Volume-5 Issue-4

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Volume-5 Issue-4

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Volume-5 Issue-4, May 2019, ISSN: 2319-9598 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Page No.

1.

Authors:

Anindya Sundar Dey

Paper Title:

Detection of Suspicious Patterns in Online MCQ Exams

Abstract: The purpose of this paper is to find ways to identify or detect suspicious patterns in multiple choice questions and highlight them allowing for greater scrutiny and helping curb malpractices in examination halls. For this purpose an algorithm has been developed to detect suspicious answer patterns in online MCQ exams which can detect if multiple students have been taking outside help using applications like Team Viewer. To obtain data for this project we obtained results of an MCQ test where two groups of students in who completed a MCQ test of moderate difficulty. While both groups were kept under scrutiny. Unlike group B group A had a select number of “special examinees” who were being helped by outside sources( teachers ). We later ran the test results of both the groups under the algorithm. The Algorithm was able to detect 17 Students with Suspicious Patterns in Group A. While no student was detected in Group B.

Keywords: Suspicion Threshold, Online Mcq, Suspicion Factor, Answer Key, Array of Converted Answers, Comparison Point.

References:

  1. Bellezza, F.S. and Bellezza, S.F. (1989) Detection of Cheating on Multiple-Choice Tests by Using Error-Similarity Analysis. Teaching of Psychology, 16(3): 151–155.
  2. Bresnock, A.E. (1989) Multiple-Choice Testing: Question and Response Position. Journal of Economic Education, 20(3), 239–245.
  3. Denyer, G. and Hancock, D. (2002) Graded Multiple Choice Questions: Rewarding Understanding and Preventing Plagiarism. Journal of Chemical Education, 79(8), 961–964.
  4. Frary, R.B. (1993) Statistical Detection of Multiple-Choice Answer Copying: Review and Commentary. Applied Measurement in Education, 6(2), 153–165.
  5. Houston, J.P. (1983) Alternate Test Forms as a Means of Reducing Multiple-Choice Answer Copying in the Classroom. Journal of Educational Psychology, 75(4), 572–575.
  6. Houston, J.P. (1986) Classroom Answer Copying: Roles of Acquaintanceship and Free versus Assigned Seating. Journal of Educational Psychology, 78(3), 230–232.
  7. Moeck, P.G. (2002) Academic Dishonesty: Cheating among Community College Students. Community College Journal of Research and Practice, 26(6), 479–491.
  8. Sotaridona, L.S. and Meijer, R.R. (2001) Two New Statistics To Detect Answer Copying. Research Report, Twente University, Enschede (Netherlands). Faculty of Educational Science and Technology. [BBB31588].
  9. Levitt S.D. & Jacob B.A. (2003) Rotten Apples: An Investigation of the Prevalence and Predictors of Cheating, The Quarterly Journal of Economics, 118(3), pg 843-877
  10. Wollack J.A. (1997) A Nominal Response Model Approach for Detecting Answer Copying, Applied Psychological Movement, 21(4),pg 307-320
  11. Van der Linden, W. J., & Sotaridona, L. (2006). Detecting Answer Copying When the Regular Response Process Follows a Known Response Model. Journal of Educational and Behavioral Statistics, 31(3), 283–304. 
  12. Wollack, J. A. (2003). Comparison of Answer Copying Indices with Real Data. Journal of Educational Measurement, 40(3), 189–205. 
  13. Angoff, W. H. (1974). The Development of Statistical Indices for Detecting Cheaters. Journal of the American Statistical Association, 69(345), 44–49. 
  14. Saupe, J. L. (1960). An Empirical Model for the Corroboration of Suspected Cheating on Multiple-Choice Tests. Educational and Psychological Measurement, 20(3), 475–489.
  15. Wollack, J. A. & Cohen, A. (1998). Detection of answer copying with unknown item and trait parameters. Applied Psychological Measurement, 22, 144-152.
  16. Scheers, N. J., & Dayton, C. M. (1987). Improved estimation of academic cheating behavior using the randomized response technique. Research in Higher Education, 26, 61-69.
  17. Anikeef, A. M. (1954). Index of collaboration for test administrators. Journal of Applied
  18. Psychology, 38, 174-177.
  19. Dickenson, H. F. (1945). Identical Errors and Deception. The Journal of Educational Research, 38(7), 534–542. 
  20. Cody, R. P. (1985). Statistical analysis of examinations to detect cheating. Journal of Medical
  21. Education, 60, 136-137.
  22. Bird, C. (1929). An improved method of detecting cheating in objective examinations. Journal
  23. of Educational Research, 19, 341-348.
  24. Baird, J. S., Jr. (1980). Current trends in college cheating. Psychology in the Schools, 17, 515-522

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2.

Authors:

Remya Paul

Paper Title:

Survey on Various Techniques to Decode Low Quality QR Codes

Abstract: QR codes also known as quick response code is a two dimensional barcode. It was created by Denso Wave .It was frame worked for automotive industry but today it was found everywhere in magazines, billboards, newspapers and so on. Compared to barcodes QR codes can carry large data and it’s more fault tolerant. Earlier there are specific scanners are used to decode QR code. Today modern handheld devices like mobile phones can capture and read QR code. But the problem is that images can be blurred or of low quality. It may be due to poor quality of the camera or external elements present in the code. This paper does a survey of various techniques available decode low quality QR codes

Keywords: Bar Codes, Binarization, Decoding, Image Pre-Processing, QR codes, Symbologies

References:

  1. Kamon Homkajorn, Mahasak Ketcham, and Sartid Vongpradhip “A Technique to Remove Scratches from QR Code Images ”, International Conference on Computer and Communication Technologies (ICCCT'2012) May 26-27, 2012 Phuket
  2. Poompavai1 , Dr.R.BalaSubramanian,”An Image Binarization Algorithm for Scratches Removal and Restoration of QR Code Using Spatial Point Processing Threshold “,International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882 Volume 5, Issue 9, September 2016
  3. Ashna Thomas , Remya Paul,” An Effective Method for Removing Scratches and Restoring Low -Quality QR Code Image”, International Journal For Advance Research In Engineering And Technology, Volume 1, Issue V, June 2013
  4. Liyanage, J. P. 2007. “Efficient Decoding of Blurred, Pitched, and Scratched Barcode Images”. Proceedings of the 2nd international conference on industrial and information systems, Kandy, Sri Lanka.
  5. Ramtin Shams Parastoo Sadeghi ,” Bar Code Recognition in Highly Distorted and Low Resolution Images”, IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07
  6. Douglas Chai and Florian Hock ,”Locating And Decoding Ean-13 Barcodes From Images Captured By Digital Cameras” The Seventh International Conference on Information and Communications - curity,ICICS2005
  7. Yue Liu Ju Yang Mingjun Liu,” Recognition of QR Code with mobile phones”, Chinese Control and Decision Conference-2008
  8. Jeng-An Lin and Chiou-Shann Fuh,”2D Barcode Image Decoding”, Mathematical Problems in Engineering
    Volume 2013, Article ID 848276, 10 pages

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