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  1. learning analytics

  2. what are learning analytics? related fields of study processes resources a model for learning analytics where are we now? implementation tips references literature review

  3. learning analytics are: the ability to “scale the real-time use of learning analytics by students, instructors and academic advisors to improve student success” - Next Generation: Learning Challenges next page: learning analytics involves main page

  4. learning analytics involves: • the development of new processes and tools aimed at improving learning and teaching for individual students and instructors • 2. the integration of these tools and processes into the practice of teaching and learning next page: related fields of study main page related links

  5. related fields of study business intelligence web analytics academic analytics action analytics main page

  6. business intelligence: a well-established process in the business world whereby decision makers integrate strategic thinking with information technology to be able to synthesize “vast amounts of data into powerful, decision making capabilities” - Baker, 2007 next page: web analytics main page

  7. web analytics: “the collection, analysis and reporting of Web site usage by visitors and customers of a web site” in order to “better understand the effectiveness of online initiatives and other changes to the web site in an objective, scientific way through experimentation, testing, and measurement” - McFadden, 2005 next page: academic analytics main page related links

  8. academic analytics: • the application of the principles and tools of business intelligence to how institutions gather, analyze, and use data to improve student success • Campbell and Oblinger, 2007 & • Goldstein and Katz, 2005 next page: action analytics main page related links

  9. action analytics: involves deploying academic analytics “ to provide actionable intelligence, service-oriented architectures, mash-ups of information/content and services. proven models of course/curriculum reinvention, and changes in faculty practice that improve performance and reduce costs - Norris et al, 2008 next page: learning analytics processes main page

  10. learning analytics processes data gathering capture sharing select knowledge application information processing aggregate refine predict use main page

  11. data gathering select There are so many metrics that could be tracked, it is essential to define goals and identify relevant data. What do we want to achieve? Are we measuring what we should be? How can we create innovative metrics? capture Large store of data already exist and computer-mediated distance education increasingly creates student data trails. Most often exists in disjointed and meaningless forms. next page: information processing main page

  12. information processing predict aggregate To be usable, we must be able to aggregate that data into a meaningful form. Dashboards and social network analysis are two promising tools. Data is useful when it can be used to predict future events. To date, however, no guidance it available to educators to indicate which captured variables are pedagogically meaningful. Outside of education, search engines and recommenders sites are examples of aggregating information and using it to predict user needs. next page: information processing main page

  13. knowledge application use In order to be a knowledge discovery cycle, data and actions must be re-presented to users. Otherwise, it is just data mining. refine Analytics are a self-improvement project. Monitoring impact must be a continual effort, the results of which are used to update the models and improve predictions. next page: analytics tools main page

  14. sharing When institutions work together and share, duplication is reduced and improvements are increased. Sharing data, models and innovations, therefore, has the potential to improve learning for everyone. next page: analytics tools main page

  15. Organizations Computers learning analytics resources People Theory ...a single amalgam of human and machine processing which is instantiated through an interface that both drives and is driven by the whole system, human and Machine - Dron and Anderson, 2009 There are four types tools that must interact for learning analytics to be successful. main page

  16. Organizations Computers computers People Theory Sophisticated computers already collect data. They also facilitate data processing with visualization tools because we can process an incredible amount of information if it is packaged and presented correctly. Two promising visualization tools for learning analytics are dashboards and social networks maps. next page: dashboards main page related links

  17. dashboards Organizations Computers People Theory Meaningful information can be can be extracted from CMS/LMS and be made available to students and instructors. next page: social network analysis main page related links

  18. Organizations Computers social network maps People Theory Automates the process of extraction, collation, evaluation and visualisation of student network data into a form quickly usable by instructors. next page: theory main page related links

  19. Organizations Computers theory People Theory Computer hardware and software are only useful if they are based on sound theory. Social networks maps, for example, are only useful because of sound research-based theory that demonstrates we learn better when we interact with others. next page: people main page

  20. Organizations Computers people People Theory There are still a significant aspects of an analytics system that require human knowledge, skills and abilities to operate. Developing effective learning interventions remains highly dependent on human cognitive problem-solving and decision-making skills. next page: organizations main page more information

  21. Organizations Computers organizations People Theory Social networks maps, for example, are only useful because of sound research-based theory that shows peer networks play an important role in student persistence and overall success. next page: organizations main page

  22. a model for learning analytics Organizations Computers data gathering capture People Theory select sharing information processing knowledge application refine aggregate use predict main page next page: where are we now?

  23. where are we now? Learning analytics is an emerging field. Analyticsis other fields is already well established. Tools and lessons learned from other fields can be used to support the introduction of learning analytics to the majority. next page: tips for analytics main page more information

  24. implementation tips • Learn from others disciplines in which analytics is an established field • Find out what you are already measuring • Combine web-based data with traditional evaluation, assessment and demographic data • Good communication skills are essential • Change is hard for everyone and rarely welcome - tread lightly and offer support next page: references main page

  25. references Arnold, K. E. (2010). Signals: Applying Academic Analytics, EDUCAUSE Quarterly 33(1). Retrieved October 1, 2010 from http://www.educause.edu/EDUCAUSE+Quarterly/EDUCAUSEQuarterlyMagazineVolum/SignalsApplyingAcademicAnalyti/199385 Astin, A. (1993). What Matters in College? Four Critical Years Revisited. San Francisco: Jossey-Bass. Baker, B. (2007). A conceptual framework for making knowledge actionable through capital formation. D.Mgt. dissertation, University of Maryland University College, United States -- Maryland. Retrieved October 19, 2010, from ABI/INFORM Global.(Publication No. AAT 3254328). Dron, J. and Anderson, T. (2009). On the design of collective applications, Proceedings of the 2009 International Conference on Computational Science and Engineering , Volume 04, pp. 368-374. 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 next page: references (cont’d)

  26. references(continued) McFadden, C. (2005). Optimizing the Online Business Channel with Web Analytics [blog post]. Retrieved October 5, 2010 from http://www.webanalyticsassociation.org/members/blog_view.asp?id=533997&post=89328&hhSearchTerms=definition+and+of+and+web+and+analytics NextGeneration: Learning Challenges (n.d.). Learning Analytics [website]. Retrieved October 12, 2010 fromhttp://nextgenlearning.com/the-challenges/learning-analytics Norris, D., Baer, L., Leonard, J., Pugliese, L. and Lefrere, P. (2008). Action Analytics: Measuring and Improving Performance That Matters in Higher Education, EDUCAUSE Review 43(1). Retrieved October 1, 2010 from http://www.educause.edu/EDUCAUSE+Review/EDUCAUSEReviewMagazineVolume43/ActionAnalyticsMeasuringandImp/162422 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://0-www.editlib.org.aupac.lib.athabascau.ca/p/32307.