The document discusses learning analytics, which is defined as the measurement, collection, analysis and reporting of learner data to optimize learning. It describes how data from student profiles, activities, course content and results can be collected and analyzed descriptively, diagnostically, predictively and prescriptively. The document also addresses ethical concerns regarding data privacy, transparency and ensuring analytics are used to benefit students. It provides examples of how different stakeholders may use analytics and discusses the Open University's principles of applying analytics in an ethical manner that respects student consent and privacy.

1. Teaching & Learning Forum
By Moodlerooms

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. 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

4. 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

5. 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

6. DESCRIPTIVE ANALYTICS
What has happened?

7. DIAGNOSTIC ANALYTICS
Why this happened?

8. PREDICTIVE ANALYTICS
What will happen?

9. PRESCRIPTIVE ANALYTICS
What to do?

10. What data is there on students?
Profile Activity
Content Results
Data Sets

11. Profile
• Prior skill set
• Prior examination results
• Prior subject choice
• Prior examination levels
• Demographics

12. Activity
• Library visits
• Number of books / resources used
• Class attendence
• Wifi access
• Online systems access

13. Content
• Which modules
• How many modules
• Level of modules
• Workload of modules

14. Results
• Year completion
• Module completion
• Module grades
• Assignment grades
• Question level success
• Surveys
• Competency assessments
• Competency related success

15. Key Goals
• Improve student success
• Improve student retention
• Improve the learning experience

16. WHO
WHAT
WHERE
WHEN
WHY

17. 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

18. 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?

19. What
What data and reports are they going to need for their
usage?

20. Where
Where do they need these reports and data?
Where and how will they be accessing them

21. 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

22. Why
Why are they going to use it?

23. 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

24. WHY ANALYSE
Applying Analytics to learning

25. “With this data available
it is wrong to withhold it from the students
themselves”

26. What would a student do with the
information he is given through learning
analytics?

27. What would a lecturer do with the
information he is given about a student
through learning analytics?

28. What would a lecturer do with the
information he is given about his course
through learning analytics?

29. SOME QUESTIONS

30. 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?

31. Teacher
• 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?
• Are students using the resources? Which resources?
When ?
• With which resources are students outcomes the best
in assessments?

32. 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?

33. 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?

34. ETHICAL CONCERNS
Applying Morals to Analytics

35. 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

36. Legal issues
• Data protection laws
• Security policies
• Access policies
• Terms of use
• Student awareness
• Student Impact

37. THE OPEN UNIVERSITY
An example of transparency in analytics

38. 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

39. 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

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