Search

Research on Teaching Reform of Machine Learning Courses for Cultivating Engineering Innovation Capabilities

Author(s)

Xinan Chen, Jianzhong Li

Affiliation(s)

School of Computer Information Engineering, Hanshan Normal University, Chaozhou, Guangdong, China

Abstract

The rapid evolution of artificial intelligence technologies and their deep integration with industry, university machine learning courses face entirely new demands for capability cultivation. Addressing the status quo where traditional teaching models lag behind the goals of training engineering innovation talents in terms of conceptual updates, content adaptation, and evaluation mechanisms, this study to address critical deficiencies—such as the disconnect between knowledge transmission and engineering practice, and the scarcity of student innovation capabilities. We constructed and implemented a "One Body, Two Wings" teaching reform scheme. This framework takes the systemic reconstruction of the Course Kernel (The Body) as the main component, enhancing teaching quality and efficiency in the teaching process through value reshaping, paradigm innovation, and the integration of application scenarios. It utilizes the collaborative support of Prerequisite and Parallel Courses (The Left Wing) to solidify mathematical foundations and create interdisciplinary practical contexts. Furthermore, it establishes a Subsequent Achievement Incubation Channel (The Right Wing) to build a diversified evaluation system and a value-added chain for capabilities. This framework constructs a complete cultivation ecosystem ranging from knowledge input and capability internalization to value output. Tracking data from two rounds of teaching practice indicate that this reform has achieved significant effectiveness in improving academic achievement, strengthening engineering practice capabilities, and cultivating comprehensive literacy, providing a referential practical paradigm for the construction of similar courses under the background of "New Engineering" education.

Keywords

Machine Learning; Engineering Innovation Capability; One Body, Two Wings; Teaching Reform; New Engineering Education

References

[1] Cong, S., Bao, P., Yuan, S., Ye, X., Liang, H. (2022). Exploration and practice of collaborative innovation education mode in integration of science-education: Taking “Machine Learning” course as an example. Theory and Practice of Innovation and Entrepreneurship, 11(6), 7–10. [2] Chu, X., Wang, J., & Ding, X. (2024). Exploration of teaching reform for "Foundation of Machine Learning" course based on OBE concept. Science and Education Guide, 2024(3), 88–90. [3] Zhu, H., Liang, S., He, F., Li, H., & Xia, H. (2022). Exploration of machine learning course teaching based on project practice in the context of new engineering. Guangxi Physics, 43(2), 146–154. [4] Jiang, L., Zhang, L., Yan, J., & Wan, J. (2020). Teaching reform methods of machine learning’s open practical course for undergraduate education. Education Modernization, 7(56), 76–79. [5] Yin, X., & Qian, Y. (2025). Teaching reform application practice of machine learning practice course driven by large model. Software Guide, 24(3), 206–210. [6] Wei, N., Yin, L., Ning, H., & Fang, B. (2022). Preliminary study on the reform of machine learning teaching. Chinese Journal of Network and Information Security, 8(4), 182–189. [7] Sun, J., Liu, S., Pang, C., Lei, X., & Sun, Z. (2024). Machine learning teaching reform for cultivating practice and innovation abilities. Theory and Practice of Innovation and Entrepreneurship, 12(6), 39–43. [8] Kang, Y., Wang, H., Tian, S., & Ren, K. (2023). Research on teaching and practice reform of machine learning course in ethnic colleges. Education and Teaching Forum, 2023(51), 89–92. [9] Liu, J., Wang, X., & Yuan, X. (2024). Exploration of teaching reform in postgraduate “Machine Learning” course based on CDIO concept. Science and Education Guide, 2024(19), 135–137. [10]Zhang, Z., He, W., & Wei, Y. (2025). LLM-driven tripartite curriculum reformation: Role, ecology, and personalized empowerment—Evidence from Introduction to Machine Learning. Science and Education Guide, 2025(18), 7–9.