Research on teaching effect prediction and evaluation system of ideological and political courses combining artificial intelligence and big data technology

Abstract

This study aims to develop an AI-driven big data system for predicting and evaluating the effectiveness of ideological and political education courses, thereby enhancing teaching quality and personalized learning experiences. The methodology integrates data collection and processing, feature extraction, and machine learning algorithms, utilizing models such as Support Vector Machines (SVM), Random Forests, and Deep Neural Networks (DNN) to conduct multi-dimensional analysis and prediction of student behavior data, classroom interaction records, and feedback information. The system performs real-time assessments of teaching outcomes through machine learning models and generates customized feedback reports. Research findings demonstrate that the system excels in improving prediction accuracy and real-time responsiveness, while demonstrating strong adaptability and scalability. It provides data-driven support for ideological and political education courses, offering practical solutions for educational innovation.