Learning Analytics: Seeking new insights from educational data
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1) Learning analytics seeks new insights from educational data by measuring, collecting, analyzing and reporting data about learners and learning environments to optimize learning. 2) There are three eras of social science research: collecting simple data on important questions; getting the most from little data; and today's "big data" deluge allowing new questions. 3) Educational data can be analyzed through psychometrics, educational data mining, and learning analytics, typically focusing on assessment, learning over time, and wider contexts respectively.
![[1] UCT Student Experience Survey
• Understand students’
overall experience
• Data to effect change,
improve decisions and
policies, affirm good
practices & quality
assure
• Good practice](https://image.slidesharecdn.com/seminar2014leaninganalytics-cput-share-140527095131-phpapp01/85/Learning-Analytics-Seeking-new-insights-from-educational-data-7-320.jpg)
![[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](https://image.slidesharecdn.com/seminar2014leaninganalytics-cput-share-140527095131-phpapp01/85/Learning-Analytics-Seeking-new-insights-from-educational-data-9-320.jpg)
![[3] UCT and social media
Prominent links to:
– Facebook
– Flickr
– LinkedIn
– Twitter](https://image.slidesharecdn.com/seminar2014leaninganalytics-cput-share-140527095131-phpapp01/85/Learning-Analytics-Seeking-new-insights-from-educational-data-10-320.jpg)
![[3] Developing learning analytics](https://image.slidesharecdn.com/seminar2014leaninganalytics-cput-share-140527095131-phpapp01/85/Learning-Analytics-Seeking-new-insights-from-educational-data-21-320.jpg)
Learning Analytics: Seeking new insights from educational data
- 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. 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. Age of Big Data Source: The Economist
- 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. ASKING NEW QUESTIONS Three eras of social research
- 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. [1] UCT Student Experience Survey • Understand students’ overall experience • Data to effect change, improve decisions and policies, affirm good practices & quality assure • Good practice
- 9. [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
- 10. [3] UCT and social media Prominent links to: – Facebook – Flickr – LinkedIn – Twitter
- 11. 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
- 12. Twitter: apps & locations 27% 36% 20% 17% 1 2 3 4 Smartphone geo-location Cell phones Blackberry
- 13. Twitter: tweeter relationships Frequent tweeters: 1. Drama student (162) 2. UCT Radio (132) 3. Science student (84)
- 14. Twitter: viral #UCT Varsity Cup final Helicopter crash 6 months of tweets
- 15. Flickr: helicopter crash at UCT Ian Barbour - http://www.flickr.com/people/barbourians/
- 16. 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?
- 17. Facebook: all friend relationships Paul Butler http://www.facebook.com/notes/facebook-engineering/visualizing-friendships/469716398919
- 18. 1st year course combinations at UCT Health Sciences Engineering Humanities Science Commerce
- 19. DIFFERENT ANALYTICAL TOOLS Three approaches to educational data
- 20. 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)
- 21. [3] Developing learning analytics
- 22. 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
- 23. 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
- 24. 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
- 25. 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
- 26. 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
- 27. 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
- 28. Example of tools: RapidMiner
- 29. Sociogram of a discussion forum Dawson (2010)
- 30. 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
- 33. CHANGING ROLES OF ANALYTICS The future with more data
- 34. 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)
- 35. 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)
- 36. 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)
- 37. 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
- 38. 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
- 39. 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.