Skip to main content
SpringerLink
Log in
Book cover

Frontiers of Cyberlearning pp 69–89Cite as

QUESGEN: A Framework for Automatic Question Generation Using Semantic Web and Lexical Databases

  • Chapter
  • First Online:

Part of the book series: Lecture Notes in Educational Technology ((LNET))

Abstract

Semantic web and lexical databases offer multifaceted purposes. In this chapter, we present an automatic question generation framework for teachers that deploys semantic web and lexical databases for generating questions for a specific lesson topic. This framework is intended to assist teachers in preparing questions for their lessons. We investigated two research questions: (1) “which semantic/lexical database is more appropriate for which learning domain?” and (2) “can a vector space model-based ranking algorithm enhance the relevance of generated questions?”

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    On the Bildungsserver platforms in Germany, information about education in each federal state is published.

References

  • Adamson, D., Bhartiya, D., Gujral, B., Kedia, R., Singh, A., & Rose, C. P. (2013). Automatically generating discussion questions. In Artificial Intelligence in Education (pp. 81–90).

    Google Scholar 

  • Anderson, L. W., Krathwohl, D. R., et al (Eds.) (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Allyn & Bacon. Boston, MA (Pearson Education Group).

    Google Scholar 

  • Arora, R., & Ravindran, B. (2008). Latent dirichlet allocation based multi-document summarization. In Proceedings of the 2nd Workshop on Analytics for Noisy Unstructured Text Data (pp. 91–97).

    Google Scholar 

  • Bhagwant. (2017). Latent semantic analysis tutorial. Available: https://technowiki.wordpress.com/2011/08/27/latent-semantic-analysis-lsa-tutorial/.

  • Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., & Hellmann, S. (2009): DBpedia – A crystallization point for the Web of Data. Web Semantics: science, services and agents on the world wide web, 7(3), 154–165.

    Article  Google Scholar 

  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.

    Google Scholar 

  • Cambridge University Press. (2004). Teaching repertoire: Pedagogy and practice-teaching and learning in secondary schools Unit 7: Questioning. Department for Education and Skills. DfES Publications.

    Google Scholar 

  • Chafi, M. E., & Elkhouzai, E. (2014). Classroom interaction: Investigating the forms and functions of teacher questions in Moroccan primary school. Journal of Innovation and Applied Studies, 6(3), 352–361.

    Google Scholar 

  • Chali, Y., & Hasan, S. A. (2015). Towards topic-to-question generation. Journal Computational Linguistics, 41(1), 1–20.

    Article  Google Scholar 

  • Chaudhri, V. K., Cheng, B., Overholtzer, A., Roschelle, J., Spaulding, A., Clark, P., Greaves, M., & Gunning, D. (2013). Inquire Biology: A textbook that answers questions. AI Magazine, 34(3).

    Article  Google Scholar 

  • Chicago Center for Teaching. (2016). The power of questions. http://teaching.uchicago.edu/teaching-guides/asking-effective-questions/. Last Access: July 13, 2016.

  • Dewey, J. (1966). Democracy and education: An introduction to the philosophy of education. New York: The Free Press. http://www.archive.org/stream/democracyandedu01dewegoog. Last Access: April 3, 2017.

  • Dickman, N. E. (2009). The challenge of asking engaging questions. In J. Rege (Ed.), Currents in Teaching and Learning, 2(1).

    Google Scholar 

  • Fischer, R. (2005). Questions for thinking. In English! Winter, 6–9.

    Google Scholar 

  • Graesser, A. C., Ozuru, Y., & Sullins, J. (2009). What is a good question? In M. G. McKeown & L. Kucan (Eds.), Threads of coherence in research on the development of reading ability (pp. 112–141).

    Google Scholar 

  • Harvey, S., & Goudvis, A. (2007). Strategies that work: Teaching comprehension for understanding and engagement (2nd ed.). Stenhous Publishers.

