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Reseach Article

Arabic Short Answer Scoring with Effective Feedback for Students

by Wael Hassan Gomaa, Aly Aly Fahmy
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 86 - Number 2
Year of Publication: 2014
Authors: Wael Hassan Gomaa, Aly Aly Fahmy
10.5120/14961-3177

Wael Hassan Gomaa, Aly Aly Fahmy . Arabic Short Answer Scoring with Effective Feedback for Students. International Journal of Computer Applications. 86, 2 ( January 2014), 35-41. DOI=10.5120/14961-3177

@article{ 10.5120/14961-3177,
author = { Wael Hassan Gomaa, Aly Aly Fahmy },
title = { Arabic Short Answer Scoring with Effective Feedback for Students },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 86 },
number = { 2 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 35-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume86/number2/14961-3177/ },
doi = { 10.5120/14961-3177 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:03:13.118646+05:30
%A Wael Hassan Gomaa
%A Aly Aly Fahmy
%T Arabic Short Answer Scoring with Effective Feedback for Students
%J International Journal of Computer Applications
%@ 0975-8887
%V 86
%N 2
%P 35-41
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we explore text similarity techniques for the task of automatic short answer scoring in Arabic language. We compare a number of string-based and corpus-based similarity measures, evaluate the effect of combining these measures, handle student's answers holistically and partially, provide immediate useful feedback to student and also introduce a new benchmark Arabic data set that contains 50 questions and 600 student answers. Overall, the obtained correlation and error rate results prove that the presented system performs well enough for deployment in a real scoring environment.

References
  1. Gomaa, W. H. , & Fahmy, A. A. (2013). Automatic scoring for answers to Arabic test questions. Computer Speech & Language. http://dx. doi. org/10. 1016/j. csl. 2013. 10. 005
  2. Gomaa, W. H. & Fahmy, A. A. (2012). Short Answer Grading Using String Similarity and Corpus-Based Similarity. In International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 3, No. 11.
  3. Mohler, M. , & Mihalcea, R. (2009). Text-to-text semantic similarity for automatic short answer grading. In Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (pp. 567-575). Association for Computational Linguistics.
  4. Mohler, M. , Bunescu, R. C. , & Mihalcea, R. (2011). Learning to Grade Short Answer Questions using Semantic Similarity Measures and Dependency Graph Alignments. In ACL (pp. 752-762).
  5. Basu, S. , Jacobs, C. , & Vanderwende, L. (2013). Powergrading: a Clustering Approach to Amplify Human Effort for Short Answer Grading, In Transactions of the ACL (TACL).
  6. Gomaa, W. H. & Fahmy, A. A. (2011). Tapping Into The Power of Automatic Scoring. the eleventh International Conference on Language Engineering, Egyptian Society of Language Engineering (ESOLEC '2011).
  7. Ziai, R. , Ott, N. & Meurers, D. (2012). Short answer assessment: establishing links between research strands. In: Proceedings of the Seventh Workshopon Building Educational Applications Using NLP, June. Association for Computational Linguistics, pp. 190–200.
  8. Rosé, C. P. , Roque, A. , Bhembe, D. , & VanLehn, K. (2003). A hybrid approach to content analysis for automatic essay grading. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers-Volume 2 (pp. 88-90). Association for Computational Linguistics.
  9. Leacock, C. , Chodorow, M. (2003). C-Rater: automated scoring of short-answer questions. Computers and the Humanities 37 (4), 389–405.
  10. Mitchell, T. , Russell, T. , Broomhead, P. & Aldridge, N. (2002). Towards robust computerized marking of free-text responses. In: Proceedings of the Sixth International Computer Assisted Assessment Conference. Loughborough University, Loughborough, UK.
  11. Pulman, S. G. & Sukkarieh, J. Z. (2005). Automatic short answer marking. In: Proceedings of the Second Workshop on Building Educational Applications Using NLP, June. Association for Computational Linguistics, pp. 9–16.
  12. Dzikovska, M. O. , Nielsen, R. D. , Brew, C. , Leacock, C. , Giampiccolo, D. , Bentivogli, L. , et al. (2013). SemEval 2013 task 7: the joint student response analysis and 8th recognizing textual entailment challenge. In: Proceedings of the 7th International Workshop on Semantic Evaluation (SemEval2013), in Conjunction with the Second Joint Conference on Lexical and Computational Semantics (*SEM 2013), Atlanta, Georgia, USA, June. Association for Computational Linguistics.
  13. Dzikovska, M. O. , Nielsen, R. D. & Brew, C. (2012). Towards effective tutorial feedback for explanation questions: a dataset and baselines. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, June. Association for Computational Linguistics, pp. 200–210.
  14. Gomaa, W. H. , Fahmy, A. A. (2013). A Survey of text similarity approaches. International Journal of Computer Applications 68 (13), 13–18.
  15. Kolb, P. (2009). Experiments on the difference between semantic similarity and relatedness. In Proceedings of the 17th Nordic Conference on Computational Linguistics NODALIDA'09.
  16. Taghva, K. , Elkhoury, R. , & Coombs, J. (2005, April). Arabic stemming without a root dictionary. In Information Technology: Coding and Computing, 2005. ITCC 2005. International Conference on (Vol. 1, pp. 152-157). IEEE.
  17. Hall, M. , Frank, E. , Holmes, G. , Pfahringer, B. , Reutemann, P. , & Witten, I. H. (2009). The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter, 11(1), 10-18.
  18. Mostafa, M. S. , Haggag M. H. & Gomaa, W. H. (2008). Document Clustering using Word Sense Disambiguation, In proceeding of: 17th International Conference on Software Engineering and Data Engineering (SEDE 2008), June 30 - July 2, 2008, Omni Los Angeles Hotel at California Plaza, Los Angeles, California, USA.
  19. Wang, H. & Song, M. (2011). Ckmeans : optimal k means clustering in one dimension by dynamic programming. The R Journal 3, 29–33.
Index Terms

Computer Science
Information Sciences

Keywords

Short Answer Scoring Text Similarity Semantic Similarity Arabic Corpus