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

Sidhidatri Nayak, Reshu Agarwal, Sunil Kumar Khatri, Masoud Mohammadian

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)728-736
Number of pages9
JournalInternational Journal of Advanced Computer Science and Applications
Volume15
Issue number3
Publication statusPublished - Mar 2024

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