An Intelligent Assessment Tool for Students’ Java Submissions in Introductory Programming Courses

Fatima Al Shamsi, Ashraf Elnagar

Computer Science Department, College of Sciences, University of Sharjah, Sharjah, United Arab Emirates..

DOI: 10.4236/jilsa.2012.41006                

Abstract

This paper presents a graph-based grading system for Java introductory programming courses, eGrader. This system grades submission both dynamically and statically to ensure a complete and through grading job. While dynamic analysis is based on JUnit framework, the static analysis is based on the graph representation of the program and its quality which is measured by software metrics. The graph representation is based on the Control Dependence Graphs (CDG) and Method Call Dependencies (MCD). eGrader outperforms existing systems in two ways: the ability of grading submission with semantic-errors, effectively, and generating reports for students, as a feedback on their performance, and instructors on the overall performance of the class. eGrader is well received by instructors not only for saving time and effort but also for its high success rate that is measured by four performance indicators which are sensitivity (97.37%), specificity (98.1%), precision (98.04%) and accuracy (97.07%).

Keywords

Java; Programming; Computer Aided Education; Computer Aided Assessment; Control Dependence Graphs

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	J. Hollingsworth, “Automatic Graders for Programming Classes,” Communications of the ACM, Vol. 3, No. 10, 1960, pp. 528-529. doi:10.1145/367415.367422
[2]	A. Elnagar and L. Lulu, “A Visual Tool for Computer Supported Learning: The Robot Motion Planning Example,” International Journal of Computers & Education, Vol. 49, No. 2, 2007, pp. 269-283.
[3]	M. W. Goldberg, “Calos: An Experiment with ComputerAided Learning for Operating Systems,” ACM SIGCSE Bulletin, Vol. 28, No. 1, 1996, pp. 175-179. doi:10.1145/236462.236534
[4]	S. Harous and A. Benmerzouga, “A Computer Aided Learning Tool,” Journal of Computer Science, Vol. 4, No. 1, 2008, pp. 10-14. doi:10.3844/jcssp.2008.10.14
[5]	B. W. Becker, “Teaching CS1 with Karel the Robot in JAVA,” ACM SIGCSE Bulletin, Vol. 33, No. 1, 2001, pp. 50-54. doi:10.1145/366413.364536
[6]	C. Kelleher, “Alice: Using 3d Gaming Technology to Draw Students into Computer Science,” Proceedings of the 4th Game Design and Technology Workshop and Conference, Liverpool, 2006, pp. 16-20.
[7]	C. Evans, “The Effectiveness of m-Learning in the Form of Podcast Revision Lectures in Higher Education,” International Journal of Computers & Education, Vol. 50, No. 2, 2008, pp. 491-498.
[8]	L. F. Motiwalla, “Mobile Learning: A Framework and Evaluation,” International Journal of Computers & Education, Vol. 49, No. 3, 2007, pp. 581-596.
[9]	C. Vibet, “Handling Quiz-Based Tests with TEX Macros,” Education and Information Technologies, Vol. 2, No. 3, 1997, pp. 235-246. doi:10.1023/A:1018669415152
[10]	D. Andrews, B. Nonnecke and J. Preece, “Electronic Survey Methodology: A Case Study in Reaching Hard-toInvolve Internet Users,” International Journal of HumanComputer Interaction, Vol. 16, No. 2, 2003, pp. 185-210. doi:10.1207/S15327590IJHC1602_04
[11]	L. Prechelt, G. Malpohl and M. Philippsen, “Finding plagiarism Among a Set of Programs with JPlag,” Journal of Universal Computer Science, Vol. 8, No. 11, 2002, pp. 1016-1038.
[12]	A. Aiken, “Moss: A System for Detecting Software Plagiarism. http://www.cs.stanford.edu/aiken/moss.html
[13]	M. J. Wise, “YAP3: Improved Detection of Similarities in Computer Program and Other Texts,” ACM SIGCSE, Vol. 28, No. 1, 1996, pp. 130-134. doi:10.1145/236462.236525
[14]	J. Jadalla and A. Elnagar, “PDE4Java: Plagiarism Detection Engine for Java Source Code: A Clustering Approach,” International Journal of Business Intelligence and Data Mining, Vol. 3, No. 2, 2008, pp. 121-135. doi:10.1504/IJBIDM.2008.020514
[15]	C. Douce, D. Livingstone and J. Orwell, “Automatic TestBased Assessment of Programming: A Review,” Journal on Educational Resources in Computing, Vol. 5, No. 3, 2005, p. 4. doi:10.1145/1163405.1163409
[16]	U. Von Matt, “Kassandra: The Automatic Grading System,” SIGCUE Outlook, Vol. 22, No. 1, 1994, pp. 26-40. doi:10.1145/182107.182101
[17]	C. Daly, “RoboProf and an Introductory Computer Programming Course,” ACM SIGCSE Bulletin, Vol. 31, No. 3, 1999, pp. 155-158. doi:10.1145/384267.305904
[18]	C. Daly and J. Waldron, “Assessing the Assessment of Programming Ability,” Proceedings of the 35th SIGCSE technical Symposium on Computer Science Education, Norfolk, 3-7 March 2004, pp. 210-213.
[19]	N. Truong, P. Bancroft and P. Roe, “ELP-A Web Environment for Learning to Program,” Proceeding of the 19th Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education, Vol. 19, Auckland, 8-11 December 2002, pp. 661-670.
[20]	T. Wang, X. Su, Y. Wang and P. Ma,” Semantic Similarity-Based Grading of Student Programs,” Information and Software Technology, Vol. 49, No. 2, 2007, pp. 99-107. doi:10.1016/j.infsof.2006.03.001
[21]	N. Truong, P. Roe and P. Bancroft, “Static Analysis of Students’ Java Programs,” Proceedings of the 6th Conference on Australasian Computing Education, Vol. 30, 2004, p. 325.
[22]	K. A. Naude, J. H. Greyling and D. Vogts, “Marking Student Programs Using Graph Similarity,” Computers & Education, Vol. 54, No. 2, 2010, pp. 545-561. doi:10.1016/j.compedu.2009.09.005
[23]	V. Massol and T. Husted, “JUnit in Action,” Manning Publications Co., Greenwich, 2003.