Method of Cultivating College Students' Independent Learning Ability Based on Integration of Multiple Algorithm

Ying You

doi:10.4108/eetsis.4492

Authors

Ying You College of Tourism Kaifeng University, Kaifeng 475004, Henan, China

DOI:

https://doi.org/10.4108/eetsis.4492

Keywords:

college students' independent learning ability cultivation, multiple modes integration, blended learning model, butterfly optimization algorithm

Abstract

INTRODUCTION: The research on the multi-mode fusion of college students' independent learning ability cultivation method is conducive to college students' change of learning mode and learning thinking, improvement of the utilization rate of educational resources, and the development of the academic environment as well as the reform of the educational concept.

OBJECTIVES: Aiming at the problems of college students' current independent learning mode, such as the need for more in-depth research and the single study means.

METHODS: A method for cultivating college students' autonomous learning ability through the integration of intelligent optimization algorithms and multiple modes has been proposed. Firstly, the practices of analyzing the current college students' autonomous learning mode and multiple learning modes are analyzed; then, using the butterfly optimization algorithm, a weight optimization method for the cultivation of college students' independent learning ability based on the fusion of multiple modes is proposed; finally, the validity and robustness of the proposed method are verified through experimental analysis.

RESULTS: The results show that the proposed method has a high cultivation effect.

CONCLUSION: Solves the problem of fusion of college students' independent learning ability cultivation modes.

References

Ying M, Yong F . Primary Study on the Cultivation of College Students' Self-directed Learning Ability in Teaching PRESCRIPTIONS[J]. Jiangxi University of Traditional Chinese Medicine, 2017.

Kwak E, Woo J . Effects of Peer-Assisted Learning Applied to Nursing Education on Self-Directed Learning Ability, Academic Self-Efficacy and Academic Achievement of Nursing Students[J].Journal of Industrial Convergence, 2022.

Park J M, Seo H J, Kim S M, et al. Association between COVID-19-related stress and self-directed learning ability among Korean nursing students[J]. Nurse education in practice, 2023.

Qianna S . Evaluation model of classroom teaching quality based on improved RVM algorithm and knowledge recommendation[J].Journal of Intelligent and Fuzzy Systems, 2021, 40(2):2457-2467.

Yuan F, Nie Y . Online Classroom Teaching Quality Evaluation System Based on Facial Feature Recognition[J].Scientific Programming, 2021(Pt.14). 2021.

Hou J . Online teaching quality evaluation model based on support vector machine and decision tree[J].Journal of Intelligent and Fuzzy Systems, 2021, 40(2):2193-2203.

Qianna S . Evaluation model of classroom teaching quality based on improved RVM algorithm and knowledge recommendation[J].Journal of Intelligent and Fuzzy Systems, 2021, 40(2):2457-2467.

Lu C, He B, Zhang R . Evaluation of English interpretation teaching quality based on GA optimized RBF neural network[J]. Fuzzy Systems, 2021, 40(2):3185-3192.

Liang J . Problems and Solutions of Art Professional Service Rural Revitalization Strategy Based on Random Forest Algorithm[J].Wireless Communications and Mobile Computing, 2022, 2022(1):1-11.

Means B, Toyama Y, Murphy R F, et al. The Effectiveness of Online and Blended Learning: A Meta-Analysis of the Empirical Literature[J]. Teachers College Record, 2013,115(3):134-162.

Goodyear V, Dudley D. "I am a Facilitator of Learning!"Understanding What Teachers and Students Do Within Student-Centered Physical Education Models [J].Quest, 2015(3): 274-289.

Chen FF. Research and practice of hybrid teaching of digital electronics technology course based on rain classroom [D]. Guizhou Normal University,2019.

Guo Xiangyong, Fu Guoqiang. Constructing a comprehensive evaluation system of multimedia teaching quality by fuzzy mathematical method[J]. Research on Electrochemical Education, 2007, (3): 76 -80.

Lai Xiaoyun, Jiao Zhongming. Exploration and Implementation of Blended Teaching in Network Environment: A Case of Teaching Reform of Public Courses on Modern Educational Technology in Colleges and Universities[J]. Journal of Distance Education,2007(05):57-59.

Yang H. Effective computer teaching in the context of online and offline education [J]. Science and Technology Economy Guide, 2019, 27(34):158.

Liu Chunling, Zhang Huansheng, Hao Guofen. Application of BP neural network in teaching evaluation[J]. Coal Technology, 2012, 31(5): 239-240.

Mylvaganam T, Sassano M, Astolfi A. A Differential Game Approach to Multi-agent Collision Avoidance[J]. IEEE Transactions on Automatic Control, 2017, 62(8):4229-4235.

Cho S . Effects of Problem-solving Ability on Quality of Life and the Mediation Effect of Self-reliance Will of Participants in International Development Cooperation[J].The Journal of Humanities and Social Sciences 21, 2023.

Gama P H T , Oliveira H , Dos Santos J A ,et al. An overview of Meta-learning approaches for Few-shot Weakly-supervised Segmentation[J].Computers & amp; graphics, 2023.

Li Y, Hao Y, Zhao P, et al. Edge-enhanced Global Disentangled Graph Neural Network for Sequential Recommendation[J].ACM transactions on knowledge discovery from data, 2023.

Liu J , Ruan X , Zheng Y ,et al. LEARNING-ABILITY OF DISCRETE-TIME ITERATIVE LEARNING CONTROL SYSTEMS WITH FEEDFORWARD[J].SIAM Journal on Control and Optimization, 2023.

Abbasi S F, Jamil H, Chen W . Optimal Cooperative Spectrum Sensing Based on Butterfly Optimization Algorithm[J].Computers, Materials, and Continua (Tech et al.), 2022(1).