Learning analytics

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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 (2011) 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.
Learning and Academic Analytics. © 2011 George Siemens and Phil Long, EDUCAUSE Review, vol. 46, no. 5 (September/October 2011) . Licensed under the Commons Attribution-NonCommercial 3.0 License

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 “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 seen either (1) as tools that should be integrated into the learning environment and scenario with respect to specific teaching goals or (2) as a more general and bigger "framework" for doing "education intelligence". The latter also would include the former.

Frameworks

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

The SoLAR strategy

The technical framework

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. In other words, the fog is identifed as problem and not the way education is designed. I.e. this proposal focuses on "intelligence" as opposed to "design". If you like, is more like "General motors" than "Apple". Related to that it is also assumed that "metrics" work, while in reality testing-crazy systems like the US high-school education has a much lower performance than design-based systems like the Finnish one.

The project proposes a general framework based on modern service-oriented architectures. So far, all attempts to use such architectures in education did fail, probably because of the lack of substantial long-term funding, e.g. see the e-framework project. We also a wonder a bit how such funding could be found, since not even the really simple IMS Learning Design has been implemented in a usable set of tools. In addition, even more simple stuff, like simple wiki tracking is not available, e.g. read wiki metrics, rubrics and collaboration tools

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) with the utter failure of 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 state two things: The #1 issue is in education is not analytics, but designing good learning scenarios within appropriate learning environements (most are not). The #2 issue is massive long term funding. Such a system won't work before at least 50 man years over a 10 year period is spend.

Somewhat it also 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 never used. This can be said in general of CSCL tools that usually include scaffolding and meta-reflective tools. In other words, this proposal seems to imply that that education and students will remain simple, but "enhanced" with both teacher and student cockpits.

Finally, this project raises deep privacy and innovation issues. Since analytics can be used for assessment, there will be attemps to create life-long scores. In addition, if students are require to play the analytics game in order to improve chances, this will be other blow to to creativity. If educational systems formally adopt analytics, it opens the way for keeping education in line with "main-stream" e-learning, an approach designed for training simple subject matters (basic facts and skills) through reading, quizzing and chatting. This, because analytics will work fairly easily with simple stuff, e.g. scores, lists of buttons clicked, number of blog and forum posts, etc.

Software

According to Wikipedia, retrieved 14:04, 2 March 2012 (CET). “Much of the software that is currently used for learning analytics duplicates functionality of web analytics software, but applies it to learner interactions with content. Social network analysis tools are commonly used to map social connections and discussions”

Social network analysis

  • SNAPP - “a software tool that allows users to visualize the network of interactions resulting from discussion forum posts and replies. The network visualisations of forum interactions provide an opportunity for teachers to rapidly identify patterns of user behaviour – at any stage of course progression. SNAPP has been developed to extract all user interactions from various commercial and open source learning management systems (LMS) such as BlackBoard (including the former WebCT), and Moodle. SNAPP is compatible for both Mac and PC users and operates in Internet Explorer, Firefox and Safari.”, retrieved 14:04, 2 March 2012 (CET).

IMS/CP tracking

  • LOCO-Analyst “is implemented as an extension of Reload Content Packaging Editor, an open-source tool for creating courses compliant with the IMS Content Packaging (IMS CP) specification. By extending this tool with the functionalities of LOCO-Analyst, we have ensured that teachers effectively use the same tool for creating learning objects, receiving and viewing automatically generated feedback about their use, and modifying the learning objects accordingly.” (LOCO-Analyst, retrieved 14:04, 2 March 2012 (CET)).

Other

  • SAM - “To increase self-reflection and awareness among students and for teachers, we developed a tool that allows analysis of student activities with different visualizations”. Could be used for Personal learning environments.

Use of social software

Links

Overviews

Organizations

Web sites

Free online courses

Talks

Bibliography

This bibliography is (so far) mostly based on the Open Learning Analytics proposal and Penetrating the Fog EduCause article.

