Evaluation of Blended Teaching Effectiveness Based on BP Neural Network

To enhance the efficacy of blended English teaching, a proposed methodology leverages big data analysis for evaluating its effectiveness. The approach involves employing the parallel K-means clustering algorithm, integrated with adaptive cuckoo search, to cluster relevant data pertaining to blended online and offline English teaching. This method aims to provide robust support for assessing and improving the quality of blended English instruction through sophisticated data analysis techniques. Based on the clustering obtained teaching data, an evaluation index system for blended English teaching is designed, and a teaching effectiveness evaluation model based on improved BP neural network is constructed to complete the evaluation of blended English teaching effectiveness. Based on the evaluation results, targeted measures are taken to enhance the effectiveness of blended English teaching and improve students' English grades. The results obtained from the experiments underscore the positive impact of employing big data analysis in assessing the efficacy of blended online and offline English teaching. The implementation of enhanced teaching methodologies has led to a substantial improvement in the English test scores of students majoring in English at the institution. Over a series of 12 consecutive English tests, both oral and written scores exhibited remarkable consistency and demonstrated significant increases. The score trends displayed minimal fluctuations throughout the 12 assessments, with the overall oral and written test scores for the experimental class consistently reaching 85 points or higher. Remarkably, even in the regular class setting, the adoption of a blended teaching approach resulted in English oral and written test scores exceeding 80 points. in summary, BP neural network is highly effective in evaluating the effectiveness of blended English teaching and is a reliable and practical tool in the teaching evaluation process.