    Google Scholar 

  • Heilman, M., & Smith, N. A. (2010). Good question! Statistical ranking for question generation. In Proceedings of the annual conference of the North American Chapter of the Association for Computational Linguistics (pp. 609–617).

    Google Scholar 

  • Intel Teach Program. (2007). Designing effective projects: Questioning, the Socratic questioning technique.

    Google Scholar 

  • Jouault, C., Seta, K., & Hayashi, Y. (2016). Content-dependent question generation using LOD for history learning in open learning space. Japan Society of Artificial Intelligence, 34(4).

    Article  Google Scholar 

  • Kawamura, Y. (2012). Reading Tutor, A Reading Support System for Japanese Language Learners. Acta Linguistica Asiatica, 2(3).

    Article  Google Scholar 

  • Koujalagi, A. (2015). Determine word relevance in document queries using TF-IDF. International Journal of Scientific Research, 4(8). https://doi.org/10.15373/22778179.

  • Le, N. T. (2015). Using Semantic Web for Generating Questions: Do Different Populations Perceive Questions Differently?. Transaction on Computational Collective Intelligence, 17.

    Google Scholar 

  • Le, N. T., Kojiri, T., & Pinkwart, N. (2014). Automatic Question Generation for Educational Applications - the state of Art. In T. V. Do, H. A. L. Thi, N. T. Ngugen, (Eds.), Advances in Intelligent Systems and Computing (282) - Advanced Computational Methods for Knowledge Engineering - Proceedings of the 2nd International Conference on Computer Science, Applied Mathematics and Applications (ICCSAMA) (pp. 325–338). Berlin, Germany, Springer Verlag.

    Chapter  Google Scholar 

  • Le, N.-T., & Pinkwart, N. (2015). Evaluation of a question generation approach using open linked data for supporting argumentation. Special Issue on Modeling, Management and Generation of Problems/Questions in Technology-Enhanced Learning—Journal Research and Practice in Technology Enhanced Learning.

    Google Scholar 

  • Le, N.-T., & Pinkwart, N. (2016). Ein adaptierbares Fragengenerierungs-Framework zur Unterrichtsvorbereitung. In Proceedings of the DELFI Workshop on “Blended Learning konkret: Didaktische Szenarien für den täglichen Unterricht”.

    Google Scholar 

  • Leven, T., & Long, R. (1981). Effective instruction. Washington, D.C.: Association for supervision and Curriculum Development.

    Google Scholar 

  • Li, L., Deng, H., Dong, A., Chang, Y., Baeza-Yates, R., & Zha, H. (2017). Exploring query auto-completion and click logs for contextual-aware web search and query suggestion. In Proceedings of the 26th International Conference on World Wide Web (pp. 539–548).

    Google Scholar 

  • Liu, M., Calvo, R. A., & Rus, V. (2012). G-Asks: An intelligent automatic question generation system for academic writing support. Dialogue and Discourse, 3(2), 101–124.

    Article  Google Scholar 

  • Magalhaes, J., & Rueger, S. (2007). High-dimensional visual vocabularies for image retrieval. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 815–816).

    Google Scholar 

  • Manning, C. D., Raghavan, P., & Schütze, H. (2009). An introduction to information retrieval.

    Google Scholar 

  • Mason, J. (2011). Cognitive engagement and questioning online. In A. Mendez-Vilas (Ed.), Education in a technological world: Communicating current and emerging research and technological efforts.

    Google Scholar 

  • Miller, G. A. (1995). WordNet: A lexical database for English. Communications of the ACM, 38(11), 39–41.

    Article  Google Scholar 

  • Mostow, J. (2011). Questions and answers about questions and answers: Lessons from generating, scoring, and analyzing questions in a reading tutor for children, Invited talk at AAAI Symposium on Question Generation. Arlington, VA, USA.

