Strengthen the Foundation and Practice Orientation: Exploration of Teaching Reform in Machine Learning Courses
DOI:
https://doi.org/10.54097/a4xeav89Keywords:
Machine Learning; Practice; Teaching Reform.Abstract
Machine learning has become a core technology in the field of artificial intelligence with its excellent performance and wide range of applications. Traditional machine learning course teaching models often focus on theoretical explanations and lack sufficient practical guidance, making it difficult for students to apply their knowledge to solve practical problems. This article explores feasible ways to reform the teaching of machine learning courses, by strengthening the close integration of basic knowledge and practical skills, as well as optimizing course evaluation methods, significantly improving students' learning effectiveness and practical application abilities.
Downloads
References
Simard, Patrice Y., et al. "Machine Teaching: A New Paradigm for Building Machine Learning Systems." ArXiv, 2017, /abs/ 1707. 06742. Accessed 3 Aug. 2024.
Shieh R S, Chang W. Fostering student’s creative and problem-solving skills through a hands-on activity[J]. Journal of Baltic Science Education, 2014, 13(5): 650.
Karan E, Brown L. Enhancing Student's Problem-Solving Skills through Project-Based Learning[J]. Journal of Problem Based Learning in Higher Education, 2022, 10(1): 74-87.
Prianto A, Qomariyah U N, Firman F. Does student involvement in practical learning strengthen deeper learning competencies? [J]. International Journal of Learning, Teaching and Educational Research, 2022, 21(2): 211-231.
Hang L T, Van V H. Building Strong Teaching and Learning Strategies through Teaching Innovations and Learners' Creativity: A Study of Vietnam Universities[J]. International Journal of Education and Practice, 2020, 8(3): 498-510.
Tashtoush M A, Wardat Y, Aloufi F, et al. The effectiveness of teaching method based on the components of concept-rich instruction approach in students achievement on linear algebra course and their attitudes towards mathematics[J]. Journal of Higher Education Theory and Practice, 2022, 22(7).
Dakhi O, JAMA J, IRFAN D. Blended learning: a 21st century learning model at college[J]. International Journal of Multi Science, 2020, 1(08): 50-65.
Zhai X, Yin Y, Pellegrino J W, et al. Applying machine learning in science assessment: a systematic review[J]. Studies in Science Education, 2020, 56(1): 111-151.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Academic Journal of Science and Technology

This work is licensed under a Creative Commons Attribution 4.0 International License.








