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IJIET 2018 Vol.8(8): 538-545 ISSN: 2010-3689
doi: 10.18178/ijiet.2018.8.8.1096

Automated Scoring System for Multiple Choice Test with Quick Feedback

M. Alomran and D. Chai

Abstract—Although automatic scoring systems for multiple choice questions already exist, they are still restrictive and use specialised and expensive tools. In this paper, an automated scoring system is proposed to reduce the cost and processing restrictions by taking advantage of image processing technology. The proposed method enables the user to print the answer sheets and subsequently scan them by an off-the-shelf scanner. In addition, a personal computer can process all the scanned sheets automatically. After scoring, the proposed system annotates the sheets with feedback and send them back to students via email. Moreover, two novel features are introduced. The first feature is the handwriting recognition method to recognize student ID. We called this the segmented handwritten character recognition. This new method replaces the conventional student ID recognition commonly known as the Matrix Identifier. The second feature is our specially designed answer sheet that allows students to easily change their answers with multiple attempts. As a result, there is no need to erase pencil shading or change the entire answer sheet if any mistake happened during the test. The proposed system is designed to be cheap and fast.

Index Terms—OMR, OCR, MCQ, assessment, scoring systems, answer sheets.

The authors are with the School of Engineering, Edith Cowan University, Perth, Australia (e-mail: malomran@our.ecu.edu.au, d.chai@ecu.edu.au).


Cite: M. Alomran and D. Chai, "Automated Scoring System for Multiple Choice Test with Quick Feedback," International Journal of Information and Education Technology vol. 8, no. 8, pp. 538-545, 2018.

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2022: 2.0), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net


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