Optimization and implementation of management technology integrated with data analysis for college students' course evaluation and academic early warning

Educational managers need curriculum evaluation results and academic warnings to enrich educational management content. The study integrates data analysis technology into education and teaching, and uses association rules to mine the internal relationship of each dimension element of course evaluation. The results show that the performance of the Apriori algorithm with interest degree is better, and it can reduce 15 wrong rules. The results of association rules generation show that teaching design should pay attention to the construction of network resources, and the reform of teaching content needs the promotion of high-quality teachers. The academic early warning model uses the GA-BP model to predict grades, and then formulates an early warning index based on the grades. The results show that the average accuracy rate of the prediction model is 89.12 %, which is better than other models, and the prediction accuracy rate of the potential early warning student group is >76.1 %. Compared with the final grades, the fitting degree of the prediction experiment results reaches 97.3 %, which shows that the performance of the model meets the needs of academic early warning.