Comprehensive Quality Evaluation Model of College Students Based on Deep Learning

Evaluating college students' quality in higher education institutions is significant for promoting enhancement and innovation in teaching and learning practices. Conventional evaluation strategies mainly depend on subjective evaluation, which limits their ability to capture the multifaceted characteristics of students. In this study, we developed a distinct deep learning (DL) architecture by combining the Convolutional Deep Belief Network and Red Fox Optimization (CDBN-RFO) to evaluate college students' quality. Initially, a database containing academic performance records, extracurricular activities, and other relevant information is collected and fed into the system. Then, the database was preprocessed to make it an appropriate format for subsequent analysis. Further, the proposed CDBN-RFO was created, and the CDBN was trained using the preprocessed database to understand the patterns and relations within data for evaluating college students' quality. Subsequently, the RFO approach was deployed to fine-tune the CDBN hyperparameters to their finest range, which increases the overall system performance. Subsequently, the RFO optimized the CDBN training process by finetuning its parameters to the optimal range, enhancing overall evaluation performance. The developed framework was modeled using Python software, and the results were analyzed, including accuracy, error, computational time, etc. Also, a comparative analysis was done with conventional algorithms, which validated that the proposed strategy outperformed them in accuracy.