You are here:

Learning Analytics: Principles and Constraints PROCEEDINGS

, , Graz University of Technology, Austria

AACE Award

EdMedia + Innovate Learning, in Montreal, Quebec, Canada ISBN 978-1-939797-16-2 Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC


Within the evolution of technology in education, Learning Analytics has reserved its position as a robust technological field that promises to empower instructors and learners in different educational fields. The 2014 horizon report (Johnson et al., 2014), expects it to be adopted by educational institutions in the near future. However, the processes and phases as well as constraints are still not deeply debated. In this research study, the authors talk about the essence, objectives and methodologies of Learning Analytics and propose a first prototype life cycle that describes its entire process. Furthermore, the authors raise substantial questions related to challenges such as security, policy and ethics issues that limit the beneficial appliances of Learning Analytics processes.


Khalil, M. & Ebner, M. (2015). Learning Analytics: Principles and Constraints. In S. Carliner, C. Fulford & N. Ostashewski (Eds.), Proceedings of EdMedia 2015--World Conference on Educational Media and Technology (pp. 1789-1799). Montreal, Quebec, Canada: Association for the Advancement of Computing in Education (AACE). Retrieved August 21, 2018 from .

View References & Citations Map


  1. Anciaux, N., Bouganim, L., & Pucheral, P. (2006). Data confidentiality: to which extent cryptography and secured hardware can help. In Annales des télécommunications (Vol. 61, No. 3-4, pp. 267-283). Springer-Verlag.
  2. 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 (LAK’12) (pp. 267-270). New
  3. 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 (LAK’11) (pp. 168-173). New York, USA: ACM.
  4. Castro, F., Vellido, A., Nebot, A., & Mugica, F. (2007). Applying data mining techniques to e-learning problems. In L.C. Jain, T. Raymond& D. Tedman (Eds.), Evolution of teaching and learning paradigms in intelligent environment (Vol. 62, pp. 183-221). Berlin: Springer-Verlag.
  5. Chatti, M.A., Dyckhoff, A.L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5/6), 318–331.
  6. Chen, L., & Wang, G. (2008). An efficient piecewise hashing method for computer forensics. In Knowledge Discovery and Data Mining, 2008. WKDD 2008. First International Workshop (pp. 635-638). IEEE.
  7. Clow, D. (2012). The learning analytics cycle: closing the loop effectively. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK’12) (pp. 134-138). New York, USA: ACM.
  8. Cooper, A. (2012). A Framework of Characteristics for Analytics. CETIS Analytics Series, 1(7).
  9. Cooper, A. (2013). Learning Analytics Interoperability– some thoughts on a“way ahead” to get results sometime soon. Retrieved 10 November 2014 from
  10. Dawson, S., Gašević, D., Siemens, G. & Joksimovic, S. (2014). Current state and future trends: a citation network analysis of the learning analytics field. In: Proceedings of the Fourth International Conference on Learning Analytics& Knowledge (LAK’14) (pp. 231-240). New York, USA: ACM.
  11. Duval, E. (2011). Attention please! Learning analytics for visualization and recommendation. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (LAK ’11) (pp. 9–17). New York, USA: ACM.
  12. Ebner, M., & Schön, M. (2013). Why Learning Analytics in Primary Education Matters. Bulletin of the Technical Committee on Learning Technology, Karagiannidis, C. & Graf, S (Ed.), 15(2), 14-17.
  13. Edwards, G. (2010). Mixed-method approaches to social network analysis. ESRC National Centre for Research Methods Review paper NCRM/015. National Centre for Research Methods.
  14. Elias, T. (2011). Learning Analytics: Definitions, Processes and Potential. Retrieved 10 November 2012 from
  15. Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5), 304-317.
  16. Fournier, H., Kop, R., and Sitlia, H. (2011). The Value of learning analytics to networked learning on a Personal Learning Environment. In: the 1st International Conference on Learning Analytics and Knowledge (LAK’11) (pp. 104-109). New
  17. Goodyear, P., Retalis, S. (2010). Technology-Enhanced Learning, Design Patterns and Pattern Languages. Sense Publishers. Volume 2. Retrieved 12 November 2012 from
  18. Grau-Valldosera, J., & Minguillón, J. (2011). Redefining dropping out in online higher education: a case study from the UOC. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 75-80). ACM.
  19. Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Educational Technology& Society, 15 (3), 42–57.
  20. Jackson, G. & Read, M. (2012). Connect 4 Success: A Proactive Student Identifications ad Support Program. Retrieved 12 November 2014 from
  21. Johnson, L., Adams Becker, S., Estrada, V., Freeman, A. (2014). NMC Horizon Report: 2014 Higher Education Edition. Austin, Texas: The New Media Consortium.
  22. Jones, K., Thomson, J., and Arnold, K. (2014). Questions of Data Ownership on Campus. EDUCASE Review. Retrieved 3rd December 2014 from
  23. Knight, S., Buckingham Shum, S., and Littleton, K. (2013). Epistemology, pedagogy, assessment and learning analytics. In: Third Conference on Learning Analytics and Knowledge (LAK 2013) (pp. 75–84). New York, USA: ACM.
  24. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A.H. (2011). Big data: The next frontier for innovation, competition, and productivity.
  25. Mostow, J., & Beck, J. (2006). Some useful tactics to modify, map and mine data from intelligent tutors. Natural Language Engineering, 12(02), 195-208.
  26. Open University of England. (2014). Policy on Ethical Use of Student Data for Learning Analytics Retrieved 03 December 2014 from
  27. Prinsloo, P., & Slade, S. (2013). An evaluation of policy frameworks for addressing ethical considerations in learning analytics. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (LAK 2013) (pp. 240-244). New York, USA: ACM.
  28. Romero, C., & Ventura, S. (2010). Educational data mining: a review of the state of the art. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 40(6), 601-618.
  29. Siemens, G.(2010). What are Learning Analytics? Retrieved 7 November
  30. Singer, N. (2014). ClassDojo adopts Deletion Policy for Student Data. Retrieved 29 November 2014 from & Emc=rss & _r=2
  31. Society for Learning Analytics Research. (2011). Open Learning Analytics: an integrated& Modularized platform Retrieved 01 November 2014 from
  32. Taraghi, B., Ebner, M., Saranti, A., & Schön, M. (2014) On using markov chain to evidence the learning structures and difficulty levels of one digit multiplication. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (LAK’14) (pp. 68-72).New York, USA: ACM.
  33. 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). IEEE.
  34. Waterman, K.K., & Bruening, P.J. (2014). Big Data analytics: risks and responsibilities. International Data Privacy Law, 4(2), 89-95.

These references have been extracted automatically and may have some errors. If you see a mistake in the references above, please contact