Abstract
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.
Highlights
The current system of manual evaluation has some limitations due to which it becomes important to automate the descriptive answers evaluation
Available systems are useful for essay grading and short answer grading systems, but descriptive answer evaluation system is still an open research issue
LSA has been modified to overcome some of these issues/limitations and the proposed technique, denoted as ‘High Precision Latent Semantic Evaluation’ (HPLSE) has been used for automation of descriptive answer evaluation process, with much better results
Summary
The current system of manual evaluation has some limitations due to which it becomes important to automate the descriptive answers evaluation. LSA is a statistical natural language processing (NLP) method for inferring meaning from a text It was developed by researchers at Bellcore as an information retrieval technique (Deerwester et al, 1990) in the late 1980s. LSA has been modified to overcome some of these issues/limitations and the proposed technique, denoted as ‘High Precision Latent Semantic Evaluation’ (HPLSE) has been used for automation of descriptive answer evaluation process, with much better results. Answers) of the students in iteration, to increase the adequacy of the standard/reference answer This issue has been already raised in the introduction section of this report as it’s an important aspect of automation of descriptive answer evaluation process as well. Online tools that support managing of online assessments such as Moodle and Zoho are based on string matching technique for short answers but long answer evaluation is still handled manually by most of the systems. Some of the approaches are based on keyword matching, sequence matching, quantitative analysis, fuzzy system, rule based system which provides some solution for online assessment of answer sheets, but the general descriptive answer evaluation is still an open problem
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