Advertisement

Towards the Readiness of Learning Analytics Data for Micro Learning

  • Jiayin LinEmail author
  • Geng SunEmail author
  • Jun ShenEmail author
  • Tingru CuiEmail author
  • Ping YuEmail author
  • Dongming XuEmail author
  • Li LiEmail author
  • Ghassan BeydounEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11515)

Abstract

With the development of data mining and machine learning techniques, data-driven based technology-enhanced learning (TEL) has drawn wider attention. Researchers aim to use established or novel computational methods to solve educational problems in the ‘big data’ era. However, the readiness of data appears to be the bottleneck of the TEL development and very little research focuses on investigating the data scarcity and inappropriateness in the TEL research. This paper is investigating an emerging research topic in the TEL domain, namely micro learning. Micro learning consists of various technical themes that have been widely studied in the TEL research field. In this paper, we firstly propose a micro learning system, which includes recommendation, segmentation, annotation, and several learning-related prediction and analysis modules. For each module of the system, this paper reviews representative literature and discusses the data sources used in these studies to pinpoint their current problems and shortcomings, which might be debacles for more effective research outcomes. Accordingly, the data requirements and challenges for learning analytics in micro learning are also investigated. From a research contribution perspective, this paper serves as a basis to depict and understand the current status of the readiness of data sources for the research of micro learning.

Keywords

Micro learning Learning analytics Machine learning Data mining Data insufficiency 

Notes

Acknowledgement

This research has been carried out with the support of the Australian Research Council Discovery Project, DP180101051, and Natural Science Foundation of China, no. 61877051, and UGPN RCF 2018-2019 project between University of Wollongong and University of Surrey.

