Advertisement

Curricular and Learning Analytics: A Big Data Perspective

  • Colin Pinnell
  • Geetha Paulmani
  • Vivekanandan KumarEmail author
  • Kinshuk
Chapter

Abstract

Analytics is about insights. Learning Analytics is about insights on factors such as capacity of learners, learning behaviour, predictability of learning concerns, and nurturing of cognitive aspects of learners, among others. Learning Analytics systems can engage learners to detect and appreciate insights generated by others, engage learners to investigate models on learning factors, and engage learners to create new insights. This chapter offers details of this vision for learning analytics, particularly in light of the ability to collect enormous amounts of data from students’ study episodes, wherever they happen to study using whatever resources they employ. Further, the chapter contends that learning analytics can also be used to make statements on the efficacy of a particular curriculum and recommend changes based on curricular insights.

Keywords

Precise Model Learning Context Analytic Platform Aggregate Model Competency Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Agarwal, P., Shroff, G., & Malhotra, P. (2013). Approximate incremental big-data harmonization. In 2013 IEEE International Congress on Big Data (BigData Congress) (pp. 118–125).Google Scholar
  2. Almosallam, E., & Ouertani, H. (2014). Learning analytics: Definitions, applications and related fields. In T. Herawan, M. M. Deris & J. Abawajy (Eds.), Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013), Vol. 285 (pp. 721–730). Singapore: Springer. (ISBN: 978-981-4585-17-0). Retrieved from http://dx.doi.org/10.1007/978-981-4585-18-7_81.
  3. 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). Retrieved from http://doi.acm.org/10.1145/2330601.2330666.
  4. Bader-Natal, A., & Lotze, T. (2011). Evolving a learning analytics platform. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 180–185). ACM. (ISBN: 978-1-4503-0944-8). Retrieved from http://doi.acm.org/10.1145/2090116.2090146.
  5. Barber, R., & Sharkey, M. (2012). Course correction: Using analytics to predict course success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 259–262). ACM. (ISBN: 978-1-4503-1111-3). Retrieved from http://doi.acm.org/10.1145/2330601.2330664.
  6. Benzaken, V., Castagna, G., Nguyen, K., & Siméon, J. (2013). Static and dynamic semantics of NoSQL languages. SIGPLAN Not, 48(1), 101–114. doi: 10.1145/2480359.2429083. Retrieved from http://0-doi.acm.org.aupac.lib.athabascau.ca/10.1145/2480359.2429083.
  7. Blikstein, P. (2013). Multimodal learning analytics. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 102–106). ACM. (ISBN: 978-1-4503-1785-6). Retrieved from http://0-doi.acm.org.aupac.lib.athabascau.ca/10.1145/2460296.2460316.
  8. Boyd, D., & Crawford, K. (2011). Six provocations for big data. In A decade in Internet time: Symposium on the dynamics of the internet and society. Retrieved from http://dx.doi.org/10.2139/ssrn.1926431.
  9. 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), 318–331. Retrieved from http://dl.acm.org/citation.cfm?id=2434498.
  10. Cuzzocrea, A., & Simitsis, A. (2012). Searching semantic data warehouses: Models, issues, architectures. In Proceedings of the 2nd International Workshop on Semantic Search over the Web (pp. 6:1–6:5). ACM. (ISBN: 978-1-4503-2301-7). Retrieved from http://0-doi.acm.org.aupac.lib.athabascau.ca/10.1145/2494068.2494074.
  11. del Blanco, A., Serrano, A., Freire, M., Martinez-Ortiz, I., & Fernandez-Manjon, B. (2013). E-learning 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
  12. Dobre, C., & Xhafa, F. (2014). Parallel programming paradigms and frameworks in big data era. International Journal of Parallel Programming, 42(5), 710–738. doi: 10.1007/s10766-013-0272-7.CrossRefGoogle Scholar
  13. Duval, E., Klerkx, J., Verbert, K., Nagel, T., Govaerts, S., Parra Chico, G. A., et al. (2012). Learning dashboards & learnscapes. Educational Interfaces, Software, and Technology, 1–5. Retrieved from https://lirias.kuleuven.be/handle/123456789/344525.
  14. 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). Retrieved from http://doi.acm.org/10.1145/2330601.2330616.
  15. Jensen, M. (2013). Challenges of privacy protection in big data analytics. Paper presented at the 2013 IEEE International Congress on Big Data (BigData Congress) (pp. 235–238).Google Scholar
  16. Miller, J., Baramidze, G., Sheth, A., & Fishwick, P. (2004). Investigating ontologies for simulation modeling. In Proceedings of the 37th Annual Simulation Symposium, 2004 (pp. 55–63).Google Scholar
  17. Prinsloo, P., & Slade, S. (2015). Student privacy self-management: Implications for learning analytics. In Proceedings of the Fifth International Conference on Learning Analytics and Knowledge (LAK’15) (pp. 83–92). New York: ACM. Retrieved from http://dl.acm.org/citation.cfm?id=2723585.
  18. Teplovs, C., Fujita, N., & Vatrapu, R. (2011). Generating predictive models of learner community dynamics. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 147–152). ACM. (ISBN: 978-1-4503-0944-8). Retrieved from http://0-doi.acm.org.aupac.lib.athabascau.ca/10.1145/2090116.2090139.
  19. van Harmelen, M. (2006). Personal learning environments. In Proceedings of the Sixth International Conference on Advanced Learning Technologies, 2006 (pp. 815–816).Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Colin Pinnell
    • 1
  • Geetha Paulmani
    • 1
  • Vivekanandan Kumar
    • 1
    Email author
  • Kinshuk
    • 1
  1. 1.Athabasca UniversityAthabascaCanada

Personalised recommendations