Skip to main content
Log in

Automatic Question Generation for Spanish Textbooks: Evaluating Spanish Questions Generated with the Parallel Construction Method

  • ARTICLE
  • Published:
International Journal of Artificial Intelligence in Education Aims and scope Submit manuscript

Abstract

The integration of formative practice questions with textbook content is a well-known method for increasing student learning, and recent advances in artificial intelligence have made automatic question generation a viable option for scaling this learning method to thousands of textbooks. To expand this method to even more students, a parallel construction approach was developed to utilize the question generation process in English to create questions for other languages, such as Spanish. However, validation of the Spanish questions by native speaking subject matter experts is a necessary step to ensure the questions generated through parallel construction are of the same quality and suitable for educational purposes. In this paper, questions were generated via parallel construction for six Spanish textbooks and evaluated by subject matter experts teaching those subjects at major universities in Mexico and Argentina. Results from this review are discussed and implications for future use and research outlined.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Abel, A. B., & Bernanke, B. S. (2004). Macroeconomía (4th ed.). Pearson Educación.

    Google Scholar 

  • Czinkota, M. R., & Ronkainen, I. A. (2008). Marketing internacional (8th ed.). Cengage Learning.

    Google Scholar 

  • Das, B., Majumder, M., Phadikar, S., & Sekh, A. A. (2021). Automatic question generation and answer assessment: a survey. Research and Practice in Technology Enhanced Learning, 16(1), 1–15.

    Article  Google Scholar 

  • Dyer, C., Chahuneau, V., & Smith, N. A. (2013). A simple, fast, and effective reparameterization of IBM model 2. NAACL HLT 2013 - 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Main Conference, June, 644–648.

  • Feldman, R. S. (2017). Psicología con aplicaciones de América Latina (12th ed.). McGraw-Hill Education.

    Google Scholar 

  • Heilman, M., & Smith, N. A. (2010). Rating computer-generated questions with Mechanical Turk. Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk, 35–40. https://aclanthology.org/W10-0705.pdf

  • Honnibal, M., Montani, I., Van Landeghem, S., & Boyd, A. (2020). spaCy: Industrial-strength natural language processing in Python. https://doi.org/10.5281/zenodo.1212303

  • Jerome, B., Van Campenhout, R., Dittel, J. S., Benton, R., Greenberg, S., & Johnson, B. G. (2022). The Content Improvement Service: An adaptive system for continuous improvement at scale. In Meiselwitz, et al., Interaction in New Media, Learning and Games. HCII 2022. Lecture Notes in Computer Science, vol. 13517, 286–296, Springer. https://doi.org/10.1007/978-3-031-22131-6_22

  • Jerome, B., Van Campenhout, R., Dittel, J. S., Benton, R., & Johnson, B. G. (2023). Iterative improvement of automatically generated practice with the Content Improvement Service. In R. Sottilare & J. Schwarz (Eds.), Adaptive Instructional Systems. HCII 2023. Lecture Notes in Computer Science, vol. 14044, 312–324, Springer. https://doi.org/10.1007/978-3-031-34735-1_22

  • Johnson, B. G., Dittel, J. S., Van Campenhout, R., Bistolfi, R., Maeda, A., & Jerome, B. (2022a). Parallel construction: A parallel corpus approach for automatic question generation in non-English languages. Fourth Workshop on Intelligent Textbooks at the 23rd International Conference on Artificial Intelligence in Education. CEUR Workshop Proceedings, 40–49. http://ceur-ws.org/Vol-3192/itb22_p5_short9847.pdf

  • Johnson, B. G., Dittel, J. S., Van Campenhout, R., & Jerome, B. (2022b). Discrimination of automatically generated questions used as formative practice. Proceedings of the Ninth ACM Conference on Learning@Scale, 325–329. https://doi.org/10.1145/3491140.3528323

  • Koedinger, K. R., Kim, J., Jia, J., McLaughlin, E., & Bier, N. (2015). Learning is not a spectator sport: Doing is better than watching for learning from a MOOC. Proceedings of the Second ACM Conference on Learning@Scale, 111–120. https://doi.org/10.1145/2724660.2724681

  • Koedinger, K. R., McLaughlin, E. A., Jia, J. Z., & Bier, N. L. (2016). Is the doer effect a causal relationship? How can we tell and why it’s important. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, 388–397. Edinburgh, United Kingdom. https://doi.org/10.1145/2883851.2883957

  • Koedinger, K. R., Scheines, R., & Schaldenbrand, P. (2018). Is the doer effect robust across multiple data sets? Proceedings of the 11th International Conference on Educational Data Mining, 369–375.

  • Kurdi, G., Leo, J., Parsia, B., Sattler, U., & Al-Emari, S. (2020). A systematic review of automatic question generation for educational purposes. International Journal of Artificial Intelligence in Education, 30(1), 121–204. https://doi.org/10.1007/s40593-019-00186-y

    Article  Google Scholar 

  • Label, W., de León Ledesma, J., & Ramos Arriagada, R. A. (2016). Contabilidad para no contadores (2nd ed.). ECOE Ediciones.