References

  1. 1.
    Ferguson, R.: The state of learning analytics in 2012: a review and future challenges. Knowledge Media Institute, Technical report KMI-2012-01 (2012)Google Scholar
  2. 2.
    Sun, G., et al.: MLaaS: a cloud-based system for delivering adaptive micro learning in mobile MOOC learning. IEEE Trans. Serv. Comput. 11(2), 292–305 (2018)CrossRefGoogle Scholar
  3. 3.
    Hendez, M., Achour, H.: Keywords extraction for automatic indexing of e-learning resources. In: 2014 World Symposium on Computer Applications & Research (WSCAR). IEEE (2014)Google Scholar
  4. 4.
    Du, X., Zhang, F., Zhang, M., Xu, S., Liu, M.: Research on result integration mechanism based on crowd wisdom to achieve the correlation of resources and knowledge points. In: Wu, T.-T., Huang, Y.-M., Shadieva, R., Lin, L., Starčič, A.I. (eds.) ICITL 2018. LNCS, vol. 11003, pp. 568–577. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-99737-7_60CrossRefGoogle Scholar
  5. 5.
    Verbert, K., et al.: Context-aware recommender systems for learning: a survey and future challenges. IEEE Trans. Learn. Technol. 5(4), 318–335 (2012)CrossRefGoogle Scholar
  6. 6.
    Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., Koper, R.: Recommender systems in technology enhanced learning. In: Ricci, F., Rokach, L., Shapira, B., Kantor, Paul B. (eds.) Recommender Systems Handbook, pp. 387–415. Springer, Boston, MA (2011).  https://doi.org/10.1007/978-0-387-85820-3_12CrossRefGoogle Scholar
  7. 7.
    Chen, W., et al.: A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web 17(2), 271–284 (2014).CrossRefGoogle Scholar
  8. 8.
    Yang, H., Meinel, C.: Content based lecture video retrieval using speech and video text information. IEEE Trans. Learn. Technol. 2, 142–154 (2014)CrossRefGoogle Scholar
  9. 9.
    Dessì, D., et al.: Bridging learning analytics and cognitive computing for big data classification in micro-learning video collections. Comput. Hum. Behav. (2018, in Press)Google Scholar
  10. 10.
    Kim, J., et al.: Understanding in-video dropouts and interaction peaks inonline lecture videos. In: Proceedings of the first ACM conference on Learning@ scale conference. ACM (2014)Google Scholar
  11. 11.
    Risko, E.F., et al.: The collaborative lecture annotation system (CLAS): a new TOOL for distributed learning. IEEE Trans. Learn. Technol. 6(1), 4–13 (2013)CrossRefGoogle Scholar
  12. 12.
    Welinder, P., et al.: The multidimensional wisdom of crowds. In: Advances in Neural Information Processing Systems (2010)Google Scholar
  13. 13.
    Cernea, D., Del Moral, E., Gayo, J.: SOAF: semantic indexing system based on collaborative tagging. Interdisc. J. E-Learn. Learn. Obj. 4(1), 137–149 (2008)Google Scholar
  14. 14.
    Niemann, K., Wolpers, M.: Usage context-boosted filtering for recommender systems in TEL. In: Hernández-Leo, D., Ley, T., Klamma, R., Harrer, A. (eds.) EC-TEL 2013. LNCS, vol. 8095, pp. 246–259. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40814-4_20CrossRefGoogle Scholar
  15. 15.
    Shu, J., et al.: A content-based recommendation algorithm for learning resources. Multimedia Syst. 24(2), 163–173 (2018)CrossRefGoogle Scholar
  16. 16.
    Ziegler, C.-N., et al.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web. ACM (2005)Google Scholar
  17. 17.
    Zhao, Q., Zhang, Y., Chen, J.: An improved ant colony optimization algorithm for recommendation of micro-learning path. In: 2016 IEEE International Conference on Computer and Information Technology (CIT). IEEE (2016)Google Scholar
  18. 18.
    Chen, M., et al.: Recommendation of learning path using an improved ACO based on novel coordinate system. In: 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE (2017)Google Scholar
  19. 19.
    Zhou, Y., et al.: Personalized learning full-path recommendation model based on LSTM neural networks. Inf. Sci. 444, 135–152 (2018)CrossRefGoogle Scholar
  20. 20.
    Wu, D., Lu, J., Zhang, G.: A fuzzy tree matching-based personalized e-learning recommender system. IEEE Trans. Fuzzy Syst. 23(6), 2412–2426 (2015)CrossRefGoogle Scholar
  21. 21.
    Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4), 19 (2016)Google Scholar
  22. 22.
    Fenza, G., Orciuoli, F., Sampson, D.G.: Building adaptive tutoring model using artificial neural networks and reinforcement learning. In: 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT). IEEE (2017)Google Scholar
  23. 23.
    Al-Hmouz, A., et al.: Modeling and simulation of an adaptive neuro-fuzzy inference system (ANFIS) for mobile learning. IEEE Trans. Learn. Technol. 5(3), 226–237 (2012)CrossRefGoogle Scholar
  24. 24.
    Dorça, F.A., et al.: An approach for automatic and dynamic analysis of learning objects repositories through ontologies and data mining techniques for supporting personalized recommendation of content in adaptive and intelligent educational systems. In: 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT). IEEE (2017)Google Scholar
  25. 25.
    Yang, T.-Y., et al.: Behavior-based grade prediction for MOOCs via time series neural networks. IEEE J. Sel. Topics Signal Process. 11(5), 716–728 (2017)Google Scholar
  26. 26.
    Brinton, C.G., et al.: Mining MOOC clickstreams: on the relationship between learner behavior and performance. arXiv preprint arXiv:1503.06489 (2015)
  27. 27.
    Brinton, C.G., Chiang, M.: MOOC performance prediction via clickstream data and social learning networks. In: 2015 IEEE Conference on Computer Communications (INFOCOM). IEEE (2015)Google Scholar
  28. 28.
    Chiang, M.: Networks: friends, money, and bytes, Princeton University (2012). https://www.coursera.org/course/friendsmoneybytes
  29. 29.
    Brinton, C., Chiang, M.: Networks illustrated: principles without calculus, Princeton University (2013). https://www.coursera.org/learn/networks-illustrated
  30. 30.
    Kórösi, G., et al.: Clickstream-based outcome prediction in short video MOOCs. In: 2018 International Conference on Computer, Information and Telecommunication Systems (CITS). IEEE (2018)Google Scholar
  31. 31.
    Shridharan, M.: et al.: Predictive learning analytics for video-watching behavior in MOOCs. In: 2018 52nd Annual Conference on Information Sciences and Systems (CISS). IEEE (2018)Google Scholar
  32. 32.
    Sinha, T., et al.: Your click decides your fate: inferring information processing and attrition behavior from MOOC video clickstream interactions. arXiv preprint arXiv:1407.7131 (2014)
  33. 33.
    Odersky, M.: Functional programming principles in scala (2012). https://www.coursera.org/learn/progfun1
  34. 34.
    Lopez, G., et al.: Google BigQuery for education: framework for parsing and analyzing edX MOOC data. In: Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale. ACM (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computing and Information TechnologyUniversity of WollongongWollongongAustralia
  2. 2.Research Lab of Electronics, Department of EE and CSMassachusetts Institute of TechnologyCambridgeUSA
  3. 3.UQ Business SchoolThe University of QueenslandBrisbaneAustralia
  4. 4.Faculty of Computer and Information ScienceSouthwest UniversityChongqingChina
  5. 5.School of Information System and ModellingUniversity of Technology SydneySydneyAustralia

Personalised recommendations