  • 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. PDF
  • Campbell, John P.; Peter B. DeBlois, and Diana G. Oblinger, “Academic Analytics: A New Tool for a New Era,” EDUCAUSE Review, vol. 42, no. 4 (July/August 2007), pp. 40–57, <http://www.educause.edu/library/erm0742>.
  • 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.
  • Dawson, S. (2008). A study of the relationship between student social networks and sense of community, Educational Technology and Society 11(3), pp. 224-38.
  • Dawson, ". (2010) Seeing the learning community. An exploration of the development of a resource for monitoring online student networking. British Journal of Educational Technology, 41(5), 736-752. doi:10.1111/j.1467-8535.2009.00970.x.
  • Dawson, S., Heathcote, L. and Poole, G. (2010). Harnessing ICT potential: The adoption and analysis of ICT systems for enhancing the student learning experience, International Journal of Educational Management 24(2) pp. 116-128.
  • De Liddo, A., Buckingham Shum, S., Quinto, I., Bachler, M. and Cannavacciuolo, L. (2011). Discourse-Centric Learning Analytics. Proc. 1st International Conference on Learning Analytics & Knowledge. Feb. 27-Mar 1, 2011, Banff. ACM Press: New York. Eprint: http://oro.open.ac.uk/25829
  • Ferguson, R. and Buckingham Shum, S. (2011). Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat. Proc. 1st International Conference on Learning Analytics & Knowledge. Feb. 27-Mar 1, 2011, Banff. ACM Press: New York. Eprint: http://oro.open.ac.uk/28955
  • Goldstein, P. J. (2005) Academic Analytics: Uses of Management Information and Technology in Higher Education PDF
  • Goldstein, P. J. and Katz, R. N. (2005). Academic Analytics: The Uses of Management Information and Technology in Higher Education, ECAR Research Study Volume 8. Retrieved October 1, 2010 from http://www.educause.edu/ers0508
  • 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.
  • Hijon R. and Carlos, R. (2006). E-learning platforms analysis and development of students tracking functionality, in Proceedings of the 18th World Conference on Educational Multimedia,Hypermedia & Telecomunications, pp. 2823-2828.
  • 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.
  • Leah P. Macfadyen and Shane Dawson, “Mining LMS Data to Develop an ‘Early Warning System’ for Educators: A Proof of Concept,” Computers & Education, vol. 54, no. 2 (2010), pp. 588–599.
  • Libby V. Morris, Catherine Finnegan, and Sz-Shyan Wu, “Tracking Student Behavior, Persistence, and Achievement in Online Courses,” The Internet and Higher Education, vol. 8, no. 3 (2005), pp. 221–231.
  • 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.
  • Mazza R., and Dimitrova, V. (2004). Visualising student tracking data to support instructors in web-based distance education, 13th International World Wide Web conference, New York, NY, USA: ACM Press, pp. 154-161. Retrieved October 7, 2010 from http://www.iw3c2.org/WWW2004/docs/2p154.pdf
  • 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)
  • Siemens, G. (2007) “Connectivism: Creating a Learning Ecology in Distributed Environment,” in Didactics of Microlearning: Concepts, discourses, and examples, in T. Hug, (ed.),Waxmann Verlag, New York, pp. 53-68
  • 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.
  • Zhang, H., Almeroth, K., Knight, A., Bulger, M., and Mayer, R. (2007). Moodog: Tracking Students' Online Learning Activities in C. Montgomerie and J. Seale (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications, Chesapeake, VA: AACE, 2007, pp. 4415􀂱4422. DOI 10.1.1.82.951
  • Zhang, H. and Almeroth, K. (2010). Moodog: Tracking Student Activity in Online Course Management Systems. Journal of Interactive Learning Research, 21(3), 407-429. Chesapeake, VA: AACE. Retrieved October 5, 2010 from http://www.editlib.org.aupac.lib.athabascau.ca/p/32307.

Planned:

Acknowledgements and copyright modification

This article is licensed under the Creative Commons Attribution-NonCommercial 3.0 License and you also must cite cite Penetrating the Fog: Analytics in Learning and Education if you reuse parts from this source, e.g. figures and the bibliography.

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