Automatic Answer Assessment in LMS Using Latent Semantic Analysis

Thomas, Neethu; Kumar, Ashwini; Bijlani, Kamal

doi:10.1016/j.procs.2015.08.019

Cited by 9 publications

(2 citation statements)

References 4 publications

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“…From the raw data, LSA generates a term-document matrix, which lists terms in rows and documents in columns, with each cell indicating how frequently a term appears in this document. Here each row in the matrix represents the terms in the answer query and each column represents a document or the query [13]. If the term is present, then it is represented as 1 otherwise 0.…”

Section: B Similarity Based On Semanticsmentioning

confidence: 99%

Student Outcome Assessment on Structured Query Language using Rubrics and Automated Feedback Generation

Nayak,

Agarwal,

Khatri

et al. 2024

IJACSA

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Automated assessment of student assignment based on SQL(Structured Query Language) queries is an efficient method for evaluating and providing feedback on their DBMSrelated skills. This paper provides a three step approach of how student submissions are assessed automatically using various machine learning approaches and introduced an automated grading system for SQL(Structured Query Language) queries. ASQGS (Automated SQL Query Grading System) is the process of evaluating SQL queries submitted by students of a classroom. Due to the difficulties involved in the automatic grading procedure, this endeavor continues to attract the researcher's interest in developing a new and superior grading system. The purpose of this study is to demonstrate how text relevance is calculated between a reference query that the teacher sets and a query that the student submits. To compute the grade, the similarity value between the student and reference queries will be compared. In this paper various feature similarity techniques were discussed which is required before applying the machine learning model to automatically assess the grade of the student's SQL assignment. In the second step the grade received by the ASQG is used for student outcome assessment using rubrics with respect to Bloom's taxonomy and finally scores can be calculated using predefined rubrics criteria. Additionally, in the 3 rd step the system can generate feedback for students, highlighting specific areas of improvement, errors, or suggestions to enhance their queries among different groups of students segregated by their SQL knowledge.

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Section: B Similarity Based On Semanticsmentioning

confidence: 99%

Student Outcome Assessment on Structured Query Language using Rubrics and Automated Feedback Generation

Nayak,

Agarwal,

Khatri

et al. 2024

IJACSA

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show abstract

“…Researchers (Thomas et al, 2015), have also used LSA for automatic answer assessment and the proposed system assesses the descriptive answers by comparing it with the ideal answer using LSA, positional indexing and spell checking. A word-document matrix is created, where words are collected from the submitted student answers and student descriptive answer are considered as a document.…”

Section: Research Work Related To Lsa Techniquementioning

confidence: 99%

High Precision Latent Semantic Evaluation for Descriptive Answer Assessment

Kaur¹,

Kumar²

2018

Journal of Computer Science

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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.

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Automated Essay Evaluation Based on Fusion of Fuzzy Ontology and Latent Semantic Analysis

Darwish

Mohamed

2019

Advances in Intelligent Systems and Computing

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Automatic Answer Assessment in LMS Using Latent Semantic Analysis

Cited by 9 publications

References 4 publications

Student Outcome Assessment on Structured Query Language using Rubrics and Automated Feedback Generation

Student Outcome Assessment on Structured Query Language using Rubrics and Automated Feedback Generation

High Precision Latent Semantic Evaluation for Descriptive Answer Assessment

Automated Essay Evaluation Based on Fusion of Fuzzy Ontology and Latent Semantic Analysis

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