Automatic Grading of Portuguese Short Answers Using a Machine Learning Approach
Lucas Busatta Galhardi, Rodrigo Clemente Thom de Souza, Jacques Duílio Brancher
Abstract
Short answers are routinely used in learning environments for students’ assessment. Despite its importance, teachers find the task of assessing discursive answers very time-consuming. Aiming at assisting in this problem, this work explores the Automatic Short Answer Grading (ASAG) field using a machine learning approach. The literature was reviewed and 44 papers using different techniques were analyzed considering many aspects. A Portuguese dataset was build with more than 7000 short answers. Different approaches were experimented and a final model was created with their combination. The model’s effectiveness showed to be satisfactory, with kappa scores indicating moderate/substantial agreement between the model and human grading.