Learning analytics

The educational technology and digital learning wiki
Jump to navigation Jump to search

Draft

Introduction

One could define learning analytics as collection of methods that allow teachers and maybe the learners to understand what is going on. I.e. all sorts of tools that allow to gain insight on participant's behavior and productions.

The Society for Learning Analytics Research Open Learning Analytics proposal associates learning analytics with the kind of "big data" that are used in busines intelligence:

The rapid development of “big data” methods and tools coincides with new management and measurement processes in corporations. The term “business intelligence” is used to describe this intersection of data and insight. When applied to the education sector, analytics fall into two broad sectors (Table 1): learning and academic.

Learning analytics (LA) is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. Learning analytics are largely concerned with improving learner success.

Academic analytics is the improvement of organizational processes, workflows, resource allocation, and institutional measurement through the use of learner, academic, and institutional data. Academic analytics, akin to business analytics, are concerned with improving organizational effectiveness.

In other words, learning analytics concerns people concerned by teaching and learning, e.g. learners themselves, teachers, course designers, course evaluators, etc. Learning analytics is seen {{quotation|LA as a means to provide stakeholders (learners, educators, administrators, and funders) with better information and deep insight into the factors within the learning process that contribute to learner success. Analytics serve to guide decision making about educational reform and learner-level intervention for at-risk students. (Simons et al. 2011: 5)

In that sense, this definition is political and like many other constructs in the education sciences it promises better education. We therefore conclude the introduction that learning analytics either can be either be seen as tools that should be integrated into the learning environment and scenario with respect to specific teaching goals or as a more general and "bigger device" for doing "education intelligence". The latter also would include the former.

Technology

Some kinds of learning analytics have been known and used since education education exists like:

The SoLAR strategy

This proposal addresses the need for integrated toolsets through the development of four specific tools and resources:

  1. A Learning analytics engine, a versatile framework for collecting and processing data with various analysis modules (depending on the environment)
  2. Adaptive content engine
  3. Intervention engine: recommendations, automated support
  4. Dashboard, reporting, and visualization tools

A short discussion

The proposal starts somehow with the assumption that education continues to use rather simple tools like courseware or somewhat now popular web 2.0 tools like personal learning environments. I.e. they do not assume that learner/teacher cockpits could be built for start into activity-based learning environments. To put it more bluntly: It is assumed that teachers don't know what is going on and that learners can't keep track of their progress or more precisely that teachers can't design scenarios that will help both teachers and students knowing what is going on. We mostly share that assumption, but would like to point out that knowledge tools do exist, e.g. knowledge forum, but these are not used. Also, the whole literature on reflective and scaffolding tools, e.g. from the CSCL community is not considered enough.

We would like to draw parallels with (1) the metadata community that spent a lot of time designing standards for describing documents and instead of working on how to create more expressive documents and understanding how people compose documents, (2) with business that spends energy on marketing and related business intelligence instead of designing better products, (3) with folks who believe in adaptive systems forgetting that learner control is central to deep learning and that most higher education is collaborative, (4) intelligent tutoring systems trying to give control to the machine and (5) finally with the illusion of learning style. These negative remarks are not meant to say that this project should or must fail, but they are meant to clearly state that the #1 issue is in education is not analytics, but designing good learning scenarios within appropriate learning environements (most are not).

Links

Organizations

Bibliography

(so far based on the Open Learning Analytics proposal)

  • Baker, R.S.J.d., de Carvalho, A. M. J. A. (2008) Labeling Student Behavior Faster and More Precisely with Text Replays. Proceedings of the 1st International Conference on Educational Data Mining, 38-47.
  • Baker, R.S.J.d., Yacef, K. (2009) The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1 (1), 3-17.
  • Dawson, S., Heathcote, L. and Poole, G. (2010), “Harnessing ICT Potential: The Adoption and Analysis of ICT Systems for Enhancing the Student Learning Experience,” The International Journal of Educational Management, 24(2), pp. 116-129.
  • Hadwin, A. F., Nesbit, J. C., Code, J., Jamieson-Noel, D. L., & Winne, P. H. (2007). Examining trace data to explore self-regulated learning. Metacognition and Learning, 2, 107-124
  • Haythornthwaite, C. (2008). Learning relations and networks in web-based communities. International Journal of Web Based Communities, 4(2), 140-158.
  • Koedinger, K.R., Baker, R.S.J.d., Cunningham, K., Skogsholm, A., Leber, B., Stamper, J. (2010) A Data Repository for the EDM community: The PSLC DataShop. In Romero, C., Ventura, S., Pechenizkiy, M., Baker, R.S.J.d. (Eds.) Handbook of Educational Data Mining. Boca Raton, FL: CRC Press, pp. 43-56.
  • Macfayden, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning” system for educators: a proof of concept. Computers & Education, 54(2), 588–599.
  • Najjar. J; M. Wolpers, and E. Duval. Contextualized attention metadata. D-Lib Magazine, 13(9/10), Sept. 2007.
  • Rath, A. S., D. Devaurs, and S. Lindstaedt. UICO: an ontology-based user interaction context model for automatic task detection on the computer desktop. In Proceedings of the 1st Workshop on Context, Information and Ontologies, CIAO ’09, pages 8:1—-8:10, New York, NY, USA, 2009. ACM.
  • Romero, C. Ventura, S. (in press) Educational Data Mining: A Review of the State-of-the-Art. IEEE Transaction on Systems, Man, and Cybernetics, Part C: Applications and Reviews.
  • Siemens George; Dragan Gasevic, Caroline Haythornthwaite, Shane Dawson, Simon Buckingham Shum, Rebecca Ferguson, Erik Duval, Katrien Verbert and Ryan S. J. d. Baker (2001). Open Learning Analytics: an integrated & modularized platform Proposal to design, implement and evaluate an open platform to integrate heterogeneous learning analytics techniques, Society for Learning Analytics Research.PDF, retrieved 22:02, 1 March 2012 (CET).
  • Siemens, G., Long, P. (2011). Penetrating the Fog: Analytics in learning and education. EDUCAUSE Review, vol. 46, no. 4 (July/August 2011)
  • Ternier, S.; K. Verbert, G. Parra, B. Vandeputte, J. Klerkx, E. Duval, V. Ordonez, and X. Ochoa. The Ariadne Infrastructure for Managing and Storing Metadata. IEEE Internet Computing, 13(4):18–25, July 2009.
  • Wolpers. M; J. Najjar, K. Verbert, and E. Duval. Tracking actual usage: the attention metadata approach. Educational Technology and Society, 10(3):106–121, 2007.

Planned:

Retrieved from "https://edutechwiki.unige.ch/mediawiki/index.php?title=Learning_analytics&oldid=37754"