Learning Analytics for E-Book-Based Educational Big Data in Higher Education

  • Hiroaki OgataEmail author
  • Misato Oi
  • Kousuke Mohri
  • Fumiya Okubo
  • Atsushi Shimada
  • Masanori Yamada
  • Jingyun Wang
  • Sachio Hirokawa


This study provides an overview of the educational big data research project at Kyushu University, Japan. This project uses an e-book system called BookLooper, which allows students to browse e-books on web browsers, PCs (personal computers), and mobile devices such as smartphones and tablet PCs. This study reveals the research issues of this project. As of December 2015, approximately 20,000 students and 10,000 faculty staffs use the e-book system, and more than 4.7 million log data have been accumulated. This paper describes why the e-book system was introduced in university education and initial findings.


Learning analytics Educational big data Digital textbook 



The research is supported by “Research and Development on Fundamental and Utilization Technologies for Social Big Data” (178A03), the Commissioned Research of the National Institute of Information and Communications Technology (NICT), Japan; Grant-in-Aid for Scientific Research (S) No. 16H06304; Grant-in-Aid for Scientific Research (B) No. 25282059; Grant-in-Aid for Challenging Exploratory Research No. 26560122; Japan Science and Technology Agency (JST) PRESTO; and the Education Enhancement Program of Kyushu University.


