Enhancing the Quality Evaluation of College Sports Informatization Teaching: A Blockchain and Neural Network Based Approach

Rezumat

In the era of the knowledge-based economy, international competitiveness increasingly depends on individuals' skillsets, placing higher education institutions, including colleges and universities (CAU), under significant pressure to enhance teaching quality and cultivate high-caliber talent. This challenge is particularly pertinent in sports education, where the integration of informatics and advanced technology can revolutionize teaching methodologies. To address these demands, this study proposes a novel teaching quality evaluation (TQE) method that integrates blockchain technology with a neural network algorithm to enhance the informatization of teaching evaluation in CAU. The study undertakes the following: (1) A comprehensive review of current TQE research, including informatization TQE in CAU, and an exploration of blockchain applications in the instructional environment; (2) The development of a TQE model based on blockchain technology and the establishment of a robust index system for evaluating informatization in teaching; (3) Introduction of the principles and improvements of the BP neural network (BPNN) algorithm to optimize the evaluation process. By selecting optimal model parameters and comparing test data with expert evaluations, the proposed model demonstrates high accuracy in evaluating teaching quality. Experimental results confirm that the blockchain-based TQE model effectively enhances teaching quality, offering practical applications for sports education. By fostering a data-driven, transparent, and adaptive evaluation process, this approach supports the cultivation of highly skilled sports professionals and aligns with the evolving demands of modern sports education.