Self-paced graph memory for learner GPA prediction and it's application in learner multiple evaluation.

A scientific and rational evaluation of teaching is essential for personalized learning. In the current teaching assessment model that solely relies on Grade Point Average (GPA), learners with different learning abilities may be classified as the same type of student. It is challenging to uncover the underlying logic behind different learning patterns when GPA scores are the same. To address the limitations of pure GPA evaluation, we propose a data-driven assessment strategy as a supplement to the current methodology. Firstly, we integrate self-paced learning and graph memory neural networks to develop a learning performance prediction model called the self-paced graph memory network. Secondly, inspired by outliers in linear regression, we use a t-test approach to identify those student samples whose loss values significantly differ from normal samples, indicating that these students have different inherent learning patterns/logic compared to the majority. We find that these learners' GPA levels are distributed across different levels. Through analyzing the learning process data of learners with the same GPA level, we find that our data-driven strategy effectively addresses the shortcomings of the GPA evaluation model. Furthermore, we validate the rationality of our method for student data modeling through protein classification experiments and student performance prediction experiments, it ensuring the rationality and effectiveness of our method.