Advertisement

Supporting Seamless Learning with a Learning Analytics Approach

  • Noriko UosakiEmail author
  • Kousuke Mouri
  • Mahiro Kiyota
  • Hiroaki Ogata
Chapter
Part of the Lecture Notes in Educational Technology book series (LNET)

Abstract

This chapter explores a learning analytics approach for the implementation of a seamless learning environment. Our developed seamless learning system called VASCORLL (Visualization and Analysis System for COnnecting Relationships of Learning Logs) and AETEL (Actions and learning on E-TExtbook Logging) System will be described. AETEL, an eBook system, was implemented on top of SCROLL, another developed system of ours. VASCORLL was also implemented on SCROLL. The objectives of VASCORLL are for visualizing and analyzing the learning logs collected from SCROLL to provide learners with more learning opportunities as well as to link eBook learning and real-life learning. Two types of visualization structures, eBook learning structure and real-life learning structure, were adapted. Our research questions are (1) Is VASCORLL able to enhance learners’ learning opportunities? (2) Does it facilitate finding important words in the seamless learning environment? and (3) Which centrality is the most effective in supporting learning in the seamless learning environment? From an evaluation experiment conducted at a university in Japan, it was found that VASCORLL enhanced learning opportunities and that it was a useful tool to find central words bridging eBook learning and real-life learning when it was based on betweenness centrality. VASCORLL had a high usability, and it contributed to enhancing learners’ learning opportunities in a way that it helped users link their eBook learning with a real-life learning as well as link their learning with that of other users. The system is expected to play an important role as a tool that links both learning modes.

Keywords

eBook Learning analytics Mobile-assisted language learning Network graph Seamless learning Social network analysis 

Notes

Acknowledgements

Part of this research work was supported by PRESTO from Japan Science and Technology Agency, and the grant-in-aid for Scientific Research No. 16H06304 and No. 17K12947 from the Ministry of Education, Science, Sports, and Culture in Japan.

