college students’ ideological and political course performance using an optimization algorithm,” J. Adv. Comput. Intell. Intell. Inform., Vol.29, No.2, pp. 389-395, 2025. https://doi.org/10.20965/jaciii.2025.p0389
  3. [3] M. Sayed, “Student progression and dropout rates using convolutional neural network: A case study of the Arab Open University,” J. Adv. Comput. Intell. Intell. Inform., Vol.28, No.3, pp. 668-678, 2024. https://doi.org/10.20965/jaciii.2024.p0668
  4. [4] L. Tao, “Improving the quality of vocational education in higher vocational colleges based on deep learning technology: Student learning prediction and personalized recommendation,” J. Adv. Comput. Intell. Intell. Inform., Vol.29, No.2, pp. 407-416, 2025. https://doi.org/10.20965/jaciii.2025.p0407
  5. [5] H. L. Tien, N. P. Van, and T. Kato, “Development of evaluation criteria for training fire students to enable new rescue roles in Vietnam,” J. Disaster Res., Vol.19, No.2, pp. 411-419, 2024. https://doi.org/10.20965/jdr.2024.p0411
  6. [6] J.-W. Wu and T.-K. Tien-Liu, “Discussing psychological changes in college students who participate in physical education using structural equation modeling,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.3, pp. 307-315, 2020. https://doi.org/10.20965/jaciii.2020.p0307
  7. [7] Y. Chen, S. Lo, and J. Cheng, “The impact of field-flipped courses on college students’ self-regulated learning and learning performance take a national university in central Taiwan as an example,” J. Adv. Comput. Intell. Intell. Inform., Vol.27, No.2, pp. 281-291, 2023. https://doi.org/10.20965/jaciii.2023.p0281
  8. [8] S. Li, Y. Dai, K. Hirota, and Z. Zuo, “A students’ concentration evaluation algorithm based on facial attitude recognition via classroom surveillance video,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.7, pp. 891-899, 2020. https://doi.org/10.20965/jaciii.2020.p0891
  9. [9] S. Nishiguchi, Y. Kameda, K. Kakusho, and M. Minoh, “Automatic video recording of lecture’s audience with activity analysis and equalization of scale for students observation,” J. Adv. Comput. Intell. Intell. Inform., Vol.8, No.2, pp. 181-189, 2004. https://doi.org/10.20965/jaciii.2004.p0181
  10. [10] S. A. Rahok, H. Oneda, S. Osawa, and K. Ozaki, “Motivation system for students to learn control engineering and image processing,” J. Robot. Mechatron., Vol.31, No.3, pp. 405-411, 2019. https://doi.org/10.20965/jrm.2019.p0405
  11. [11] F. A. Bachtiar, K. Kamei, and E. W. Cooper, “A neural network model of students’ english abilities based on their affective factors in learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.16, No.3, pp. 375-380, 2012. https://doi.org/10.20965/jaciii.2012.p0375
  12. [12] L. Xiao, J. Zhang, J. She, and S. Chen, “Active learning based on manual skills for students in mechatronics course,” J. Adv. Comput. Intell. Intell. Inform., Vol.19, No.2, pp. 307-311, 2015. https://doi.org/10.20965/jaciii.2015.p0307
  13. [13] Y. Ohtsubo, A. Ikeda, K. Ioi, and M. Kosaka, “Undergraduate-student teaching materials for mechatronics,” J. Robot. Mechatron., Vol.29, No.6, pp. 1005-1013, 2017. https://doi.org/10.20965/jrm.2017.p1005
  14. [14] S. Muramatsu, D. Chugo, S. Yokota, and H. Hashimoto, “Student education utilizing the development of autonomous mobile robot for robot competition,” J. Robot. Mechatron., Vol.29, No.6, pp. 1025-1036, 2017. https://doi.org/10.20965/jrm.2017.p1025
  15. [15] J. Kawata, J. Morimoto, M. Higuchi, and S. Fujisawa, “The educational effects of practical manufacturing activities in graduation research,” J. Robot. Mechatron., Vol.31, No.3, pp. 391-404, 2019. https://doi.org/10.20965/jrm.2019.p0391
  16. [16] R. Shimanuki and S. Nakajima, “Joint education program between technical high school and university for technical high school student through developing robots,” J. Robot. Mechatron., Vol.23, No.5, pp. 840-849, 2011. https://doi.org/10.20965/jrm.2011.p0840
  17. [17] M. Yamamoto, “Machine design education to stimulate student imagination and originality at Department of Mechanical Engineering, Kyushu University,” J. Robot. Mechatron., Vol.10, No.1, pp. 40-46, 1998. https://doi.org/10.20965/jrm.1998.p0040
  18. [18] K. Nakatani, T. Doi, T. Wada, and T. Wada, “Promotion of self-growth of students by PBL-type manufacturing practice,” J. Robot. Mechatron., Vol.29, No.6, pp. 1037-1048, 2017. https://doi.org/10.20965/jrm.2017.p1037
  19. [19] J. Morimoto, I. Kobayashi, and H. Nakayama, “Design and introduction effects of PBL which assumed software developers,” J. of JSEE Vol.65, No.1, pp. 40-45, 2017 (in Japanese). https://doi.org/10.4307/jsee.65.1_40
  20. [20] X. Zhou, J. An, X. Zhao, and Y. Dong, “Using data mining on students’ learning features: A clustering approach for student classification,” J. Adv. Comput. Intell. Intell. Inform., Vol.20, No.7, pp. 1141-1146, 2016. https://doi.org/10.20965/jaciii.2016.p1141
  21. [21] K. Demura, T. Sakamoto, Y. Asano, and M. Matsuishi, “Enhancing student engineering, personal, and interpersonal skills through Yumekobo projects,” J. Robot. Mechatron., Vol.23, No.5, pp. 811-821, 2011. https://doi.org/10.20965/jrm.2011.p0811
  22. [22] S. Fujisawa, M. Ohhashi, and T. Hanabusa, “Self-evaluation of ability on “Innovation and creativity” common subjects in The University of Tokushima,” J. of JSEE, Vol.55, No.4, pp. 48-52, 2007.
  23. [23] B. L. Roux and H. Rouanet, “Multiple Correspondence Analysis,” 1st Ed., pp. 49-93, Ohmsha Ltd., 2021.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Feb. 19, 2026