Learning Analytics
- 1. ANALYSING ANALYTICS Gavin Henrick Learning Technology Services
- 2. This Wikipedia and Wikimedia Commons image is from the user Chris 73 and is freely available at commons.wikimedia.org/wiki/File:Tokyo_University_Entrance_Exam_Results_6.JPG under the creative commons cc-by-sa 3.0 license.
- 3. LEARNING ANALYTICS What is it?
- 4. What is Learning Analytics? “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. ” Wikipedia http://en.wikipedia.org/wiki/Learning_analytics “Field associated with deciphering trends and patterns from educational big data, or huge sets of student-related data, to further the advancement of a personalized, supportive system of higher education” 2013 Horizon Report http://net.educause.edu/ir/library/pdf/HR2013.pdfz
- 5. How? “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. ” Wikipedia http://en.wikipedia.org/wiki/Learning_analytics “Field associated with deciphering trends and patterns from educational big data, or huge sets of student-related data, to further the advancement of a personalized, supportive system of higher education” 2013 Horizon Report http://net.educause.edu/ir/library/pdf/HR2013.pdfz
- 6. Why? “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. ” Wikipedia http://en.wikipedia.org/wiki/Learning_analytics “Field associated with deciphering trends and patterns from educational big data, or huge sets of student-related data, to further the advancement of a personalized, supportive system of higher education” 2013 Horizon Report http://net.educause.edu/ir/library/pdf/HR2013.pdfz
- 7. DESCRIPTIVE ANALYTICS What has happened?
- 8. DIAGNOSTIC ANALYTICS Why this happened?
- 9. PREDICTIVE ANALYTICS What will happen?
- 10. PRESCRIPTIVE ANALYTICS What to do?
- 11. What data is there on students? Profile Activity Content Results
- 12. Profile • Prior skill set • Prior examination results • Prior subject choice • Prior examination levels • Demographics
- 13. Activity • Library visits • Number of books / resources used • Class attendence • Wifi access • Online systems access
- 14. Content • Which modules • How many modules • Level of modules • Workload of modules
- 15. Results • Year completion • Module completion • Module grades • Assignment grades • Question level success • Surveys • Competency assessments • Competency related success
- 16. Key Goals • Improve student success • Improve student retention • Improve the learning experience
- 18. Who are we thinking about? Consider each of the following questions from the position of • A student • A teacher/lecturer • A programme /course coordinator • Student support staff • Central registry
- 19. Who Who is going to be using the data or the reports using the data? What controls are needed to ensure only those who should access them get access?
- 20. What What data and reports are they going to need for their usage?
- 21. Where Where do they need these reports and data? Where and how will they be accessing them
- 22. When When do they need to get the data, reports - Different data sources will have different potential latency - Different data sets may require different timeframes for usefulness - Different data sets may be useful at different times of year
- 23. Why Why are they going to use it?
- 24. Useful vs Used • Lots of data may be useful but not used • Having reports available to access is no good if they are not accessed • Important to identify what will be used and how
- 25. WHY ANALYSE Applying Analytics to learning
- 26. “With this data available it is wrong to withhold it from the students themselves”
- 27. What would a student do with the information he is given through learning analytics?
- 28. What would a lecturer do with the information he is given about a student through learning analytics?
- 29. What would a lecturer do with the information he is given about his course through learning analytics?
- 30. SOME QUESTIONS
- 31. Student • How well am I doing? • How well am I doing compared to the class? • How are my friends doing? • Which subjects should I invest more time in for greatest benefit? • What am I not doing that others are doing? • Is there anything I should be doing that I am not?
- 32. Teacher • How well are my students doing? • How well are they doing compared to the class? • How well are they doing compared to other years? • Which areas of the curriculum are getting the worst / best results? • Which learning outcomes are not being met? • Are students using the resources? Which resources? When ? • With which resources are students outcomes the best in assessments?
- 33. Support • How well are students doing? • How well are they doing compared to the class? • How well are they doing compared to other years? • Which students are in need of help on a specific subject? • Which students are in need of help across many subjects / in general?
- 34. Admins • Which courses are students not engaging in? • Which courses are teachers not engaging in? • Which courses are students underperforming in? • Which courses are generating the highest? • Which students are at risk in a course? • Which students are at risk in multiple courses?
- 35. ETHICAL CONCERNS Applying Morals to Analytics
- 36. Data and reporting concerns Some issues for discussion: • Transparency on data acquisition • Secure data storage, retention periods • Ownership of data • Purpose for reporting on different themes • Access to different data
- 37. Legal issues • Data protection laws • Security policies • Access policies • Terms of use • Student awareness • Student Impact
- 38. THE OPEN UNIVERSITY An example of transparency in analytics
- 39. The Open University 8 key principles Principle 1: Learning analytics is an ethical practice that should align with core organisational principles, such as open entry to undergraduate level study. Principle 2: The OU has a responsibility to all stakeholders to use and extract meaning from student data for the benefit of students where feasible. Principle 3: Students should not be wholly defined by their visible data or our interpretation of that data. Principle 4: The purpose and the boundaries regarding the use of learning analytics should be well defined and visible. Principle 5: The University is transparent regarding data collection, and will provide students with the opportunity to update their own data and consent agreements at regular intervals. Principle 6: Students should be engaged as active agents in the implementation of learning analytics (e.g. informed consent, personalised learning paths, interventions). Principle 7: Modelling and interventions based on analysis of data should be sound and free from bias. Principle 8: Adoption of learning analytics within the OU requires broad acceptance of the values and benefits (organisational culture) and the development of appropriate skills across the organisation. See: http://www.open.ac.uk/students/charter/essential-documents/ethical-use-student- data-learning-analytics-policy
- 40. Jisc work in the UK • Code of practice for Learning Analytics – Public consultation • http://sclater.com/blog/code-of-practice-for-learning-analytics- public-consultation/ • Final version of the Code in June • Interesting breakdown including access, and action responsibilities
- 41. Mobile App. What students want • http://sclater.com/blog/what-do-students-want-from-a- learning-analytics-app/ • Some points to consider • What is analytics to the student • What do they want tracked • What is the information that they want access to easily
- 42. References Finding the Prodigal Student: Academics' Analytics at UCD http://www.heanet.ie/conferences/2014/talks/id/97 Making Sense of Data from your LMS http://www.heanet.ie/conferences/2014/talks/id/98 Code of practice for learning analytics – A literature review of the ethical and legal issues http://analytics.jiscinvolve.org/wp/2014/12/04/jisc-releases-report-on-ethical-and- legal-challenges-of-learning-analytics/ Learning Analytics – The current state of play in UK Higher and further education http://analytics.jiscinvolve.org/wp/2014/11/20/jisc-releases-new-report-on-learning- analytics-in-the-uk/ Ethical use of Student Data for Learning Analytics Policy – The Open University http://www.open.ac.uk/students/charter/essential-documents/ethical-use-student- data-learning-analytics-policy