    Google Scholar 

  • Lefer, M.-A. (2020) Parallel corpora. In M. Paquot & S. T. Gries (Eds.), A practical handbook of corpus linguistics. Springer. https://doi.org/10.1007/978-3-030-46216-1_12

  • Lovett, M., Meyer, O., & Thille, C. (2008). The open learning initiative: measuring the effectiveness of the OLI statistics course in accelerating student learning. Journal of Interactive Media in Education, 2008(1), 1–16. https://doi.org/10.5334/2008-14

    Article  Google Scholar 

  • MartínezRamírez, B. (2021). Ciencias de la comunicación (2nd ed.). McGraw-Hill Education.

    Google Scholar 

  • Mihalcea, R., & Tarau, P. (2004). TextRank: Bringing order into text. Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, 404–411. https://aclanthology.org/W04-3252

  • Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. International Conference on Learning Representations (ICLR) 2013. Workshop proceedings. https://doi.org/10.48550/arXiv.1301.3781

  • OpenAI. (2022). ChatGPT [Large language model]. https://chat.openai.com/chat

  • Ross, S. A., Westerfield, R. W., Jaffe, J., & Jordan, B. D. (2016). Finanzas corporativas (11th ed.). McGraw-Hill Education.

    Google Scholar 

  • Snell, S., & Bohlander, G. (2013a). Administración de recursos humanos (16th ed.). Cengage Learning.

    Google Scholar 

  • Snell, S., & Bohlander, G. (2013b). Managing human resources (16th ed.). South-Western, Cengage Learning.

    Google Scholar 

  • Van Campenhout R., Clark, M., Jerome, B., Dittel, J. S., & Johnson, B. G. (2023a). Advancing intelligent textbooks with automatically generated practice: A large-scale analysis of student data. Fifth Workshop on Intelligent Textbooks at the 24th International Conference on Artificial Intelligence in Education. CEUR Workshop Proceedings, 1–12. https://intextbooks.science.uu.nl/workshop2023/files/itb23_s1p2.pdf

  • Van Campenhout, R., Dittel, J. S., Jerome, B., & Johnson, B. G. (2021a). Transforming textbooks into learning by doing environments: An evaluation of textbook-based automatic question generation. Third Workshop on Intelligent Textbooks at the 22nd International Conference on Artificial Intelligence in Education. CEUR Workshop Proceedings, 60–73. http://ceur-ws.org/Vol-2895/paper06.pdf

  • Van Campenhout, R., Jerome, B., Dittel, J. S., & Johnson, B. G. (2023b). The doer effect at scale: Investigating correlation and causation across seven courses. Proceedings of LAK23: 13th International Learning Analytics and Knowledge Conference. https://doi.org/10.1145/3576050.3576103

  • Van Campenhout, R. Johnson, B. G., & Olsen, J. A. (2021b). The doer effect: Replicating findings that doing causes learning. Proceedings of eLmL 2021: The Thirteenth International Conference on Mobile, Hybrid, and On-line Learning, 1–6. https://www.thinkmind.org/index.php?view=article&articleid=elml_2021_1_10_58001

  • Van Campenhout, R., Johnson, B. G., & Olsen, J. A. (2022). The doer effect: Replication and comparison of correlational and causal analyses of learning. International Journal on Advances in Systems and Measurements, 15(1&2), 48–59. http://www.iariajournals.org/systems_and_measurements/tocv15n12.html

  • Véronis, J. (2000) From the Rosetta stone to the information society. In J. Véronis (Ed.), Parallel text processing, (pp. 1–24). Springer. https://doi.org/10.1007/978-94-017-2535-4_1

  • VitalSource Technologies (2023). VitalSource Supplemental Data Repository. https://github.com/vitalsource/data

Download references

Acknowledgements

We gratefully acknowledge the SME reviewers and coordinators for their participation in this research, Reilly Fitzgibbons for her assistance with the project, and the issue editor and anonymous reviewers for their constructive comments on the manuscript.

Funding

This work was supported by VitalSource Technologies.

The authors are employed at the company supporting the research.

Author information

Authors and Affiliations

Authors

Contributions

Benny G. Johnson, Rachel Van Campenhout, Bill Jerome, and Jeffrey S. Dittel contributed to the study conception and design. Maria Fernanda Castro and Rodrigo Bistolfi contributed subject matter expertise for question validation and reviewer instructions and feedback. Rachel Van Campenhout and Benny G. Johnson completed the manuscript. All authors approve the manuscript.

Corresponding author

Correspondence to Benny G. Johnson.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Johnson, B.G., Van Campenhout, R., Jerome, B. et al. Automatic Question Generation for Spanish Textbooks: Evaluating Spanish Questions Generated with the Parallel Construction Method. Int J Artif Intell Educ (2024). https://doi.org/10.1007/s40593-024-00394-1

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40593-024-00394-1

Keywords

Navigation