Learning Analytics: Seeking new insights from educational data

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CPUT Fundani TWT - 22 May 2014

Analytics is a buzzword that encompasses the analysis and visualisation of big data. Current interest results from the growing access to data and the many software tools now available to analyse this data in Higher Education, through platforms such as Learning Management Systems. This seminar provides an overview of current applications and uses of learning analytics and how it can help institutions of learning better support their learners. The illustrative examples look at institutional and social media data that together provide rich insights into institutional, teaching and learning issues. A few simple ways to perform such analytics in a context of Higher Education will be introduced.

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Learning Analytics: Seeking new insights from educational data

  1. 1. Learning Analytics seeking new insights from educational data Andrew Deacon Centre For Innovation in Learning and Teaching University of Cape Town Teaching and Learning with Technology workshop, CPUT, 2014
  2. 2. Outline • What is changing with ‘big data’ • Three eras of social science research • Three ways educational data is analyzed • Changing roles of analytics with more data
  3. 3. Age of Big Data Source: The Economist
  4. 4. Learning Analytics … is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs. https://tekri.athabascau.ca/analytics
  5. 5. ASKING NEW QUESTIONS Three eras of social research
  6. 6. Three eras of social research 1. Age of Quételet collect data on simple & important questions 2. Classical period get the most information from a little data 3. Present day big data deluge of data and questions
  7. 7. [1] UCT Student Experience Survey • Understand students’ overall experience • Data to effect change, improve decisions and policies, affirm good practices & quality assure • Good practice
  8. 8. [2] Are streams being disadvantaged? Within Degree Type: • Differences in mean final mark are significant • Across years, differences in means are similar • Differences in 2013 are not unusual Change in mode of delivery
  9. 9. [3] UCT and social media Prominent links to: – Facebook – Flickr – LinkedIn – Twitter
  10. 10. Twitter: UCT chatter • Looked at 6 months of data April – Sept 2011 • Selected tweets with a UCT hashtag or text #UCT, #Ikeys, University of Cape Town, … • Attributes tweet amplification, app used, location • Dataset Just over 5,000 tweets
  11. 11. Twitter: apps & locations 27% 36% 20% 17% 1 2 3 4 Smartphone geo-location Cell phones Blackberry
  12. 12. Twitter: tweeter relationships Frequent tweeters: 1. Drama student (162) 2. UCT Radio (132) 3. Science student (84)
  13. 13. Twitter: viral #UCT Varsity Cup final Helicopter crash 6 months of tweets
  14. 14. Flickr: helicopter crash at UCT Ian Barbour - http://www.flickr.com/people/barbourians/
  15. 15. Twitter: helicopter crash at UCT 2 hours after the event • Peak of 140 tweets in 5 minutes • Media organisations tweets get re-tweeted • Crash or hard-landing?
  16. 16. Facebook: all friend relationships Paul Butler http://www.facebook.com/notes/facebook-engineering/visualizing-friendships/469716398919
  17. 17. 1st year course combinations at UCT Health Sciences Engineering Humanities Science Commerce
  18. 18. DIFFERENT ANALYTICAL TOOLS Three approaches to educational data
  19. 19. Three approaches to educational data 1. Psychometrics placing measures on a scale (e.g., in assessment) 2. Educational Data Mining focus on learning over time (e.g., in schools) 3. Learning Analytics typically wider contexts (e.g., in universities)
  20. 20. [3] Developing learning analytics
  21. 21. Students’ use of Vula in a course Site visits Chat room activity Sectioning of students Polling of students Content accessed Submission of assignments Submission of assignments
  22. 22. Purdue University's Course Signals • Early warning signs provides intervention to students who may not be performing well • Marks from course • Time on tasks • Past performance Source: http://www.itap.purdue.edu/learning/tools/signals
  23. 23. Advisors – U Michigan • Advisors are key element • Data from LMS – Measures to compare students (LMS performance and LMS usage) – Classifications (<55% red and >85% green) – Visualizations of student performance • Engagement with advisors – Dashboard
  24. 24. Measures to compare students • LMS Gradebook and Assignments – Student score as percentage of total – Class mean score as percentage of total • LMS Presence as proxy for ‘effort’ – Weekly total – Cumulative total
  25. 25. Classifications of cohort Comparisons are intra-class Performance Change Presence Rank Action >= 85% Encourage 75% to 85% < 15% Explore >= 15% < 25% Explore >= 15% >= 25% Encourage 65% to 75% < 15% < 25% Engage < 15% >= 25% Explore >= 15% Explore 55% to 65% >= 10% Explore < 10% Engage < 55% Engage
  26. 26. Advisor support • Shorten time to intervene – Weekly update – Contact ‘red’ students – Useful to prepare for consultation • Contextualizing student performance – Longitude trends (course and degree) – Identify students who don’t need support
  27. 27. Example of tools: RapidMiner
  28. 28. Sociogram of a discussion forum Dawson (2010)
  29. 29. Words used by Lecturers vs Students Used more by Students Used more by Lecturers/tutors ‘Weiten’ – textbook author Marks; thanks; test; Tut; guys Week; pages
  30. 30. MOOC Completion Rates http://www.katyjordan.com/MOOCproject.html
  31. 31. CHANGING ROLES OF ANALYTICS The future with more data
  32. 32. Concerns about Big Data thinking • Does Big Data… – change the definition of knowledge – increase objectivity and accuracy – analysis improves with more data – make the context less critical – availability means using the data is ethical – reduce digital divides See (Boyd & Crawford 2012)
  33. 33. Effective visualisations The success of a visualization is based on deep knowledge and care about the substance, and the quality, relevance and integrity of the content. Tufte (1981)
  34. 34. Correlation and causation • Correlation does not imply causation – Covariation is a necessary but not a sufficient condition for causality – Correlation is not causation (but could be a hint)
  35. 35. Future scenarios • Analytics in educational research: – More data means asking new questions – Interpreting data in a student’s context – Open up discussions and possibilities – New ethical considerations • Visualisations and analytics tools: – Good open source software is available – Encourage people to engage with learning analytics
  36. 36. Software references • Gephi – network analysis, data collection • NodeXL – network analysis, data collection • TAGS – Twitter data collection (Google Drive) • Word cloud – R package (wordcloud) • RapidMiner – Data mining, predictive analytics • Excel – spreadsheet, charts • R – statistical analysis, graphs
  37. 37. Literature references • Boyd, D., Crawford, K. (2012) Critical Questions for Big Data, Information, Communication & Society, 15:5, 662-679 • Dawson, S. (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. • Deacon, A., Paskeviciusat, M. (2011) Visualising activity in learning networks using open data and educational analytics. Southern African Association for Institutional Research Forum, Cape Town. • Berland, M., Baker, R.S., Blikstein, P. (in press) Educational data mining and learning analytics: Applications to constructionist research. To appear in Technology, Knowledge, and Learning. • Hansen, D., Shneiderman, B., Smith, M.A. (2011) Analyzing Social Media Networks with NodeXL: Insights from a Connected World, Morgan Kaufmann Publishers, San Francisco, CA. • Tufte, E. (1981) The visual display of quantitative information. Cheshire, Conn.: Graphics Press.
  38. 38. South African references

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