Linear Regression forReducing theBias of a Short Essay Scoring System
Abstract
This study is aimed to improve the performance of a short essay scoring system. The improvement is executed by integrating a simple linear regression to the output of a combined cosine similarity method (with weighted term frequency using Term Frequency –Inverse Document Frequency (TF-IDF) method) and term-matching mechanism.The linear regression is conducted by taking the short essay score (resulting from the combined cosine similarity and termmatching) as a regressor variable. In order to demonstrate the effectivenessof the proposedscoring system, the performance of the scoring system is measured relative to manual scoring by a lecturer.The results show that prior to linear regression, the scoring system tends to give higher score(biased score) compared to the manual score,which is problematic. The following scoring system with linear regression tackles this problem as the scoring bias is significantly reduced, that is, no tendency to givehigher or less scorecompared to the manual score.That the scoring bias is significantly reduced using a simple approach, linear regression,is expected to contribute in the acceleration of implementingautomatedessay scoring system on online learning technologiessuch as e-learning.
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