Smart Learning Analytics

  • David Boulanger
  • Jeremie Seanosky
  • Vive Kumar
  • Kinshuk
  • Karthikeyan Panneerselvam
  • Thamarai Selvi Somasundaram
Conference paper
Part of the Lecture Notes in Educational Technology book series (LNET)

Abstract

A smart learning environment (SLE) is characterized by the key provision of personalized learning experiences. To approach different degrees of personalization in online learning, this paper introduces a framework called SCALE that tracks finer level learning experiences and translates them into opportunities for custom feedback. A prototype version of the SCALE system has been used in a study to track the habits of novice programmers. Growth of coding competencies of first year engineering students has been captured in a continuous manner. Students have been provided with customized feedback to optimize their learning path in programming. This paper describes key aspects of our research with the SCALE system and highlights results of the study.

Keywords

SCALE framework Smart learning environment Programming e-Learning technologies Novice programming Big data learning analytics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Burghardt, C., Reisse, C., Heider, T., Giersich, M. & Kirste, T. (2008). Implementing Scenarios in a Smart Learning Environment. 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (pp. 377-382).Google Scholar
  2. Kim, S., Song, S.-M. & Yoon, Y.-I. (2011). Smart Learning Services Based on Smart Cloud Computing. Sensors 11, 7835-7850.Google Scholar
  3. Aion, N., Helmandollar, L., Wang, M. & Ng, J. (2012). Intelligent Campus (iCampus) Impact Study. 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT), Vol. 3 (pp. 291-295).Google Scholar
  4. Lee, J., Jung, Y. J., Park, S. R., Yu, J., Jin, D.-s. & Cho, K. (2012). A Ubiquitous Smart Learning Platform for the 21st Smart Learners in an Advanced Science and Engineering Education. 2012 15th International Conference on Network-Based Information Systems (NBiS) (pp. 733-738).Google Scholar
  5. Huang, R., Hu, Y., Yang, J. & Xiao, G. The Functions of Smart Classroom in Smart Learning Age.Google Scholar
  6. Cioara, T., Anghel, I., Salomie, I., Dinsoreanu, M., Copil, G. & Moldovan, D. (2010). A selfadapting algorithm for context aware systems. 2010 Ninth Roedunet International Conference (RoEduNet) (pp. 374-379).Google Scholar
  7. Koo, D.-H. (2012). Trends and Revitalization of Smart-Learning in Elementary and Middle Schools. Asian Journal of Information Technology, 160-168.Google Scholar
  8. Mikulecký, P. (2012). Smart Environments for Smart Learning.Google Scholar
  9. Yu, Z., Zhou, X. & Shu, L. (2009). Towards a semantic infrastructure for context-aware e-learning. (pp. 71-86).Google Scholar
  10. Yu, Z., Nakamura, Y., Jang, S., Kajita, S. & Mase, K. (2007). Ontology-Based Semantic Recommendation for Context-Aware E-Learning. (pp. 898-907).Google Scholar
  11. Akiyoshi, M. & Nishida, S. (1993). A qualitative simulation-based learning environment: how to enhance causal understanding of complex phenomena in large-scale plants. 1993 Twelfth Annual International Phoenix Conference on Computers and Communications (pp. 531-537).Google Scholar
  12. Blikstein, P. (2011). Using Learning Analytics to Assess Students’ Behavior in Open-ended Programming Tasks. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 110-116). ACM.Google Scholar
  13. Castro, F., Mugica, F. & Nebot, A. (2007). Causal Relevancy Approaches to Improve the Students’ Prediction Performance in an e-Learning Environment. 2007 Sixth Mexican International Conference on Artificial Intelligence - Special Session (pp. 342-351).Google Scholar
  14. Fancsali, S. E. (2011). Variable Construction for Predictive and Causal Modeling of Online Education Data. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 54-63). ACM.Google Scholar
  15. Santos, J. L., Govaerts, S., Verbert, K. & Duval, E. (2012). Goal-oriented Visualizations of Activity Tracking: A Case Study with Engineering Students. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 143-152). ACM.Google Scholar
  16. Yu, T. & Jo, I.-H. (2014). Educational Technology Approach Toward Learning Analytics: Relationship Between Student Online Behavior and Learning Performance in Higher Education. In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (pp. 269-270). ACM.Google Scholar
  17. Kosba, E., Dimitrova, V., Boyle, R. (2005). Using Student and Group Models to Support Teachers in Web-Based Distance Education. In Proceedings of the 10th International Conference on User Modeling, Edinburgh, UK (pp. 124-133).Google Scholar
  18. Lee, S., Barker, T., Kumar, V. (2014). Self-Directed Learning. Educational Technology & Society, in preparation.Google Scholar
  19. Lee, S., Barker, T., Kumar, V. (2011). Learning Preferences and Self-Regulation - Design of a Learner-Directed e-Learning Model. International Conferences ASEA, DRBC and EL 2011, Software Engineering, Business Continuity and Education. Communications in Computer and Information Science, Springer Link, Volume 257, pp. 579-589.Google Scholar
  20. Lee, S., Barker, T., Kumar, V. (2011). Models of eLearning: The Development of a Learner- Directed Adaptive eLearning System. In Sue Greener and Asher Rospigliosi (eds.), Proceedings of the European conference on e-learning held in University of Brighton, Brighton, UK, November 10-11, 2011 (pp. 390-398).Google Scholar
  21. Lee, S., Barker, T., Kumar, V. (2010). Approaches to Student Modeling in the Context of e-Learning 2.0. In Paula Escudeiro (ed.), Proceeding of the 9th European conference on e-learning, pp. 59-66.Google Scholar
  22. Kumar, V., Manimalar, P., Somasundaram, T. S., Sidhan, M., Lee, S., El-Kadi, M. (2009). Open Instructional Design. Technology for Education (T4E 2009) (pp.43-50).Google Scholar
  23. Seanosky, J., Boulanger, D., Kumar, V. & Kinshuk (2014). Unfolding Learning Analytics for Big Data. ICSLE 2014, in preparation.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • David Boulanger
    • 1
  • Jeremie Seanosky
    • 1
  • Vive Kumar
    • 1
  • Kinshuk
    • 1
  • Karthikeyan Panneerselvam
    • 2
  • Thamarai Selvi Somasundaram
    • 2
  1. 1.Athabasca UniversityAthabascaCanada
  2. 2.Anna UniversityChennaiIndia

Personalised recommendations