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Classroom teaching is the basic form of teaching in colleges and universities. Teachers need to constantly optimize classroom teaching decision-making to adapt to the ever-changing educational environment. In order to improve the quality of teaching, stimulate students' potential, and promote students' all-round development, it is necessary to fully consider the personalized needs of students in classroom teaching and provide more suitable teaching content and methods for them. To this end, this article takes the accounting major in higher vocational colleges as an example, and conducts a study on the optimization of classroom teaching decision-making for students' personalized learning needs. It introduces the psychological curve function based on expected performance and current performance to measure the satisfaction degree of students' personalized learning needs, and as an evaluation of whether students' personalized learning needs meet expectations, elaborates on the calculation method of expected performance and current performance of personalized learning. A multi-objective decision-making model for classroom teaching is constructed to achieve the three optimization objectives of maximizing the quality of classroom teaching, maximizing the attention of students' personalized learning needs, and maximizing students' dissatisfaction, and the solution method of the model is given. Experimental results verify the effectiveness of the proposed algorithm and the constructed model. [ABSTRACT FROM AUTHOR]

Scale expansion of higher vocational education requires scientific instructions, therefore figuring out the spatial and temporal evolution laws of higher education is a meaningful work for realizing the scale expansion of quality-oriented higher vocational education. However, existing studies have neither discussed the effect of such instructions on the rewards of higher vocational education, nor considered the comprehensive influence of the promotive and instructive effect of market-oriented and society-oriented employment of high vocational education on its heterogeneous rewards. To fill in these research blanks, this paper studied the evolution of the scale of higher vocational education and the difference in educational opportunities based on the expansion effect. At first, this paper constructed a scale expansion effect model of higher vocational education to explore the reward mechanism of scale expansion of higher vocational education in the labor market. Then, the paper distinguished the different levels of higher vocational education, and analyzed the evolution mechanism of the unfairness degree of students or workers acquiring different-level higher vocational education opportunities during the scale expansion process of higher vocational education. At last, the experimental results and analysis results were given. [ABSTRACT FROM AUTHOR]

The classification and sharing of online course teaching cases based on students' individual learning needs can help teachers save time and cost in preparation of lessons and enrich their teaching content, and what is more, it is in line with the digitalization trend of the modern data-driven education. Currently, there has been few research on the combination of new information technology with teaching case classification, and the research is not adequate on the existing algorithms for classification of online course teaching cases. Therefore, this paper takes the teaching cases of online open courses of finance as the object, and studies the classification method for online course teaching cases based on an improved clustering method. First, the text clustering problem of finance teaching cases was described, and the conversion process of the abstracts of the case text and the evaluation method for the importance of words to teaching cases were given. Then, the key sentences were extracted from the text of the finance teaching cases, and the related algorithm was introduced. After that, the clustering flow chart for long texts of finance teaching cases was shown, and the principle of the combined clustering algorithm for finance teaching cases with uneven distribution of long texts and short texts was described in detail. The experimental results verified the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]