Design and Implementation of Machine Learning Algorithms in Automatic Grading of Students' Assignments

Abstract

This study introduces machine learning methods for automatically grading students' assignments, catering to the changing demands of education in the digital age. By utilizing diverse techniques, the aim is to simplify the grading process, give prompt feedback, and improve overall efficiency in assessment practices. The research delves into the different types of assignments such as written responses and coding tasks, and presents a range of ML algorithms including NB, Decision Trees (DT), RF, SVM, Linear Regression (LR), KNN, and Ensemble Methods (EMs). The implementation involves extracting relevant features from assignments, preprocessing data to handle noise and outliers, and training models on a varied set of examples. The grading system prioritizes interpretability, accuracy, and efficiency while aligning with educational objectives and grading policies. Evaluation is done using suitable metrics like recall, accuracy, precision, F1 score for each algorithm. The study contributes to the progress of automated grading systems by providing valuable insights for educators on the potential impact of machine learning in enhancing assessment processes. Additionally, the designed algorithms demonstrate adaptability for different types of assignments and serve as a foundation for future enhancements in grading precision and efficiency.