Analysis of New Data Sources in Modern Teaching and Learning Processes in the Perspective of Personalized Recommendation

  • G. M. Shivanagowda
  • R. H. Goudar
  • U. P. Kulkarni
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 31)


The increased variety of learning resources have substantially affected learning styles of students, like e-books with modern collaborative tools, video lectures of different teachers across the world, lively discussion boards etc. Having accepted such forms of learning materials, teaching and learning processes in conventional set up do not have a way to capture the data generated out of students’ learning activities involving such resources and use them effectively. This paper analyses data generated by the student’s activities in Compiler of Resources in Engineering and Technology to Aid Learning (CRETAL ) restricted to video resources and asserts that they are indeed critically helpful data for teachers/tutoring systems in generating personalised recommendations which are possible only because of said data. CRETAL is the modern learning station, an intelligent system, being developed at author’s institution to facilitate variety of learning resources created and adapted by the faculty and the teachers worldwide to students.


Learning data Recommendation system Personalised learning Collaborative learning Video learning resources 


  1. 1.
    Yang, H., Gr¨unewald, F., Bauer, M., Meinel, C., Lecture Video Browsing Using Multimodal Information Resources, pp. 204–213. Springer, ICWL, Heidelberg (2013)Google Scholar
  2. 2.
    Riegler, M., Lux, M., Charvillat, V., Carlier, A., Vliegendhart, R., Larson, M.: VideoJot—A Multifunctional Video Annotation Tool, pp. 534–538. ACM, ICMR (2014)Google Scholar
  3. 3.
    Jeremic, Z., Jovanovic, J., Gaševic, D.: Student modeling and assessment in intelligent tutoring of software patterns. J. Expert Syst. Appl. 39, 210–222 (2012) (Elsevier)Google Scholar
  4. 4.
    Choi, C.R., Song, Y.J., Jeong, H.Y.: Personalized learning course planner with E-learning DSS using user profile. J. Expert Syst. Appl. 39, 2567–2577 (2012) (Elsevier)Google Scholar
  5. 5.
    ZOSMAT: Web-based intelligent tutoring system for teaching–learning process. J. Expert Syst. Appl. 36, 1229–1239 (2009) (Elsevier)Google Scholar
  6. 6.
    Chen, C.M.: Personalized E-learning system with self-regulated learning assisted mechanisms for promoting learning performance. J. Expert Syst. Appl. 36, 8816–8829 (2009)Google Scholar
  7. 7.
    Mulwa, C., Lawless, S., Sharp, M.: Inmaculada Arnedillo-Sanchez and Vincent Wade, Adaptive educational hypermedia systems in technology enhanced learning: A Literature Review, pp. 73–84. ACM, Information Technology Education, New York (2010)Google Scholar
  8. 8.
    Klamma, R.: Community Learning Analytics—Challenges and Opportunities, pp. 284–293. Springer, Heidelberg (2014)Google Scholar
  9. 9.
    Felder, R.M., Brent, R.: Understanding student differences. J. Eng. Edu. 94, 57–72 (2005)Google Scholar
  10. 10.
    Wang, K.H., Wang, T.H., Wang, W.L., Huang, S.C.: Learning styles and formative assessment strategy: enhancing student achievement in web-based learning. J. Comp. Assist. Learn. 22, 207–217 (2006)Google Scholar
  11. 11.
    Chrysafiadi, K., Virvou, M.: PeRSIVA: An empirical evaluation method of a student model of an intelligent e-learning environment for computer programming. J. Comp. Edu. 68, 322–333 (2013) (Elsevier)Google Scholar
  12. 12.
    Antal, M., Koncz, S.: Student modeling for a web-based self assessment system. J. Expert Syst. Appl. 38, 6492–6497 (2011) (Elsevier)Google Scholar
  13. 13.
    Graf, S., Viola, S.R.: Kinshuk: Automatic Student Modelling for Detecting Learning Style Preferences in Learning Management Systems, pp. 72–179. IADIS, Algarve (2007)Google Scholar
  14. 14.
    Shivanagowda, G.M., Goudar, R.H., Kulakrni, U.P.: Open assessment method for better understanding of student’s learnabilty to create personalised recommendations. In: Proceedings of CTIEE, Springer, Heidelberg (2014) (in press)Google Scholar
  15. 15.
    Devedžić: A survey of modern knowledge modeling techniques. J. Expert Syst. Appl. 17, 275–294 (1999) (Elsevier)Google Scholar
  16. 16.
    Costello, R., Mundy, D.P.: The Adaptive Intelligent Personalised Learning Environment, IEEE, Advanced Learning Technologies, pp. 606–610 (2009)Google Scholar
  17. 17.
    Wanga, S.L., Wub, C.Y.: Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system. J. Expert Syst. Appl. 38 10831–10838 (2011) (Elsevier)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • G. M. Shivanagowda
    • 1
  • R. H. Goudar
    • 2
  • U. P. Kulkarni
    • 1
  1. 1.Department of Computer Science and EngineeringSDMCETDharwadIndia
  2. 2.Department of Computer Network EngineeringVisvesvaraya Technological UniversityBelgaumIndia

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