Automated Content Grading Using Machine Learning
This addresses the time-consuming and biased manual grading process for educators, but it is incremental as it builds on existing machine learning techniques for text analysis.
The paper tackled the problem of automating the grading of theoretical exam answers in technical courses, achieving results through the implementation and comparison of machine learning models using text features like bag of words and semantic/lexical attributes.
Grading of examination papers is a hectic, time-labor intensive task and is often subjected to inefficiency and bias in checking. This research project is a primitive experiment in the automation of grading of theoretical answers written in exams by students in technical courses which yet had continued to be human graded. In this paper, we show how the algorithmic approach in machine learning can be used to automatically examine and grade theoretical content in exam answer papers. Bag of words, their vectors & centroids, and a few semantic and lexical text features have been used overall. Machine learning models have been implemented on datasets manually built from exams given by graduating students enrolled in technical courses. These models have been compared to show the effectiveness of each model.