Learning Analytics Oer

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Interactive Power Point presentation intended to introduce learners to the basics of learning analytics.

Prepared by Tanya Elias and shared in the Learning and Knowledge Analytics course https://landing.athabascau.ca/pg/file/tanyael/read/43701/learning-analytics-oer

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Learning Analytics Oer

  1. 1. learning analytics
  2. 2. what are learning analytics? related fields of study processes resources a model for learning analytics where are we now? implementation tipsreferences literature review
  3. 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 involvesmain page
  4. 4. learning analytics involves: 1. 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 studymain page related links
  5. 5. related fields of study business intelligence web analytics academic analytics action analyticsmain page
  6. 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 analyticsmain page
  7. 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 analyticsmain page related links
  8. 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 analyticsmain page related links
  9. 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 processesmain page
  10. 10. learning analytics processes capture data gathering select refine aggregate knowledge information application processing use predictmain page
  11. 11. data gathering select There are so many metrics that could be capture tracked, it is essential to define goals and identify relevant data. Large store of data already exist What do we want to achieve? and computer-mediated distance Are we measuring what we should be? education increasingly creates How can we create innovative metrics? student data trails. Most often exists in disjointed and meaningless forms. next page: information processingmain page
  12. 12. information processing predict aggregateTo be usable, we must be able to Data is useful when it can be used toaggregate that data into a predict future events.meaningful form. To date, however, no guidance itDashboards and social network available to educators to indicate whichanalysis are two promising tools. 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 processingmain page
  13. 13. knowledge application use In order to be a knowledge discovery cycle, data and refine actions must be re-presented to users. Otherwise, it is just Analytics are a self-improvement data mining. project. Monitoring impact must be a continual effort, the results of which are used to update the models and improve predictions. next page: analytics toolsmain page
  14. 14. 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 toolsmain page
  15. 15. learning analytics resources ...a single There are four amalgam of human andtypes tools that machine must interact processing which for learning Organizations Computers is instantiated through an analytics to be interface that successful. both drives and is driven by the People Theory whole system, human and Machine - Dron and Anderson, 2009main page
  16. 16. computers Computers Organizations Sophisticated computers already collect People Theory 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: dashboardsmain page related links
  17. 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 analysismain page related links
  18. 18. social network maps Organizations Computers People Theory Automates the process of extraction, collation, evaluation and visualisation of student network data into a form quickly usable by instructors. next page: theorymain page related links
  19. 19. theory Organizations Computers Computer hardware and Theory People 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: peoplemain page
  20. 20. people Organizations Computers There are still a significant People Theory 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: organizationsmain page more information
  21. 21. organizations Organizations Computers Social networks maps, for People Theory 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: organizationsmain page
  22. 22. a model for learning analytics capture data gathering select Organizations Computers People Theory refine aggregate knowledge information application processing use predictmain page next page: where are we now?
  23. 23. where are we now? Learning analytics is an emerging field. Analytics is 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 analyticsmain page more information
  24. 24. implementation tips 1. Learn from others disciplines in which analytics is an established field 2. Find out what you are already measuring 3. Combine web-based data with traditional evaluation, assessment and demographic data 4. Good communication skills are essential 5. Change is hard for everyone and rarely welcome - tread lightly and offer support next page: referencesmain page
  25. 25. referencesArnold, K. E. (2010). Signals: Applying Academic Analytics, EDUCAUSE Quarterly 33(1).Retrieved October 1, 2010 fromhttp://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 throughcapital formation. D.Mgt. dissertation, University of Maryland University College, UnitedStates -- 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 ofthe 2009 International Conference on Computational Science and Engineering , Volume04, pp. 368-374.Goldstein, P. J. and Katz, R. N. (2005). Academic Analytics: The Uses of ManagementInformation 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. 26. references (continued)McFadden, C. (2005). Optimizing the Online Business Channel with Web Analytics [blogpost]. Retrieved October 5, 2010 fromhttp://www.webanalyticsassociation.org/members/blog_view.asp?id=533997&post=89328&hhSearchTerms=definition+and+of+and+web+and+analyticsNextGeneration: Learning Challenges (n.d.). Learning Analytics [website]. RetrievedOctober 12, 2010 from http://nextgenlearning.com/the-challenges/learning-analyticsNorris, D., Baer, L., Leonard, J., Pugliese, L. and Lefrere, P. (2008). Action Analytics:Measuring and Improving Performance That Matters in Higher Education, EDUCAUSEReview 43(1). Retrieved October 1, 2010from http://www.educause.edu/EDUCAUSE+Review/EDUCAUSEReviewMagazineVolume43/ActionAnalyticsMeasuringandImp/162422Zhang, H. and Almeroth, K. (2010). Moodog: Tracking Student Activity in Online CourseManagement 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.

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