On the assessment and reliability of political and ideological education in colleges using deep learning methods

Abstract

The reliability and effectiveness of teaching outcomes are reliant upon the accurate evaluation of ideological and political (IAP) education in colleges. This study focuses on predicting assessment scores to evaluate student performance, identify areas of vulnerability, and implement targeted interventions. Sophisticated deep learning techniques including artificial neural networks (ANN), convolutional neural networks (CNN), and support vector machines (SVM) were utilized to enhance the reliability of these evaluations. The results demonstrated clear distinctions between the training and test errors for the models. The ANN exhibited the highest errors, with a training RMSE (root mean squares error) of 14.13 and test RMSE of 13.55, indicating weak generalization. The CNN showed substantial improvement, with a training RMSE of 9.31 and test RMSE of 9.32, reflecting moderate but consistent performance. However, the SVM emerged as the most reliable model, achieving the lowest prediction errors: training RMSE of 7.68 and test RMSE of 8.0, with minimal discrepancies between training and test results. These findings provide valuable insights for instructors and policymakers to refine curriculum delivery, monitor student outcomes, and address educational disparities effectively. By adopting robust models like the SVM, institutions can ensure reliable predictions, fostering a more inclusive and outcome-oriented education system.