Research on Higher Education Evaluation and Decision-Making Based on Data Mining

Educational data mining is concerned with developing methods to explore the data from educational environments which provides insights that help in understanding the learning process and improving the educational outcomes. The evaluation and decision-making methods of higher education resources ignore the number of specific basic systems of resource evaluation and decision-making, resulting in the low accuracy of evaluation and decision-making. Therefore, a research on higher education evaluation and decision-making based on data mining is proposed. We analyze the application of big data in the field of higher education and design its optimal curriculum design model. We calculate the phased teaching task objectives of higher education curriculum, form its curriculum teaching guidance according to the influence degree between learners’ learning progress and learners’ thinking limitations, and obtain the learning effect produced by the optimal selection of curriculum teaching content. Then the probability of learners completing the structured teaching goal is calculated, so as to establish the optimal curriculum design model of higher education. Finally, we obtain the quantitative values of different experiences, extract the main influencing factors of resource evaluation and decision-making, and carry out higher education resource evaluation and decision-making analysis on this basis. The experimental results show that the research method improves the flexibility and universal applicability of higher education evaluation and decision-making, achieving an evaluation accuracy of above 90% and with below 7% error rate.