Learning Analytics: definitions, applications and related fields

A study for future challenges
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 285)

Abstract

In the last few decades, the number of people connected online for educational purpose is increasing dramatically and consequently a huge quantity of data is being generated. This data is mainly “traces” or “digital breadcrumbs” that students leave as they interact with online learning environments. Confident that this data can teach us about learners’ behaviors and help us enhancing learning experience, there has been a growing interest in the automatic analysis of such data. A research area referred to as Learning Analytics (LA) is identified. It is considered by many researchers as a strategic trend in education. Nevertheless, LA cannot be considered as a new field, it actually derives from different related fields such as Educational Data Mining, Academic Analytics, Action research, Personalized Adaptive Learning.

In this paper, we begin with an examination of the educational factors that have driven the need and the development of analytics in education. We study connections between LA and its most related fields (Educational Data Mining and Academic Analytics). We summarize this interconnection in a table showing for each field the objectives, the stakeholders, the methods and the initial trigger behind the analysis actions. After that we study and run through LA applications presented in the International Learning Analytics & Knowledge Conferences during the three last years. Finally, we conclude by identifying some challenges in the area of LA in relation to the driven factors related to Educational Data.

Keywords

Learning Analytics Educational Data Analytics Applications 

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References

  1. 1.
    Elias, T. 2011. ”Learning Analytics: Definitions, Processes and Potential”.Google Scholar
  2. 2.
    George Siemens, “Structure and Logic of the Learning Analytics Field”, 2013, Retrieved June 3, 2013, from http://www.learninganalytics.net/?p=185
  3. 3.
    Maurizio De Rose, “Future trends on data analytics to support your learners”, 2013, Retrieved May 15, 2013, from http://www.jisc.ac.uk/inform/inform35/FutureTrendsDataAnalytics.html
  4. 4.
    George Siemens, “Sensemaking: Beyond Analytics as a Technical Activity”, April 11, 2012Google Scholar
  5. 5.
    Chatti, Mohamed Amine, et al. “A reference model for learning analytics.”International Journal of Technology Enhanced Learning 4.5 (2012): 318-331.‏Google Scholar
  6. 6.
    Siemens, G. (2010). What are Learning Analytics? Retrieved May 14, 2013, from http://www.elearnspace.org/blog/2010/08/25/what-are-learning-analytics/
  7. 7.
    Johnson, L., Adams Becker, S.,Cummins, M., Estrada, V., Freeman, A., and Ludgate, H. (2013). NMC Horizon Report: 2013 Higher Education Edition. Austin, Texas: The New Media ConsortiumGoogle Scholar
  8. 8.
    How data and analytics can improve education, Retrieved May 14, 2013, from http://strata.oreilly.com/2011/07/education-data-analytics-learning.html
  9. 9.
    Jie Wu, “Revolutionize Education Using Learning Analytics”, Retrieved May 11, 2013, from http://www.aboutjiewu.com/Resources/projects/INFO203/LearningAnalytics.pdf
  10. 10.
    Malcolm Brown, “Learning Analytics: The Coming Third Wave”, April 2011, Retrieved May 12, 2013, from http://www.educause.edu/library/resources/learning-analytics-coming-third-wave
  11. 11.
    Ferguson, Rebecca. “Learning analytics: drivers, developments and challenges.” International Journal of Technology Enhanced Learning 4.5 (2012): 304-317.‏Google Scholar
  12. 12.
    Suthers, Daniel, and Devan Rosen. “A unified framework for multi-level analysis of distributed learning.” Proceedings of the 1st International Conference on Learning Analytics and Knowledge. ACM, 2011.‏Google Scholar
  13. 13.
    Romero, Cristóbal, and Sebastián Ventura. “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 (2010): 601-618.‏Google Scholar
  14. 14.
    Bader-Natal, Ari, and Thomas Lotze. “Evolving a learning analytics platform.”Proceedings of the 1st International Conference on Learning Analytics and Knowledge. ACM, 2011.‏Google Scholar
  15. 15.
    Serrano-Laguna, Ángel, et al.”Tracing a Little for Big Improvements: Application of Learning Analytics and Videogames for Student Assessment.”Procedia Computer Science 15 (2012).‏Google Scholar
  16. 16.
    Graf, Sabine, et al. “AAT: a tool for accessing and analysing students’ behaviour data in learning systems.” Proceedings of the 1st International Conference on Learning Analytics and Knowledge. ACM, 2011.Google Scholar
  17. 17.
    Song, Ergang, et al. “Learning analytics at large: the lifelong learning network of 160,000 European teachers.” Towards Ubiquitous Learning. Springer Berlin Heidelberg, 2011. 398-411.‏Google Scholar
  18. 18.
    Bakharia, Aneesha, and Shane Dawson. “SNAPP: a bird’s-eye view of temporal participant interaction.” Proceedings of the 1st International Conference on Learning Analytics and Knowledge. ACM, 2011.‏Google Scholar
  19. 19.
    Arnold, Kimberly E., and Matthew D. Pistilli. “Course Signals at Purdue: Using learning analytics to increase student success. “Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. ACM, 2012.‏Google Scholar
  20. 20.
    Leony, Derick, et al. “GLASS: a learning analytics visualization tool.”Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. ACM, 2012.‏Google Scholar
  21. 21.
    Siadaty, Melody, et al. “Learn-B: a social analytics-enabled tool for self-regulated workplace learning.” Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. ACM, 2012.‏Google Scholar
  22. 22.
    McKay, Tim, Kate Miller, and Jared Tritz. “What to do with actionable intelligence: E 2 Coach as an intervention engine.” Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. ACM, 2012.‏Google Scholar
  23. 23.
    Koulocheri, Eleni, and Michalis Xenos. “Considering formal assessment in learning analytics within a PLE: the HOU2LEARN case.” Proceedings of the Third International Conference on Learning Analytics and Knowledge. ACM, 2013.‏Google Scholar
  24. 24.
    Santos, Jose Luis, et al. “Addressing learner issues with StepUp!: an Evaluation.” Proceedings of the Third International Conference on Learning Analytics and Knowledge. ACM, 2013.‏ ‏Google Scholar
  25. 25.
    Bauer, Florian, and Martin Kaltenböck. “Linked Open Data: The Essentials.”Edition mono/monochrom, Vienna (2011).‏Google Scholar
  26. 26.
    The Open Data Handbook, Retrieved June 19, 2013, from http://opendatahandbook.org/
  27. 27.
    Siemens, George, and Ryan SJ D. Baker. “Learning analytics and educational data mining: towards communication and collaboration.” Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. ACM, 2012.‏Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  • Entesar A. Almosallam
    • 1
  • Henda Chorfi Ouertani
    • 1
  1. 1.Information Technology DepartmentKing Saud UniversityRiyadhSaudi Arabia

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