References

  1. Baddeley, A. D. & Hitch, G. (1974). Working memory. In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory, 8, 47–89. New York: Academic Press.Google Scholar
  2. Baker, R. S., Corbett, A. T., & Koedinger, K. R. (2004). Detecting student misuse of intelligent tutoring systems. Proceedings of the 7th International Conference on Intelligent Tutoring Systems (pp. 531–540).Google Scholar
  3. Baker, R. S. J. D., de Carvalho, A. M. J. A., Raspat, J., Aleven, V., Corbett, A. T., & Koedinger, K. R. (2009). Educational software features that encourage and discourage “gaming the system”. Proceedings of the 14th International Conference on Artificial Intelligence in Education (pp. 475–482).Google Scholar
  4. Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Lárusson & B. White (Eds.), Learning analytics: From research to practice, computer-supported collaborative learning series (pp. 61–75). New York, NY: Springer.Google Scholar
  5. Baker, R., & Siemens, G. (2013). Educational data mining and learning analytics. In R. K. Sawyer (Ed.), Cambridge handbook of the learning sciences (2nd ed., pp. 253–274). Cambridge: Cambridge University Press.Google Scholar
  6. Barnes, J., & Hut, P. (1986). A hierarchical O(N log N) force calculation algorithm. Nature, 324(4), 446–449.CrossRefGoogle Scholar
  7. Beck, J. E., Chang, K.-M., Mostow, J., & Corbett, A. T. (2008). Does help help? Introducing the Bayesian evaluation and assessment methodology. Proceedings of Intelligent Tutoring Systems, ITS 2008 (pp. 383–394).Google Scholar
  8. Bowers, A. J. (2010). Analyzing the longitudinal K-12 grading histories of entire cohorts of students: Grades, data driven decision making, dropping out and hierarchical cluster analysis. Practical Assessment, Research & Evaluation (PARE), 15(7), 1–18.Google Scholar
  9. Chai, C., Wong, L., & King, R. (2016). Surveying and modeling students’ motivation and learning strategies for mobile-assisted seamless Chinese language learning. Educational Technology & Society, 19(3), 170–181.Google Scholar
  10. Chan, T.-W., Roschelle, J., Hsi, S., Kinshuk, Sharples, M., Brown, T., … Hoppe, U. (2006). One-to-one technology-enhanced learning: An opportunity for global research collaboration, Research and Practice of Technology Enhanced Learning, 1(1), 3–29.CrossRefGoogle Scholar
  11. Ellis, R. (2000). Task-based research and language pedagogy. Language Teaching Research, 4(3), 193–220.CrossRefGoogle Scholar
  12. Ferguson, R. (2012). The state of learning analytics in 2012: A review and future challenges, technical report KMI-12-01. Knowledge Media Institute, The Open University UK. Google Scholar
  13. Ferguson, R. & Shum, S. B. (2012). Social learning analytics: Five approaches. In The Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK’12) (pp. 23–33).Google Scholar
  14. Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1, 215–239.CrossRefGoogle Scholar
  15. Fruchterman, M. E. J., & Reingold, E. M. (1991). Graph drawing by force-directed placement. Software: Practice and Experience, 21(11), 1129–1164.Google Scholar
  16. Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360–1380.  https://doi.org/10.1086/225469.CrossRefGoogle Scholar
  17. Hu, Y. F., & Scolt, J. A. (2001). A multilevel algorithm for wavefront reduction. SIAM Journal on Scientific Computing (SISC), 23(4), 1352–1375.CrossRefGoogle Scholar
  18. Johnson, I., & Wilson, A. (2009). The time map project: Developing time-based GIS display for cultural data. Journal of GIS in Archaeology, 1, 123–135.Google Scholar
  19. Kiyota, M., Mouri, K., Uosaki, N., & Ogata, H. (2016). AETEL: Supporting seamless learning and learning log recording with e-book system. In Proceedings of the 24th International Conference on Computers in Education (ICCE 2016) (pp. 380–385).Google Scholar
  20. Latora, V., & Marchiori, M. (2004). A measure of centrality based on the network efficiency, pp. 1–16. arXiv:cond-mat/0402050v1.
  21. Li, M., Ogata, H., Hou, B., & Uosaki, N. (2013). Context-aware and personalization method in ubiquitous learning log system. Journal of Educational Technology & Society (SSCI), 16(3), 362–373.Google Scholar
  22. Looi, C. K., Sun, D., & Xie, W. (2015). Exploring students’ progression in an inquiry science curriculum enabled by mobile learning. IEEE Transactions on Learning Technologies, 8(1), 43–54.CrossRefGoogle Scholar
  23. Mathieu, J., Tommaso, V., Sebastien, H., & Mathieu, B. (2014). Force Atlas2, a continuous graph layout algorithm for handy network visualization designed for the gephi software. PLOS One, 9(6). Google Scholar
  24. Mazza, R. (2009). Introduction to information visualization. London: Springer.Google Scholar
  25. Milrad, M, Wong, L. H., Sharple, M., Hwang, G. J., Looi, C. K., & Ogata, H. (2013). Seamless learning: An international perspective on next generation technology enhanced learning. In Z. L. Berge & L. Y. Muilenburg (Eds.), Handbook of mobile learning (Chapter 9) (pp. 95–108).Google Scholar
  26. Mouri, K., & Ogata, H. (2015). Ubiquitous learning analytics in the real-world language learning. Smart Learning Environment, 2(15), 1–18.Google Scholar
  27. Mouri, K., Ogata, H., & Uosaki, N. (2017). Learning analytics in a seamless learning environment. In Proceedings of the 7th International Learning Analytics and Knowledge (LAK) Conference, Vancouver, Canada, 13–17 March 2017 (pp. 348–357).Google Scholar
  28. Noack, A. (2009). Modularity clustering is force-directed layout. Physical Review E, 79(2), 1–8.CrossRefGoogle Scholar
  29. Ogata, H., Hou, B., Li, M., Uosaki, N., Mouri, K., & Liu, S. (2014). Ubiquitous learning project using life-logging technology in Japan. Educational Technology and Society Journal, 17(2), 85–100.Google Scholar
  30. Sabourin, J., Rowe, J., Mott, B., & Lester, J. (2011). When off-task in on-task: The affective role of off-task behavior in narrative-centered learning environments. Proceedings of the 15th International Conference on Artificial Intelligence in Education (pp. 534–536).Google Scholar
  31. Siemens, G. (2011, August 5). Learning and academic analytics. http://www.learninganalytics.net/?p=131.
  32. Siemens, G., (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400.  https://doi.org/10.1177/0002764213498851.CrossRefGoogle Scholar
  33. Uosaki, N., Ogata, H., Sugimoto, T., Li, M., & Hou, B. (2012). Towards seamless vocabulary learning: How we can entwine in-class and outside-of-class learning. International Journal of Mobile Learning and Organization, 6(2), 138–155.CrossRefGoogle Scholar
  34. Uosaki, N., Ogata, H., Li, M., Hou, B., & Mouri, K. (2013). Guidelines on implementing successful seamless learning environments: A practitioners’ perspective. International Journal of Interactive Mobile Technologies, 7(2), 44–53.Google Scholar
  35. Wei, L. (2012). Construction of seamless English language learning cyberspace via interactive text messaging tool. Theory and Practice in Language Studies, 2(8), 1590–1596.CrossRefGoogle Scholar
  36. Wong, L. H., Chai, C. S., Zhang, X., & King, R. B. (2015). Employing the TPACK framework for researcher-teacher codesign of a mobile-assisted seamless language learning environment. IEEE Transactions on Learning Technologies, 8(2), 31–42.CrossRefGoogle Scholar
  37. Wong, L. H., & Looi, C. K. (2011). What seams do we remove in mobile-assisted seamless learning? A critical review of the literature. Computer & Education, 57, 2364–2381.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Noriko Uosaki
    • 1
    Email author
  • Kousuke Mouri
    • 2
  • Mahiro Kiyota
    • 3
  • Hiroaki Ogata
    • 4
  1. 1.Center for International Education and ExchangeOsaka UniversitySuita, OsakaJapan
  2. 2.Department of Computer and Information SciencesTokyo University of Agriculture and TechnologyKoganei, TokyoJapan
  3. 3.NTT Data Kyushu CorporationKyushu UniversityFukuokaJapan
  4. 4.Academic Center for Computing and Media StudiesKyoto UniversityKyotoJapan

Personalised recommendations