Unfolding Learning Analytics for Big Data

  • Jeremie SeanoskyEmail author
  • David Boulanger
  • Vivekanandan Kumar
  • Kinshuk
Conference paper
Part of the Lecture Notes in Educational Technology book series (LNET)


Educational applications, in general, treat disparate study threads as a singular entity, bundle pedagogical intervention and other student support services at a coarser level, and summatively assess final products of assessments. In this research, we propose an analytics framework where we closely monitor individual threads of study habits and assess study threads in an individual fashion to trace learning processes leading into assessment products. We developed customized intervention to target specific skills and nurture optimal study habits. The framework has been implemented in a system called SCALE (Smart Causal Analytics on LEarning). SCALE enables the tracking of students’ individual study threads towards multiple final study products. The large volume, multiple variety, and incessant flow of data classifies our work in the realms of big data analytics. We conducted a preliminary study using SCALE. The results show the ability of the system to track the evolution of competencies. We propose that explicitly supporting the development of a targeted set of competencies is one of the key tenets of Smart Learning Environments.


Learning management systems Programming Coding competency Bigdata Learning traces Learning analytics Training 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. G. Siemens, D. Gasevic, C. Haythornthwaite, S. Dawson, S. B. Shum, R. Ferguson, E. Duval, K. Verbert, and R. S. J. D. Baker (2011). Open Learning Analytics: an integrated & modularized platform, SOLAR (Society for Learning Analytics Research).Google Scholar
  2. IMS Global Learning Consortium (2013). Caliper Learning Analytics Framework and associated Sensor API. IMS Global Learning Consortium, Lake Mary, FL.Google Scholar
  3. Dawson, S., Bakharia, A., & Heathcote, E. (2010). SNAPP: Realising the affordances of real-time SNA within networked learning environments. Networked Learning – Seventh International Conference, Aalborg, Denmark.Google Scholar
  4. del Blanco, A., Serrano, A., Freire, M., Martinez-Ortiz, I. & Fernandez-Manjon, B. (2013). ELearning standards and learning analytics. Can data collection be improved by using standard data models?. In Global Engineering Education Conference (EDUCON), 2013 IEEE (pp. 1255-1261).Google Scholar
  5. Bakharia, A. & Dawson, S. (2011). SNAPP: a bird’s-eye view of temporal participant interaction. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 168–173). ACM. (ISBN: 978-1-4503-0944-8.)Google Scholar
  6. Vivekananthamoorthy, N., Sankar, S., Siva, R. & Sharmila, S. (2009). An effective E-learning framework model - a case study. In ICT and Knowledge Engineering, 2009 7th International Conference on (pp. 8-14).Google Scholar
  7. Haythornthwaite, C., de Laat, M., Dawson, S. & Suthers, D. (2013). Introduction to Learning Analytics and Networked Learning Minitrack. In System Sciences (HICSS), 2013 46th Hawaii International Conference on (pp. 3077-3077).Google Scholar
  8. Cheng, H.-C. & Liao, W.-W. (2012). Establishing an lifelong learning environment using IOT and learning analytics. In Advanced Communication Technology (ICACT), 2012 14th International Conference on (pp. 1178-1183).Google Scholar
  9. Aljohani, N. & Davis, H. (2012). Significance of Learning Analytics in Enhancing the Mobile and Pervasive Learning Environments. In Next Generation Mobile Applications, Servies and Technologies (NGMAST), 2012 6th International Conference on (pp. 70-74).Google Scholar
  10. Vozniuk, A., Govaerts, S. & Gillet, D. (2013). Towards Portable Learning Analytics Dashboards. In Advanced Learning Technologies (ICALT), 2013 IEEE 13th International Conference on (pp. 412-416).Google Scholar
  11. Ali, L., Hatala, M., Gasevic, D. & Jovanovic, J. (2012). A qualitative evaluation of evolution of a learning analytics tool. Computers & Education 58 (1), 470 - 489.Google Scholar
  12. Arnold, K. E. & Pistilli, M. D. (2012). Course Signals at Purdue: Using Learning Analytics to Increase Student Success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 267–270). ACM. (ISBN: 978-1-4503-1111-3.)Google Scholar
  13. Elias, T. (2011). Learning Analytics: Definitions, Processes and Potential. Retrieved from
  14. Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning 4 (5/6), 304-317.Google Scholar
  15. Rahman, N. & Dron, J. (2012). Challenges and Opportunities for Learning Analytics when Formal Teaching Meets Social Spaces. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 54–58). ACM. (ISBN: 978-1-4503-1111-3.)Google Scholar
  16. Ferguson, R. & Shum, S. B. (2012). Social Learning Analytics: Five Approaches. In Proceedings of the 2Nd International Conference on Learning Analytics and Knowledge (pp. 23–33). ACM. (ISBN: 978-1-4503-1111-3.)Google Scholar
  17. Verbert, K., Duval, E., Klerkx, J., Govaerts, S. & Santos, J. L. (2013). Learning Analytics Dashboard Applications. American Behavioral Scientist 57 (10), 1500-1509. (Doi: 0.1177/0002764213479363.)Google Scholar
  18. Verbert, K., Govaerts, S., Duval, E., Santos, J., Assche, F., Parra, G. & Klerkx, J. (2013). Learning dashboards: an overview and future research opportunities. Personal and Ubiquitous Computing, 1-16. (Doi:  10.1007/s00779-013-0751-2.)
  19. Winne P., Nesbit J., Kumar V., Hadwin A., Lajoie S., Azevedo R., Perry N., (2006), Supporting Self-Regulated Learning with gStudy Software: The LearningKit Project, International Journal of Technology, Instruction, Cognition, and Learning, Vol. 3, PP. 105-113.Google Scholar
  20. Kumar V., Chang, M., Leacock T. (2011), Ubiquitous Writing: Writing Technologies for Situated Mediation and Proactive Assessment, Ubiquitous Learning: An International Journal, Volume 3, Issue 3, pp.173-188.Google Scholar
  21. Kumar V., Winne P.H., Hadwin A.F., Nesbit J.C., Jamieson-Noel D., Calvert T., Samin B., Effects of self-regulated learning in programming, IEEE International Conference on Advanced Learning Technologies (ICALT 2005), Kaohsiung, Taiwan, 5-8 July, 383 - 387, 2005.Google Scholar
  22. Boulanger, D., Seanosky, J., Kumar, V., Kinshuk, Panneerselvam, K. & Somasundaram, T. S. (2014). Smart Learning Analytics.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Jeremie Seanosky
    • 1
    Email author
  • David Boulanger
    • 1
  • Vivekanandan Kumar
    • 1
  • Kinshuk
    • 1
  1. 1.School of Computing and Information SystemsAthabasca UniversityAthabascaCanada

Personalised recommendations