Application of Lightweight Characteristic Residual Frame in Small Sample Score Prediction

The analysis and modeling of educational data are of great significance to the evaluation of teaching quality and personalized learning guidance. However, the acquisition of academic data is often limited by the costs of data collection and the actual teaching scenarios that occur. Challenges like limited data access, small samples, and data sparsity make small-sample analysis both unavoidable and a persistent challenge in educational research. This study integrates the multi-feature data of 296 students majoring in computer science from a university in Zhengzhou, China. It proposed a feature residual cascade prediction framework that integrates binning technology. Firstly, the unified feature space of multimodal feature fusion is constructed through feature filtering and feature generation. Secondly, a high-precision and high-efficiency prediction model is established by combining the random forest strategy with box division residual error correction, named ReBin (Residual-Binned Model). The experimental results show that the method achieves excellent predictive performance with R ²=0.99 under limited sample conditions, and the improved ReBin model does not generate additional computational burden in terms of execution efficiency. By constructing a comprehensive comparative study of the system, significant breakthroughs have been made in both prediction accuracy and computational efficiency. This further confirms that this study not only provides an effective solution for the analysis of small sample data in education, but also provides an innovative modeling framework for the prediction research of small sample data in other fields, which has important theoretical reference and application value.