Abstract
It aims to apply the neural network algorithm to the mining of educational resource data and provide new ideas for the intelligent development of teaching evaluation. The potential correlations between the teaching evalua-tion results and the teacher’s age, gender, professional title, and academic qualification are analyzed with the Apriori algorithm, which is improved with the decision tree based on the research of the existing university teaching evaluation system. The back propagation (BP) neural network model is improved based on the differential evolution algorithm (DEA). The DEA-BP model is applied to the prediction of teaching evaluation results for analysis. The results show that the execution time of the improved association rule algorithm (ARA) is significantly better than that of other models. In addition, the teacher’s age (40 - 50 years old or 50 - 60 years old), gender (female), professional title (senior or deputy senior), and academic qualifications (undergraduate or master) have certain correlation with the teaching evaluation results (excellent). When the DEA-BP algorithm is adopted to predict the teaching evaluation results, the average absolute error (1.05%) and the relative accuracy rate (95.44%) between its prediction value and the true value are optimal. Therefore, the ARA algorithm and DEA-BP algorithm based on the decision tree can intelligently extract the potential laws and knowledge in the teaching evaluation data, and provide support for teaching evaluation decisions. Thus, it exerts the role of promotion in the mining of educational resource data in universities and the intelligent development of decision-making systems
Highlights
The popularization of higher education is accelerating day by day with the continuous deepening of education reform, so the status of teaching level evaluation and its role in education reform have become increasingly important [1]
The prediction error of differential evolution algorithm (DEA)-back propagation (BP) model is compared with that of the Gradient Boosting Decision Tree (GBDT) algorithm [19] and the improved BP neural network model based on Particle Swarm Optimization (PSO-BP) algorithm [20]
The potential correlation in the teaching evaluation data is mined based on the decision tree and modified Apriori algorithm
Summary
The popularization of higher education is accelerating day by day with the continuous deepening of education reform, so the status of teaching level evaluation and its role in education reform have become increasingly important [1]. Teaching evaluation can play an important role in promoting the development of the college and ensuring the quality of talent training in college. The advantage of data mining technology is that it can realize indepth analysis through computer technology with the existing information. It can greatly reduce the use of manpower and material resources, and has important significance for college education managers to make important decisions [5]. Applying the neural network algorithms in the mining of educational resource data can provide new ideas for the intelligent development of teaching evaluation. The teaching evaluation intelligent decision-making system is optimized with the improved data mining algorithm based on the existing teaching evaluation systems of Xi’an International University. The results of this study are of great significance for promoting the intelligent development of teaching evaluation
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