Skip to main content
SpringerLink
Log in

Implementation and Evaluation on Automated E-Learning Contents Creation Based on Natural Language Processing Techniques

  • Conference paper
  • First Online:
Web, Artificial Intelligence and Network Applications (WAINA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1150))

  • 2335 Accesses

Abstract

E-Learning automation is a set of computer instructions used to solve the difficulties of effective E-Learning systems management. And based on the merits of natural language processing techniques, E-Learning automation has already achieved great progress, such as automatic E-Learning online quizzes creation and students’ questions answering. This research mainly focuses on finding a powerful method for E-Learning contents creation to solve the problem of difficulties in E-Learning system management. This research can be mainly divided into four steps: Firstly, automatically summarize relevant texts to get straightaway texts from the source version. Secondly, detect keywords from generated summaries. Then, delete the detected keywords from summaries. Finally, rearrange outputs from previous steps to generate online questions and learning materials.

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

Access this chapter

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 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

References

  1. Ghirardini, B.: E-learning Methodologies: A Guide for Designing and Developing E-learning Courses. Food and Agriculture Organization of the United Nations, Rome (2011)

    Google Scholar 

  2. Pappas, C.: The Definitive Guide to E-Learning Automation (2016). https://elearningindustry.com/elearning-automation-definitive-guide

  3. Chen, W., Aist, G., Mostow, J.: Generating questions automatically from informational text. In: Craig, S.D., Dicheva, S. (eds.) Proceedings of the 2nd Workshop on Question Generation (2009)

    Google Scholar 

  4. Yao, X., Bouma, G., Zhang, Y.: Semantic-based question generation and implementaion. Dialogue Discourse 3(2), 11–42 (2012)

    Article  Google Scholar 

  5. Wang, Y., Allakany, A., Kulshrestha, S., Shi, W., Bose, R., Okamura, K.: Automatically generate E-Learning quizzes from IoT security ontology. In: 8th International Congress on Advanced Applied Informatics (2019)

    Google Scholar 

  6. Garbade, M.J.: A Quick Introduction to Text Summarization in Machine Learning (2018). https://towardsdatascience.com/a-quick-introduction-to-text-summarization-in-machine-learning-3d27ccf18a9f

  7. See, A., Liu, P.J., Manning, C.D.: Get to the point: summarization with pointer-generatior networks. arXiv preprint arXiv:1704.04368 (2017)

  8. Paulus, R., Xiong, C., Socher, R.: A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304 (2017)

  9. Liu, F., Pennell, D., Liu, F., Liu, Y.: Unsupervised approaches for automatic keyword extraction using meeting transcripts. In: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (2009)

    Google Scholar 

  10. Indu, M., Kavitha, K.V.: Review on text summarization evaluation methods. In: 2016 International Conference on Research Advances in Integrated Navigation Systems (RAINS), pp. 1–4. IEEE (2017)

    Google Scholar 

  11. Nedunchelian, R., Muthucumarasamy, R., Saranathan, E.: Comparison of multi document summarization techniques. Int. J. Comput. Appl. 11(3), 155–160 (2011)

    Google Scholar 

  12. Wang, Y., Okamura, K.: Automatic generation of E-Learning contents based on deep learning and natural language processing techniques. In: International Conference on Emerging Internetworking, Data & Web Technologies, pp. 311-322. Springer, Cham (2020)

    Google Scholar 

Download references

Acknowledgments

This research was supported by Strategic International Research Cooperative Program, Japan Science and Technology Agency (JST) SICORP Grant Number JPMJSC16H3 and JSPS KAKENHI Grant Number JP16K00480.

Author information

Authors and Affiliations

  1. Graduate School of Information Science and Electrical Engineer, Kyushu University, Fukuoka, Japan

    Yiyi Wang

  2. Research Institute for Information Technology, Kyushu University, Fukuoka, Japan

    Koji Okamura

Authors
  1. Yiyi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Koji Okamura
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Koji Okamura .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Electrical Engineering and Information Technology, University of Naples “Frederico II”, Naples, Italy

    Flora Amato

  3. Department of Political Science, University of Campania Luigi Vanvitelli, Caserta, Italy

    Francesco Moscato

  4. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

  5. Department of Advanced Sciences, Hosei University, Tokyo, Japan

    Makoto Takizawa

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, Y., Okamura, K. (2020). Implementation and Evaluation on Automated E-Learning Contents Creation Based on Natural Language Processing Techniques. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2020. Advances in Intelligent Systems and Computing, vol 1150. Springer, Cham. https://doi.org/10.1007/978-3-030-44038-1_61

Download citation

Publish with us

Policies and ethics

Search

Navigation