Abstract
Assessment Item Generation (AIG) aims at creating semi-automatically many items from a template. This type of approaches has been used in various domains, including language learning and mathematics to support adaptation of tests to learners or allow the item authoring process to scale through decreasing the cost of items. We illustrate in this paper the automatic creation of inline choice items for reading comprehension skills using state-of-the-art approaches. However we show how the AIG process can be implemented to support the creation of items in multiple languages (English, French, and German) and how it can be complemented by the creation of item quality metrics to improve the selection of the generated items.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
- 10.
- 11.
- 12.
- 13.
Computer. http://en.wikipedia.org/wiki/Computer.
Computer. http://de.wikipedia.org/wiki/Computer.
Ordinateur. http://fr.wikipedia.org/wiki/Ordinateur. Accessed: 27 Feb. 2015.
- 14.
Roman Empire. http://en.wikipedia.org/wiki/Roman_Empire.
Römische Kaiserzeit. http://de.wikipedia.org/wiki/R%C3%B6mische_Kaiserzeit.
Empire romain. http://fr.wikipedia.org/wiki/Empire_romain. Accessed: 27 Feb. 2015.
- 15.
References
ACRL: Framework for Information Literacy for Higher Education (2015)
Brown, J.C., Frishkoff, G.A., Eskenazi, M.: Automatic question generation for vocabulary assessment. In: Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP). pp. 819–826. Association for Computational Linguistics, Vancouver, Canada (2005)
Chen, C.Y., Liou, H.C., Chang, J.S.: Fast: an automatic generation system for grammar tests. In: COLING/ACL on Interactive Presentation Sessions, pp. 1–4. Association for Computational Linguistics, Morristown, NJ, USA (2006)
Mostow, J., et al.: Using automated questions to assess reading comprehension, vocabulary, and effects of tutorial interventions. Technol. Instr. Cogn. Learn. 2, 97–134 (2004)
Papasalouros, A., Kanaris, K., Kotis, K.: Automatic generation of multiple choice questions from domain ontologies. In: e-Learning, Citeseer (2008)
Gierl, M.J., Lai, H.: Using weak and strong theory to create item models for automatic item generation. Autom. Item Gener.: Theor. Pract., 26 (2012)
Haladyna, T.M., Downing, S.M., Rodriguez, M.C.: A review of multiple-choice item-writing guidelines for classroom assessment. Appl. Measur. Educ. 15(3), 309–333 (2002)
Mitkov, R., Ha, L.A., Karamanis, N.: A computer-aided environment for generating multiple-choice test items. Nat. Lang. Eng. 12(2), 177–194 (2006)
Shah, R.: Automatic question generation using discourse cues and distractor selection for cloze questions. In: Language Technology and Research Center (LTRC), International Institute of Information Technology, Hyderabad (2012)
Becker, L., Basu, S., Vanderwende, L.: Mind the gap: learning to choose gaps for question generation. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics (2012)
Heilman, M.: Automatic Factual Question Generation from Text. Carnegie Mellon University, Pittsburgh (2011)
Iwata, T., et al.: Automatic Generation of English Cloze Questions Based on Machine Learning. NTT Communication Science Laboratories, Kyoto (2011)
Agarwal, M., Shah, R., Mannem, P.: Automatic question generation using discourse cues. In: Proceedings of the 6th Workshop on Innovative use of NLP for Building Educational Applications, Association for Computational Linguistics (2011)
Agarwal, M.: Cloze and Open Cloze Question Generation Systems and their Evaluation Guidelines. International Institute of Information Technology, Hyderabad (2012)
Wu, J.-C., Chang, J., Chang, J.S.: Correcting serial grammatical errors based on N-grams and syntax. 中文計算語言學期刊 18(4): 31–44 (2013)
Haladyna, T.M.: Automatic item generation - a hitorical perspective. In: Gierl, M.J., Haladyna, T.M. (eds.) Automatic Item Generation. Routledge, New York (2013)
Linnebank, F., Liem, J., Bredeweg, B.: Question generation and answering. DynaLearn, Deliverable D3.3, EC FP7 STREP Project 231526 (2010)
Foulonneau, M.: Generating educational assessment items from linked open data: the case of DBpedia. In: García-Castro, R., Fensel, D., Antoniou, G. (eds.) ESWC 2011. LNCS, vol. 7117, pp. 16–27. Springer, Heidelberg (2012)
Foulonneau, M., Grouès, G.: Common vs. expert knowledge: making the semantic web an educational model. In: 2nd International Workshop on Learning and Education with the Web of Data (LiLe–2012 at WWW-2012 conference), Lyon, France (2012)
Mitkov, R., et al.: Semantic similarity of distractors in multiple-choice tests: extrinsic evaluation. In: Workshop on Geometrical Models of Natural Language Semantics (GEMS 2009), pp. 49–56. Association for Computational Linguistics, Morristown, NJ, USA (2009)
