Research on Development Strategies for Empowering Ideological and Political Education in Higher Education Institutions Through Algorithmic Recommendation Technology
DOI:
https://doi.org/10.70393/616a736d.343030ARK:
https://n2t.net/ark:/40704/AJSM.v4n2a01Disciplines:
EducationSubjects:
Higher Education StudiesReferences:
7Keywords:
Algorithmic Recommendation, Ideological and Political Education, University Students, Development StrategyAbstract
Abstract: As the primary arena for conducting and implementing ideological and political education, universities must adapt to the tide of development in the digital age.Algorithm recommendation technology, as a vital component of emerging digital-age technologies, holds significant importance for advancing the high-quality development of ideological and political education in higher education institutions. It is essential to actively explore the theoretical basis and practical foundation for leveraging algorithm recommendation technology to empower ideological and political education, while clarifying its critical value in this context. Concurrently, development strategies should be strengthened across three dimensions—technology, stakeholders, and institutional frameworks—to enhance the educational effectiveness of ideological and political education in higher education institutions.
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[1] Marx, K., & Engels, F. (2012). Selected works of Marx and Engels: Volume 1 (pp. 141–142). People's Publishing House.
[2] Xi, J. P. (2023, May). Accelerate the construction of an education powerhouse to provide strong support for the great rejuvenation of the Chinese nation. People’s Daily, (01).
[3] Vilakone, P., et al. (2018). An efficient movie recommendation algorithm based on improved k-clique. Human-Centric Computing and Information Sciences, 8(1), 1.
[4] People’s Daily. (2022, October). Hold high the great banner of socialism with Chinese characteristics and strive in unity to build a modern socialist country in all respects. People’s Daily, (01).
[5] Jeong, W. H., et al. (2013). Performance improvement of a movie recommendation system based on personal propensity and secure collaborative filtering. Journal of Information Processing Systems, 9(1), 158.
[6] Cohn, J. (2019). The burden of choice: Recommendations, subversion, and algorithmic culture. Rutgers University Press.
[7] Milano, S., Taddeo, M., & Floridi, L. (2020). Recommender systems and their ethical challenges. AI & Society, 35(4), 1–11.
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