High Precision Latent Semantic Evaluation for Descriptive Answer Assessment
Amarjeet Kaur and M. Sasi Kumar
DOI : 10.3844/jcssp.2018.1293.1302
Journal of Computer Science
Volume 14, Issue 10
This paper proposes an approach to evaluate student’s descriptive answers, using comparison-based approach in which student’s answer is compared with the standard answer. The standard answers contains domain specific knowledge as per the category (how, why, what, etc.) of questions asked in the examination. Several state-of-art claims that LSA correlates with the human assessor’s way of evaluation. With this as background, we investigated evaluation of students’ descriptive answer using Latent Semantic Analysis (LSA). In the course of research, it was discovered that standard LSA has limitations like: LSA research usually involves heterogeneous text (text from various domains) which may include irrelevant terms that are highly susceptible to noisy, missing and inconsistent data. We propose a new technique inspired by LSA, denoted as “High Precision Latent Semantic Evaluation” (HPLSE), LSA has been modified to overcome some of the limitations; this has also increased precision. By using the proposed technique (HPLSE), for the same datasets, average score difference and standard deviation between a human assessor and computer assessor has reduced and the Pearson correlation coefficient (r) has increased considerably. The new technique has been discussed and demonstrates on various problem classes.
© 2018 Amarjeet Kaur and M. Sasi Kumar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.