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

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Název: Student Outcome Assessment on Structured Query Language using Rubrics and Automated Feedback Generation
Autoři: Nayak, Sidhidatri, Agarwal, Reshu, Khatri, Sunil Kumar, Mohammadian, Masoud
Zdroj: 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
Rok vydání: 2024
Popis: 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.
Druh dokumentu: article in journal/newspaper
Popis souboru: application/pdf
Jazyk: English
DOI: 10.14569/IJACSA.2024.0150374
Dostupnost: 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
Přístupové číslo: edsbas.9ED55BAC
Databáze: BASE
Popis
Abstrakt: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