    Google Scholar 

  • Mostow, J., Beck, J., Cuneo, A., Gouvea, E., Heiner, C., & Juarez, O. (2010). Lessons from project LISTEN’s session browser. In C. Romero, S. Ventura, S. R. Viola, M. Pechenizkiy, & R. S. J. d. Baker (Eds.), Handbook of educational data mining (pp. 389–416). Taylor & Francis Group.

    Google Scholar 

  • Mostow, J., Nelson, J., & Beck, J. E. (2013). Computer-guided oral reading versus independent practice: Comparison of sustained silent reading to an automated reading tutor that listens. Journal of Educational Computing Research, 49(2), 249–276.

    Article  Google Scholar 

  • Musto, C. (2010). Enhanced vector space models for content-based recommender systems. In Proceedings of the 4th ACM Conference on Recommender Systems (pp. 361–364).

    Google Scholar 

  • Myhill, D., & Dunkin, F. (2005). Questioning learning. Language and Education, 19(5), 415–427.

    Article  Google Scholar 

  • Nguyen, T. V., Nguyen, A. T., Phan, H. D., Nguyen, T. D., & Nguyen, T. N. (2017). Combining Word2Vec with revised vector space model for better code retrieval. In Proceedings of the 39th International Conference on Software Engineering Companion (pp. 183–185).

    Google Scholar 

  • Ontario Ministry of Education. (2011). Asking effective questions. Capacity Building Series, #21.

    Google Scholar 

  • Ramos, J. (2003). Using Tf-Idf to determine word relevance in document queries.

    Google Scholar 

  • Schank, R., & Cleary, C. (1995). Engines for education. New Jersey: Lawrence Erlbaum Associates.

    Google Scholar 

  • Tofade, T., Elsner, J., & Haines, S. T. (2013). Best practice strategies for effective use of questions as a teaching tool. American Journal of Pharmaceutical Education, 77(7), Article 155.

    Article  Google Scholar 

  • Turney, P. D., & Pantel, P. (2010). From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research, 37, 141–188.

    Article  Google Scholar 

  • Wilen, W. W. (1991). Questioning skills for teachers. What Research Says to The Teacher Series. Washington, D.C.: National Education Association.

    Google Scholar 

  • Xu, G., Zhang, Y., & Yi, X. (2008). Modelling user behaviour for web recommendation using LDA model. In Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (Vol. 3, pp. 529–532).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany

    Nguyen-Thinh Le, Alexej Shabas & Patrick McLaren

Authors
  1. Nguyen-Thinh Le
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexej Shabas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Patrick McLaren
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nguyen-Thinh Le .

Editor information

Editors and Affiliations

  1. Department of Learning Technologies, University of North Texas, Denton, TX, USA

    J. Michael Spector

  2. School of Computing and Information System, Athabasca University, Athabasca, Canada

    Vivekanandan Kumar

  3. Analytics and R&D, McGraw-Hill Education, Boston, MA, USA

    Alfred Essa

  4. Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan

    Yueh-Min Huang

  5. Open University in the Netherlands, Heerlen, The Netherlands

    Rob Koper

  6. School of Computing, University of Eastern Finland, Joensuu, Finland

    Richard A. W. Tortorella

  7. Smart Learning Institute, Beijing Normal University, Beijing, China

    Ting-Wen Chang

  8. School of Educational Technology, Beijing Normal University, Beijing, China

    Yanyan Li

  9. School of Educational Technology, Beijing Normal University, Beijing, China

    Zhizhen Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Le, NT., Shabas, A., McLaren, P. (2018). QUESGEN: A Framework for Automatic Question Generation Using Semantic Web and Lexical Databases. In: Spector, J., et al. Frontiers of Cyberlearning. Lecture Notes in Educational Technology. Springer, Singapore. https://doi.org/10.1007/978-981-13-0650-1_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-0650-1_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0649-5

  • Online ISBN: 978-981-13-0650-1

  • eBook Packages: EducationEducation (R0)

Publish with us

Policies and ethics

Search

Navigation