Multi-Source Data-Driven Network Teaching Quality Evaluation System of Police Colleges

Traditional teaching quality evaluation lacks comprehensive study; this paper constructed an evaluation index system of network teaching quality based on multi-source data. Against the background of multi-source data driving, the network teaching quality evaluation index system is constructed, which consists of five indexes: teaching platform function, teaching technology support, teaching service guarantee, teaching process, and teaching feedback. Based on probabilistic hesitation fuzzy sets and evidential reasoning, a network teaching quality evaluation model is established, and probabilistic hesitation fuzzy sets are used to describe the mixed assessment information uniformly. Probabilistic hesitation, fuzzy entropy, and expert weighting are used to assign weights to evaluation indexes. The example verification demonstrated that combining evidential reasoning with utility theory can effectively assess the quality of network teaching. The evaluation results are ranked, and the comparison indicates that this method enhances the accuracy of teaching quality evaluation.