Gütl, C., Lankmayr, K., Weinhofer, J.: Enhanced approach of automatic creation of test items to foster modern learning setting. In: 9th European Conference on e-Learning (ECEL 2010), Porto, Portugal, pp. 225–234. (2010)
Aldabe, I., Maritxalar, M.: Automatic distractor generation for domain specific texts. In: Loftsson, H., Rögnvaldsson, E., Helgadóttir, S. (eds.) IceTAL 2010. LNCS, vol. 6233, pp. 27–38. Springer, Heidelberg (2010)
Moser, J.R., Gütl, C., Liu, W.: Refined distractor generation with LSA and stylometry for automated multiple choice question generation. In: Thielscher, M., Zhang, D. (eds.) AI 2012. LNCS, vol. 7691, pp. 95–106. Springer, Heidelberg (2012)
Holmes, D.: The evolution of stylometry in humanities scholarship. Literary Linguist. Comput. 13(3), 111–117 (1998)
Huang, Y.-T., et al.: TEDQuiz: automatic quiz generation for TED talks video clips to assess listening comprehension. In: 2014 IEEE 14th International Conference on Advanced Learning Technologies (ICALT), IEEE (2014)
Deane, P., et al.: Creating vocabulary item types that measure students’ depth of semantic knowledge. In: ETS Research Report Series, pp. 1–19 (2014)
Smith, S., Avinesh, P., Kilgarriff, A.: Gap-fill tests for language learners: corpus-driven item generation. In: International Conference on Natural Language Processing, Macmillan Publishers, India (2010)
Zesch, T., Melamud, O.: Automatic generation of challenging distractors using context-sensitive inference rules. In: Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications (2014)
Hoshino, A., Nakagawa, H.: Assisting cloze test making with a web application. In: Society for Information Technology and Teacher Education International Conference (2007)
Gierl, M.J., Lai, H.: Using weak and strong theory to create item models for automatic item generation. In: Gierl M.J., Haladyna T.M. (eds.) Automatic Item Generation. Routledge, New York (2013)
Sonnleitner, P.: Using the LLTM to evaluate an item-generating system for reading comprehension. Psychol. Sci. 50(3), 345 (2008)
Pho, V.-M., et al.: Multiple choice question corpus analysis for distractor characterization. In: 9th International Conference on Language Resources and Evaluation (LREC 2014) (2014)
Kolb, P.: Disco: a multilingual database of distributionally similar words. In: Proceedings of KONVENS-2008, Berlin (2008)
François, T., Miltsakaki, E.: Do NLP and machine learning improve traditional readability formulas? In: Proceedings of the First Workshop on Predicting and Improving Text Readability for Target Reader Populations, Association for Computational Linguistics (2012)
Washburne, C., Vogel, M.: Are any number combinations inherently difficult? J. Educ. Res. 17(4), 235–254 (1928)
Dale, E., Chall, J.S.: A formula for predicting readability: Instructions. Educ. Res. Bull. 27, 37–54 (1948)
Flesch, R.: A new readability yardstick. J. Appl. Psychol. 32(3), 221 (1948)
Kandel, L., Moles, A.: Application de l’indice de flesch à la langue française. J. Educ. Res. 21, 283–287 (1958)
Amstad, T.: Wie verständlich sind unsere Zeitungen? Studenten-Schreib-Service (1978)
Dale, E., Tyler, R.W.: A study of the factors influencing the difficulty of reading materials for adults of limited reading ability. Libr. Q. 4(3), 384–412 (1934)
Halliday, M.A.K., Hasan, R.: Cohesion in English. Routledge, New York (2014)
Zeid, E.A., Foulonneau, M., Atéchian, T.: Réutiliser des textes dans un contexte éducatif. Document Numérique 15(3), 119–142 (2012)
François, T., Fairon, C.: An AI readability formula for French as a foreign language. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Association for Computational Linguistics (2012)
Mesnager, J.: Mots fréquents ou mots usuels. Communication et langages 84(1), 33–46 (1990)
François, T., Fairon, C.: Les apports du TAL à la lisibilité du français langue étrangère (2013)
Goosse, A., Grevisse, M.: Le bon usage: grammaire française, Duculot (1993)
Haladyna, T.M., Downing, S.M.: A taxonomy of multiple-choice item-writing rules. Appl. Measur. Educ. 2(1), 37–50 (1989)
Acknowledgements
The research leading to these results was funded by the INTERREG IV A “Grande Région” 2007–2013 under grant agreement No. 138 GR DELUX 2 3 274.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Pfeiffer, A., Kuraeva, A., Foulonneau, M., Djaghloul, Y., Tobias, E., Ras, E. (2015). Automatically Generated Metrics for Multilingual Inline Choice Questions on Reading Comprehension. In: Ras, E., Joosten-ten Brinke, D. (eds) Computer Assisted Assessment. Research into E-Assessment. TEA 2015. Communications in Computer and Information Science, vol 571. Springer, Cham. https://doi.org/10.1007/978-3-319-27704-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-27704-2_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27703-5
Online ISBN: 978-3-319-27704-2
eBook Packages: Computer ScienceComputer Science (R0)