  1. 1.
    Nakajima, T., Shinohara, S., Tamura, Y.: Typical functions of e-textbook, implementation, and compatibility verification with use of ePub3 materials. Procedia Comput. Sci. 22, 1344–1353 (2013)CrossRefGoogle Scholar
  2. 2.
    MEXT, Japanese Ministry of Education, Culture, Sports, Science and Technology.: The vision for ICT in education. (2011)
  3. 3.
    Shin, J.H.: Analysis on the digital textbook’s different effectiveness by characteristics of learner. Int. J. Educ. Learn. 1(2), 23–38 (2012)Google Scholar
  4. 4.
    Yin, C., Okubo, F., Shimada, A., Kojima, K., Yamada, M., Fujimura, N., Ogata, H.: Smart phone based data collecting system for analyzing learning behaviors. Proceedings of International Conference of Computers on Education 2014, Nara, Japan, pp. 575–577 (2014)Google Scholar
  5. 5.
    Fang, H., Liu, P., Huang, R.: The research on e-book-oriented mobile learning system environment application and its tendency. International Conference on Computer Science and Education, Singapore, pp. 1333–1338 (2011)Google Scholar
  6. 6.
    Ihmeideh, F.M.: The effect of electronic books on enhancing emergent literacy skills of pre-school children. Comput. Educ. 79, 40–48 (2014)CrossRefGoogle Scholar
  7. 7.
    Song, H.D., Jun, J.S., Ryu, J.H.: The Effects of Digital Textbooks in Student Learning. Seoul Metropolitan Board of Education, Seoul (2007)Google Scholar
  8. 8.
    Ausubel, D.P.: The Psychology of Meaningful Verbal Learning. Grune and Stratton, New York (1963)Google Scholar
  9. 9.
    Bonwell, C.C., Eison, J.A.: Active Learning: Creating Excitement in the Classroom, ASHE-ERIC Higher Education Report No1. The George Washington University, School of Education and Human Development, Washington, DC (1991)Google Scholar
  10. 10.
    Shinogaya, K.: Learning strategies: a review from the perspective of the relation between learning phases. Jpn. J. Educ. Psychol. 60, 92–105 (2012)CrossRefGoogle Scholar
  11. 11.
    Shinogaya, K.: Students’ strategies in preparation and lectures: direct and moderating effects of teachers’ teaching strategies. Jpn. J. Educ. Psychol. 62, 197–208 (2014)CrossRefGoogle Scholar
  12. 12.
    Woody, W.D., Daniel, D.B., Baker, C.A.: E-books or textbooks: students prefer textbooks. Comput. Educ. 55, 945–948 (2010)CrossRefGoogle Scholar
  13. 13.
    Oi, M., Okubo, F., Shimada, A., Yin, C., Ogata, H.: Analysis of preview and review patterns in Undergraduates’ e-book logs. Proceedings of ICCE 2015, Hangzhou, China, pp. 665–669 (2015)Google Scholar
  14. 14.
    Ogata, H., Yin, C., Oi, M., Okubo, F., Shimada, T., Kojima, K., Yamada, M.: Analyses of learning behavior of active learners using logs of digital teaching materials. Bull. KIKAN Educ. 2, 48–60 (2016)Google Scholar
  15. 15.
    Okubo, F., Shimada, A., Yin, C., Ogata, H.: Visualization and prediction of learning activities by using discrete graphs. Proceedings of ICCE2015, Hangzhou, China, pp. 739–744 (2015)Google Scholar
  16. 16.
    Hlosta, M., Herrmannová, D., Váchová, L., Kužílek, J., Zdrahal, Z., Wolff, A.: Modelling student online behaviour in a virtual learning environment. Workshop Proc. LAK 2014, Indianapolis, USA (2014)Google Scholar
  17. 17.
    Norris J.R.: Markov Chains. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, Cambridge, UK (1998).Google Scholar
  18. 18.
    Okubo, F., Hirokawa, S., Oi, M., Shimada, A., Kojima, K., Yamada, M., Ogata, H.: Learning activity features of high performance students. Proceedings of Cross-LAK2016, Edingburgh, UK, pp. 24–29 (2016)Google Scholar
  19. 19.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)zbMATHGoogle Scholar
  20. 20.
    Yamada, M., Yin, C., Shimada, A., Kojima, K., Okubo, F., Ogata, H.: Preliminary research on self-regulated learning and learning logs in a ubiquitous learning environment. Proceedings of the 15th IEEE International Conference on Advanced Learning Technologies (ICALT 2015), Hualien, Taiwan, pp. 93–95 (2015)Google Scholar
  21. 21.
    Goda, Y., Yamada, M., Matsuda, T., Kato, H., Saito, Y., Miyagawa, H.: Effects of help seeking target types on completion rate and satisfaction in e-learning. Proceedings of INTED 2013, Valencia, Spain, pp. 1399–1403 (2013)Google Scholar
  22. 22.
    Goda, Y., Yamada, M., Matsuda, T., Saito, Y., Kato, H., Miyagawa, H.: Procrastination and other learning behavioral types in e-learning and their relationship with learning outcomes. Learn. Individ. Differ. 37, 72–80 (2015). doi: 10.1016/j.lindif.201411.001 CrossRefGoogle Scholar
  23. 23.
    Pintrich, R.R., DeGroot, E.V.: Motivational and self-regulated learning componentsof classroom academic performance. J. Educ. Psychol. 82, 33–40 (1990) CrossRefGoogle Scholar
  24. 24.
    Ausubel, D.P.: Educational Psychology: A Cognitive View. Holt, New York (1968)Google Scholar
  25. 25.
    Ausubel, D.P., Novak, J.D., Hanesian, H.: Educational Psychology: A Cognitive View, 2nd edn. Holt, Rinehart and Winston, New York (1978)Google Scholar
  26. 26.
    Novak, J.D.: Meaningful learning: the essential factor for conceptual change in limited or appropriate propositional hierarchies (liphs) leading to empowerment of learners. Sci. Educ. 86(4), 548–571 (2002)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Lee, J.H., Segev, A.: Knowledge maps for e-learning. Comput. Educ. 59(2), 353–364 (2012)CrossRefGoogle Scholar
  28. 28.
    Wang, J., Mendori, T., Juan, X.A.: Language learning support system using course-centered ontology and its evaluation. Comput. Educ. 78, 278–293 (2014)CrossRefGoogle Scholar
  29. 29.
    Wang, J., Mendori, T., Xiong, J.A.: Customizable language learning support system using ontology-driven engine. Int. J. Dist. Educ. Technol. 11(4), 81–96 (2013)CrossRefGoogle Scholar
  30. 30.
    Mouri, K., Okubo, F., Shimada, A., Ogata, H.: Profiling high-achieving students using e-book-based logs. Proc. of the first international workshop on Learning Analytics and Knowledge (LAK 16), Edingburgh, UK, pp. 1–6 (2016)Google Scholar
  31. 31.
    Shimada, A., Okubo, F., Yin, C., Kojima, K., Yamada, M., Ogata, H.: Informal learning behavior analysis using action logs and slide features in e-textbooks. Proceedings of IEEE International Conference on Advanced Learning Technologies, Hualien, Taiwan, pp. 116–117 (2015)Google Scholar
  32. 32.
    Yin, C., Okubo, F., Shimada, A., Oi, M., Hirokawa, S., Yamada, M., Kojima, K., Ogata, H.: Analyzing the features of learning behaviors of students using e-books. Workshop proceedings of International Conference on Computers in Education 2015, Hangzhou, China, pp. 617–626 (2015)Google Scholar
  33. 33.
    Mouri, K., Ogata, H., Uosaki, N., Liu, S.: Visualization for analyzing ubiquitous learning logs. Proceedings of International Conference on Computers in Education (ICCE 2014), Nara, Japan, pp. 461–470 (2014)Google Scholar
  34. 34.
    Mouri, K., Ogata, H.: Ubiquitous learning analytics in the real-world language learning. Smart Learn. Environ. 2(15), 1–18 (2015)Google Scholar
  35. 35.
    Ogata, H., Li, M., Bin, H., Uosaki, N., El-Bishoutly, M., Yano, Y.: SCROLL: supporting to share and reuse ubiquitous learning logs in the context of language learning. Res. Pract. Technol. Enhanc. Learn. 6(3), 69–82 (2011)Google Scholar
  36. 36.
    Ogata, H., Bin, H., Li, M., Uosaki, N., Mouri, K., Liu, S.: Ubiquitous learning project using life-logging technology in Japan. Educ. Technol. Soc. J. 17(2), 85–100 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hiroaki Ogata
    • 1
    Email author
  • Misato Oi
    • 1
  • Kousuke Mohri
    • 1
  • Fumiya Okubo
    • 1
  • Atsushi Shimada
    • 1
  • Masanori Yamada
    • 1
  • Jingyun Wang
    • 1
  • Sachio Hirokawa
    • 1
  1. 1.Learning Analytics CenterKyushu UniversityFukuokaJapan

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