본문 바로가기
  • Home

Relational Database SQL Test Auto-scoring System

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2019, 24(11), pp.127-133
  • DOI : 10.9708/jksci.2019.24.11.127
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : August 30, 2019
  • Accepted : November 15, 2019
  • Published : November 29, 2019

Tai-Sung Hur 1

1인하공업전문대학

Accredited

ABSTRACT

SQL is the most common language in data processing. Therefore, most of the colleges offer SQL in their curriculum. In this research, an auto scoring SQL test is proposed for the efficient results of SQL education. The system was treated with algorithms instead of using expensive DBMS(Data Base Management System) for automatic scoring, and satisfactory results were produced. For this system, the test question bank was established out of ‘personnel management’ and ‘academic management’. It provides users with different sets of test each time. Scoring was done by dividing tables into two sections. The one that does not change the table(select) and the other that actually changes the table(update, insert, delete). In the case of a search, the answer and response were executed at first and then the results were compared and processed, the user’s answers are evaluated by comparing the table with the correct answer. Modification, insertion, and deletion of table actually changes the data table, so data was restored by using ROLLBACK command. This system was implemented and tested 772 times on the 88 students in Computer Information Division of our college. The results of the implementation show that the average scoring time for a test consisting of 10 questions is 0.052 seconds, and the performance of this system is distinguished considering that multiple responses cannot be processed at the same time by a human grader, we want to develop a problem system that takes into account the difficulty of the problem into account near future.

Citation status

* References for papers published after 2023 are currently being built.