370
Views
9
CrossRef citations to date
0
Altmetric
Articles

Development of a practical system for computerized evaluation of descriptive answers of middle school level students

ORCID Icon & ORCID Icon
Pages 215-228 | Received 02 Dec 2018, Accepted 31 Jul 2019, Published online: 19 Aug 2019

References

  • Aldabe, I., Maritxalar, M., & de Lacalle, O. L. (2013). EHU-ALM: Similarity feature based approach for student response analysis. Proceedings of 7th international workshop on semantic evaluation (SemEval 2013) June 14-15, Atlanta, Georgia.
  • Alikaniotis, D., Yannakoudakis, H., & Rei, M. (2016). Automatic text scoring using neural networks. CoRR, abs/1606.04289. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, Association for Computational Linguistics. (Volume 1: Long Papers), Pages:715–725.
  • Bicici, E., & van Genabith, J. (2013). CNGL: grading student answers by acts of translation. Proceedings of SemEval 2013.
  • Blei, D. M., Ng, A. Y., Jordan, M. I., & Lafferty, J. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 993–1022.
  • Burrows, S., Gurevych, I., & Stein, B. (2015). The eras and trends of automatic short answer grading. International Journal of Artificial Intelligence in Education, 25, 60–117. doi: https://doi.org/10.1007/s40593-014-0026-8
  • Burstein, J., Kukich, K., Wolff, S., Lu, C., & Chodorow, M. (1998). Enriching automated scoring using discourse marking. Proceedings of the workshop on discourse relations and discourse marking in ACL-1998.
  • Conneau, A., Douwe, K., Holger, S., Loic, B., & Bordes, A. (2017). Supervised learning of universal sentence representations from natural language inference data (Vol. 1). Preprint, arXiv:1705.02364.
  • Das, B, & Majumder, M. (2017). Factual open cloze question generation for assessment of learner's knowledge. International Journal of Educational Technology in Higher Education, 14(1). doi: https://doi.org/10.1186/s41239-017-0060-3
  • Dessus, P., & Lemaire, B. (1999). apex, un syst‘eme daide ‘a la preparation dexamens [apex, a system to assist in the preparation of exams]. Sciences et Techniques Educatives, 6(2), 409–415. doi: https://doi.org/10.3406/stice.1999.1637
  • Dhamecha, T. I., Marvaniya, S., Saha, S., Sindhgatta, R., & Sengupta, B. (2018). Balancing human efforts and performance of student response analyzer in dialog-based tutors. In International conference on artificial intelligence in education (AIED 2018). (pp. 70–85), London, UK.
  • Dumais, S. T. (2005). Latent semantic analysis. Annual Review of Information Science and Technology, 38, 188–230. doi: https://doi.org/10.1002/aris.1440380105
  • Dzikovska, M. O., Nielsen, R. D., Brew, C., Leacock, C., Giampiccolo, D., Bentivogli, L., … Doagan, I. (2013). SemEval-2013 task 7: The joint student response analysis and 8th recognizing textual entailment challenge. Proceedings of SemEval 2013, Atlanta, GA.
  • Elliot, S. (2003). Intellimetric TM: From here to validity. In M. D. Shermis & J. Burstein (Eds.), Automated essay scoring: A cross-disciplinary perspective (p. 75). NewJersey, NJ: Lawrence Erlbaum Associates.
  • Elsayed, E., Eldahshan, K., & Tawfeek, S. (2013). Automatic evaluation technique for certain types of open questions in semantic learning systems. Human-centric Computing and Information Sciences, 3(19).
  • Farag, Y., Yannakoudakis, H., & Briscoe, T. (2018). Neural automated essay scoring and coherence modeling for adversarially crafted input. In Proceedings of NAACL-HLT, June 1–6, 2018 (pp. 263–271). New Orleans, LA.
  • Foltz, P. W., Laham, D., & Landauer, T. K. (1999). Automated essay scoring: Applications to educational technology. Proceedings of ED-MEDIA conference on educational multimedia, hypermedia, and telecommunications, Washington.
  • Geigle, C., Zhai, C. X., & Ferguson, D. (2016). An exploration of automated grading of complex assignments. In L@S 2016, April 25–26, 2016 (pp. 351–360). Edinburgh.
