A Ranked-Based Learning Approach to Automated Essay Scoring

• Published in: 2012 International Conference on Cloud and Green Computing pp. 448 - 455
• Main Authors: Hongbo Chen, Ben He, Tiejian Luo, Baobin Li
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2012
• Subjects: Automated essay scoring; Educational institutions; Feature extraction; Learning to rank; Linear regression; Machine learning; Machine learning algorithms; Support vector machines; Vectors
• ISBN: 1467330272; 9781467330275
• DOI: 10.1109/CGC.2012.41

Automated essay scoring is the computer techniques and algorithms that evaluate and score essays automatically. Compared with human rater, automated essay scoring has the advantage of fairness, less human resource cost and timely feedback. In previous work, automated essay scoring is regarded as a classification or regression problem. Machine learning techniques such as K-nearest-neighbor (KNN), multiple linear regression have been applied to solve this problem. In this paper, we regard this problem as a ranking problem and apply a new machine learning method, learning to rank, to solve this problem. We will introduce detailed steps about how to apply learning to rank to automated essay scoring, such as feature extraction, scoring. Experiments in this paper show that learning to rank outperforms other classical machine learning techniques in automated essay scoring.