Practice and Exploration of Ideological and Political Education in Colleges and Universities in the Context of Big Data

• Published in: Applied mathematics and nonlinear sciences Vol. 9; no. 1
• Main Author: Jia, Xiaoxia
• Format: Journal Article
• Language: English
• Published: Beirut Sciendo 01.01.2024; De Gruyter Poland
• Subjects: 97M80; Big Data; Coefficient of variation; Colleges & universities; Distance learning; Education; Ideology; Learning interest model; Online learning; Student behavior sequence; Subject knowledge model
• ISSN: 2444-8656; 2444-8656
• DOI: 10.2478/amns-2024-3242

Big data is not only a technological innovation but also provides a new value and methodology, which provides new ideas and methods to enhance and improve the effect of education and makes the realization of personalization of ideological and political education possible. In this paper, based on the disciplinary knowledge model, we draw a knowledge structure map, organize the learning resource base, prepare disciplinary quizzes, and obtain effective student assessment data. By analyzing student assessment data, we construct a learner interest model to accurately locate the learners’ ideological and political learning needs. Modeling student behavior during video learning of ideological and political courses on the online course platform of universities. Quantify the quality of online learning from the perspective of learning participation time. Then, a learning behavior sequence is defined to describe ideological and political online learning behavior. Finally, two ideological and political education courses in the online learning platform of a university are analyzed for students’ learning behavior, and optimization suggestions for ideological and political education in colleges and universities are proposed based on the analysis results. The geometric means of play completion of the two ideological and political courses are 0.6391 and 0.7907, and the coefficients of variation of the courses are 0.1738 and 0.2136, which indicate that students’ play completion in the two ideological and political courses deviates from the mean value. The results have strong practical application value and provide a new perspective for carrying out personalized education of ideology and politics in colleges and universities in a digital environment.