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

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Bibliographic Details
Title: Student Outcome Assessment on Structured Query Language using Rubrics and Automated Feedback Generation
Authors: Nayak, Sidhidatri, Agarwal, Reshu, Khatri, Sunil Kumar, Mohammadian, Masoud
Source: Nayak, S, Agarwal, R, Khatri, S K & Mohammadian, M 2024, 'Student Outcome Assessment on Structured Query Language using Rubrics and Automated Feedback Generation', International Journal of Advanced Computer Science and Applications, vol. 15, no. 3, pp. 728-736. https://doi.org/10.14569/IJACSA.2024.0150374
Publication Year: 2024
Description: Automated assessment of student assignment based on SQL(Structured Query Language) queries is an efficient method for evaluating and providing feedback on their DBMS-related 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 3rd 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.
Document Type: article in journal/newspaper
File Description: application/pdf
Language: English
DOI: 10.14569/IJACSA.2024.0150374
Availability: https://researchprofiles.canberra.edu.au/en/publications/743219a5-4574-4cf0-933c-214cfd5dbb26
https://doi.org/10.14569/IJACSA.2024.0150374
https://researchsystem.canberra.edu.au/ws/files/93465599/Paper_74-Student_Outcome_Assessment_on_Structured_Query_Language.pdf
http://www.scopus.com/inward/record.url?scp=85189933548&partnerID=8YFLogxK
Rights: info:eu-repo/semantics/openAccess
Accession Number: edsbas.9ED55BAC
Database: BASE
Description
Abstract:Automated assessment of student assignment based on SQL(Structured Query Language) queries is an efficient method for evaluating and providing feedback on their DBMS-related 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 3rd 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.
DOI:10.14569/IJACSA.2024.0150374