  • Heilman, M., & Madnani, N. (2013). ETS: Domain adaptation and stacking for short answer scoring. Proceedings of SemEval 2013, Atlanta, Georgia.
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. doi: https://doi.org/10.1162/neco.1997.9.8.1735
  • Jimenez, S., Becerra, C., Gelbukh, A., Btiz, A. J. D., & Mendizbal, A. (2013). SOFTCARDINALITY: Hierarchical text overlap for student response analysis. Proceedings of SemEval 2013, Atlanta, Georgia.
  • Leacock, C., & Chodorow, M. (2003). C-Rater: automated scoring of short-answer questions. Computers and the Humanities, 37(4), 389–405. doi: https://doi.org/10.1023/A:1025779619903
  • Lemaire, B., & Dessus, P. (2001). A system to assess the semantic content of student essays. Journal of Educational Computing Research, 24(3), 305–320. doi: https://doi.org/10.2190/G649-0R9C-C021-P6X3
  • Mihalcea, R., & Tarau, P. (2004). TextRank: Bringing order into texts. Proceedings the EMNLP-04, Barcelona, Spain.
  • Miller, G. A. (1995). WordNet: A lexical database for English. Communications of the ACM, 38(11), 39–41. doi: https://doi.org/10.1145/219717.219748
  • Miller, T. (2003). Essay assessment with latent semantic Analysis. Journal of Educational Computing Research, 29(4), 495–512. doi: https://doi.org/10.2190/W5AR-DYPW-40KX-FL99
  • Mohler, M., Bunescu, R., & Mihalcea, R. (2011). Learning to grade short answer questions using semantic similarity measures and dependency graph alignments. In Proceedings of ACL-2011 (pp. 752–762), Portland, Oregon, USA.
  • Mohler, M., & Mihalcea, R. (2009). Text-to-text semantic similarity for automatic short answer grading. In Proceedings of EACL-2009 (pp. 567–575), Athens, Greece.
  • Natarajan, A. M., Balasubramanie, P., & Tamilarasi, A. (2014). Operation research (2 ed). New Delhi, India: Pearson Publisher.
  • O'Connor, B., & Heilman, M. (2013. October). ARKref: A rule-based coreference resolution system. arXiv:1310.1975.
  • Page, E. B. (1966). The imminence of grading essays by computer. Phi Delta Kappan, 47(5), 238–243.
  • Page, E. B. (1968). The use of the computer in analyzing student essays. International Review of Education, 14(3), 253–263.
  • Pedersen, T., Patwardhan, S., & Michelizzi, J. (2004). WordNet: Similarity-measuring the relatedness of concepts. In Proceedings of National Conference on Artificial Intelligence (pp. 1024–1025), San Jose, California.
  • Ramachandran, L., Cheng, J., & Foltz, P. (2015). Identifying patterns for short answer scoring using graph-based Lexico-Semantic text matching. In Proceedings of workshop on innovative use of NLP for building educational applications, June 4, 2015 (pp. 97–106). Denver, CO.
  • Roy, S., Narahari, Y., & Deshmukh Om, D. (2015). A perspective on computer assisted assessment techniques for short free-text answers. In Proceedings of international conference on Computer Assisted Assessment, June 22–23, 2015 (pp. 96–109). Zeist.
  • Sukkarieh, J. Z., & Stoyanchev, S. (2009). Automating model building in C-Rater. In Proceedings of workshop on applied textual inference in ACL-2009 (pp. 61–69), Singapore.
  • Sultan, M. A., Salazar, C., & Sumner, T. (2016). Fast and easy short answer grading with high accuracy. In Proceedings of NAACL-HLT 2016, June 12–17, 2016 (pp. 1070–1075), San Diego, CA.
  • Taghipour, K., & Ng, H. T. (2016). A neural approach to automated essay scoring. In Proceedings of EMNLP-2016 (pp. 1882–1891), Austin, Texas, USA.
  • Zesch, T., Heilman, M., & Cahill, A. (2015). Reducing annotation efforts in supervised short answer scoring. In Proceedings of workshop on innovative use of NLP for building educational applications (pp. 124–132). Denver, CO.
  • Zhao, H., Lu, Z., & Poupart, P. (2015). Self-adaptive hierarchical sentence model. arXiv:1504.05070.
  • Zhao, S., Zhang, Y., Xiong, X., Botelho, A., & Heffernan, N. (2017). A memory-augmented neural model for automated grading. In Proceedings of ACM conference on Learning@Scale, L@S17 (pp. 189–192). Massachusetts, NY, USA.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.