Design and Implementation of Machine Learning Algorithms for Automated Student Assignment Grading

Abstract

With the continuous advancement of educational digital transformation, automated student assignment grading systems have become indispensable for improving teaching efficiency and ensuring objective assessment in large-scale learning environments. As intelligent educational platforms increasingly rely on wireless communication infrastructures and electromagnetic information transmission for real-time data interaction and distributed computing, reliable automated grading algorithms play a critical role in supporting scalable digital education. To address the low efficiency and subjectivity of traditional manual grading, this study proposes a deep learning-based grading framework that integrates a grading-standard awareness mechanism with multi-dimensional feature extraction. By dynamically learning the semantic characteristics of grading criteria, the proposed algorithm accurately evaluates assignments across different subjects and educational stages while adapting to diverse instructional objectives. Experimental results obtained from 15,000 Chinese essays and 20,000 mathematical solution assignments demonstrate grading accuracies of 87.6% and 83.2%, respectively, representing improvements of 12–15 percentage points over existing approaches. A one-year pilot deployment in three middle schools reduced teachers’ grading time by an average of 62% and increased grading consistency to 91.5%, while scoring consistency under varying teaching objectives reached 89.3%. The results indicate that the dynamic grading-standard adaptation mechanism is the primary contributor to performance improvement and provides an effective technical framework for intelligent educational assessment systems operating over smart communication networks and electromagnetic information infrastructures.