Advertisement

Automatic Generation of Coherent Learning Pathways for Open Educational Resources

Conference paper
  • 2k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11722)

Abstract

Learners and educators all over the world have been increasingly relying on the internet for education, thus generating and consuming vast amounts of online learning resources. Selecting appropriate learning resources among them and structuring them in a way that maximises comprehension and skill building is a challenging task. In this work, we propose a model to automatically generate learning pathways from available open learning resources, such that the generated pathways are semantically coherent and pedagogically progressive. The proposed model has two components– a Greedy Generator and a Validator based on Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) models respectively. The Greedy Generator chooses the next resource in the learning pathway based on local considerations and the Validator validates the learning pathway as a whole. They work in tandem with each other connected by a feedback loop. Since we work with open educational resources that lack standard meta-data, we also propose methods to generate metrics that compare a pair of learning resources. The learning pathways generated by our model from a corpus of open learning resources show promising results.

Keywords

Learning pathway generation Deep Learning Natural language processing Technology enhanced learning 

Notes

Acknowledgements

The authors would like to thank the project associates Abhiramon Rajasekharan, Nikhil Sai Bukka, and Vibhav Agarwal for their contribution.

References

  1. 1.
    Arnett, T.: Connecting Ed & Tech: Partnering to Drive Student Outcomes. Clayton Christensen Institute for Disruptive Innovation (2016)Google Scholar
  2. 2.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  3. 3.
    Brusilovsky, P., Henze, N.: Open corpus adaptive educational hypermedia. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 671–696. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-72079-9_22CrossRefGoogle Scholar
  4. 4.
    Changuel, S., Labroche, N., Bouchon-Meunier, B.: Resources sequencing using automatic prerequisite-outcome annotation. ACM Trans. Intell. Syst. Technol. (TIST) 6(1), 6 (2015)Google Scholar
  5. 5.
    Chen, C.M.: Intelligent web-based learning system with personalized learning path guidance. Comput. Educ. 51(2), 787–814 (2008)CrossRefGoogle Scholar
  6. 6.
    Diwan, C., Srinivasa, S., Ram, P.: Computing exposition coherence across learning resources. In: Panetto, H., Debruyne, C., Proper, H., Ardagna, C., Roman, D., Meersman, R. (eds.) On the Move to Meaningful Internet Systems. Lecture Notes in Computer Science, vol. 11230, pp. 423–440. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-02671-4_26CrossRefGoogle Scholar
  7. 7.
    Fung, S., Tam, V., Lam, E.Y.: Enhancing learning paths with concept clustering and rule-based optimization. In: 2011 11th IEEE International Conference on Advanced Learning Technologies (ICALT), pp. 249–253. IEEE (2011)Google Scholar
  8. 8.
    Hsieh, T.C., Lee, M.C., Su, C.Y.: Designing and implementing a personalized remedial learning system for enhancing the programming learning. J. Educ. Technol. Soc. 16(4), 32–46 (2013)Google Scholar
  9. 9.
    Karampiperis, P., Sampson, D.: Adaptive learning resources sequencing in educational hypermedia systems. J. Educ. Technol. Soc. 8(4), 128–147 (2005)Google Scholar
  10. 10.
    Knauf, R., Sakurai, Y., Takada, K., Tsuruta, S.: Personalizing learning processes by data mining. In: 2010 IEEE 10th International Conference on Advanced Learning Technologies (ICALT), pp. 488–492. IEEE (2010)Google Scholar
  11. 11.
    Labutov, I., Lipson, H.: Web as a textbook: curating targeted learning paths through the heterogeneous learning resources on the web. In: EDM, pp. 110–118 (2016)Google Scholar
  12. 12.
    Lilleberg, J., Zhu, Y., Zhang, Y.: Support vector machines and word2vec for text classification with semantic features. In: 2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC), pp. 136–140. IEEE (2015)Google Scholar
  13. 13.
    Manrique, R.: Towards automatic learning content sequence via linked open data. In: Proceedings of the International Conference on Web Intelligence, pp. 1230–1233. ACM (2017)Google Scholar
  14. 14.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  15. 15.
    Pérez Martínez, C., López Morteo, G., Martínez Reyes, M., Gelbukh, A.: Wikipedia-based learning path generation. Computación y Sistemas 19(3), 589–600 (2015)CrossRefGoogle Scholar
  16. 16.
    Shen, L., Shen, R.: Learning content recommendation service based-on simple sequencing specification. In: Liu, W., Shi, Y., Li, Q. (eds.) ICWL 2004. LNCS, vol. 3143, pp. 363–370. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-27859-7_47CrossRefGoogle Scholar
  17. 17.
    Siehndel, P., Kawase, R., Nunes, B.P., Herder, E.: Towards automatic building of learning pathways. In: WEBIST, vol. 2, pp. 270–277 (2014)Google Scholar
  18. 18.
    Sparck Jones, K.: A statistical interpretation of term specificity and its application in retrieval. J. Documentation 28(1), 11–21 (1972)CrossRefGoogle Scholar
  19. 19.
    Tsai, M.J., Tsai, C.C.: Information searching strategies in web-based science learning: the role of internet self-efficacy. Innov. Educ. Teach. Int. 40(1), 43–50 (2003)CrossRefGoogle Scholar
  20. 20.
    Wong, L.H., Looi, C.K.: Adaptable learning pathway generation with ant colony optimization. J. Educ. Technol. Soc. 12(3), 309 (2009)Google Scholar
  21. 21.
    Yu, Z., Nakamura, Y., Jang, S., Kajita, S., Mase, K.: Ontology-based semantic recommendation for context-aware e-learning. In: Indulska, J., Ma, J., Yang, L.T., Ungerer, T., Cao, J. (eds.) UIC 2007. LNCS, vol. 4611, pp. 898–907. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-73549-6_88CrossRefGoogle Scholar
  22. 22.
    Yueh-Min, H., Tien-Chi, H., Wang, K.T., Hwang, W.Y.: A markov-based recommendation model for exploring the transfer of learning on the web. J. Educ. Technol. Soc. 12(2), 144 (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.IIIT BangaloreBengaluruIndia
  2. 2.Gooru Inc.Redwood CityUSA

Personalised recommendations