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Analysing Learning Analytics
James Little
Learning Technologist
SDDU
Analysing Learning Analytics
• What is learning analytics?
• How can learning analytics be performed?
– Considerations
– E...
WHAT IS LEARNING ANALYTICS?
What is Learning Analytics?
The 2013 NMC Horizon Report describes
learning analytics as:
“[a] field associated with deciph...
What is Learning Analytics?
What is the purpose of learning analytics?
A JISC CETIS (2012) report identifies a focus on us...
What is Learning Analytics?
Does Learning Analytics = Big Data?
What is Learning Analytics?
Does Learning Analytics = Big Data?
Yes.
What is Learning Analytics?
Big Data is…
• Multiple sets of data from different areas and
attempting to analyse these - i....
What is Learning Analytics?
Does Learning Analytics = Big Data?
Yes… and No!
What is Learning Analytics?
Big Data is not simply a large sample size
(M Callaghan 2014. - HPC Computer Officer)
What is Learning Analytics?
Learning Analytics can look at big data,
large sample sizes of data, or even small
sample size...
HOW CAN LEARNING ANALYTICS BE
PERFORMED? - CONSIDERATIONS
How can learning analytics be
performed?
• 3 considerations:
1. Context (what you want to know) affects:
2. Sample Size (s...
Context
• What do you want to find out?
– To improve something?
– To gain insight into an unknown?
– To provide detailed s...
Context
A JISC CETIS (2012) report identifies a focus on using it:
• for individual learners to reflect on their achieveme...
Defining Sample Size
Small: Individuals on one unit (i.e.
Medical Anatomy)
Medium: Individuals on
the unit stretching back...
Defining Scope
Narrow: Outcomes for one
session on – i.e. Philosophy Logic
and Reason, session 4
Medium: Outcomes
for a wh...
Defining Scope
Narrow: 1 student’s
information at UoL and their
programme of study
Medium: A whole
cohort of student’s
inf...
Sample Size/Scope
Big
Medium
Small
Scale of challenge!
MOOCS
VIRTUAL LEARNING ENVIRONMENTS
INSTITUTIONAL DATA
UNIT ACTIVIT...
Sample Size/Scope
• Historically, information at the small sample size and scope
has been available for analysis – thinkin...
HOW CAN LEARNING ANALYTICS BE
PERFORMED? – EXAMPLES
Examples
• VLE – Applied and Professional Ethics Online Masters IDEA
CETL (University of Leeds)
– Discussion forum analyti...
Examples
• MOOCS / FutureLearn – Introduction to Anatomy
• Statistics produced on:
– Signups
– Completion
– Discussion eng...
Examples
• MOOCS / FutureLearn – First 8 Courses
– https://about.futurelearn.com/blog/measuring-our-first-eight-courses/
Examples
• MOOCS / FutureLearn – First 8 Courses
– https://about.futurelearn.com/blog/measuring-our-first-eight-courses/
Examples
• Sample size large, scope medium.
• MOOCS / FutureLearn – First 8 Courses
– https://about.futurelearn.com/blog/m...
Examples
– Quiz/Polling tool
– Can be used to analyse understanding of a group
– Often used within one session/unit
– Pote...
Examples
• Past examples have been re-active and aimed at
those running the course (academics and
professional support)
• ...
Examples
• Stanford Lytics Labs – Sherif Halawa
– http://web.stanford.edu/~halawa/cgi-bin/
• Developing Tools that will an...
Examples – WorldWide
• The Glass Classroom
• Santa Monica College’s Glass Classroom initiative strives to enhance student ...
Tools Summary
• For small and large sample sizes – but small scope:
– Analytics can take place within a specific platform
...
Tools Summary
• “Big Data” – (multiple sets of data from
different areas) requires:
– Sorting (combining and standardising...
WHAT IMPACTS CAN IT HAVE ON YOUR
LEARNING, TEACHING OR RESEARCH?
What impacts can it have on your
learning, teaching or research?
• Learning & Teaching:
– Using data produced by platforms...
What impacts can it have on your
learning, teaching or research?
• Research
– Connect any research data back into teaching...
Impacts - Challenges
• To liberate and find multiple sets of institution-wide
data.
• Re-surfacing information in a timely...
Next Steps…
• What could you do with information already available to you?
– Feedback to students
– Enable students to acc...
Further Resources & References
• Learning Analytics Community Exchange
– http://www.laceproject.eu/lace/
– http://www.lace...
THANK YOU
QUESTIONS & DISCUSSION
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Learning Analytics

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A introduction to Learning Analytics

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Learning Analytics

  1. 1. Analysing Learning Analytics James Little Learning Technologist SDDU
  2. 2. Analysing Learning Analytics • What is learning analytics? • How can learning analytics be performed? – Considerations – Examples • What impacts can it have on your learning, teaching or research?
  3. 3. WHAT IS LEARNING ANALYTICS?
  4. 4. What is Learning Analytics? The 2013 NMC Horizon Report describes learning analytics as: “[a] 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.”
  5. 5. What is Learning Analytics? What is the purpose of learning analytics? A JISC CETIS (2012) report identifies a focus on using it: • for individual learners to reflect on their achievements and patterns of behaviour in relation to others; • as predictors of students requiring extra support and attention; • to help teachers and support staff plan supporting interventions with individuals and groups; • for functional groups such as course team seeking to improve current courses or develop new curriculum offerings; and • for institutional administrators taking decisions on matters such as marketing and recruitment or efficiency and effectiveness measures.
  6. 6. What is Learning Analytics? Does Learning Analytics = Big Data?
  7. 7. What is Learning Analytics? Does Learning Analytics = Big Data? Yes.
  8. 8. What is Learning Analytics? Big Data is… • Multiple sets of data from different areas and attempting to analyse these - i.e. start with “dirty data” is big data (VLE/Student Records). – Data has to be sorted first then analysed together – Proposed NHS core.health data from multiple areas
  9. 9. What is Learning Analytics? Does Learning Analytics = Big Data? Yes… and No!
  10. 10. What is Learning Analytics? Big Data is not simply a large sample size (M Callaghan 2014. - HPC Computer Officer)
  11. 11. What is Learning Analytics? Learning Analytics can look at big data, large sample sizes of data, or even small sample sizes. Purpose to look at both trends and patterns which can then enable action on a local, personal level as well as to inform wider decisions.
  12. 12. HOW CAN LEARNING ANALYTICS BE PERFORMED? - CONSIDERATIONS
  13. 13. How can learning analytics be performed? • 3 considerations: 1. Context (what you want to know) affects: 2. Sample Size (small or large) 3. Scope (sliding scale between narrow or wide) (Scope and sample size can be interrelated)
  14. 14. Context • What do you want to find out? – To improve something? – To gain insight into an unknown? – To provide detailed support?
  15. 15. Context A JISC CETIS (2012) report identifies a focus on using it: • for individual learners to reflect on their achievements and patterns of behaviour in relation to others; • as predictors of students requiring extra support and attention; • to help teachers and support staff plan supporting interventions with individuals and groups; • for functional groups such as course team seeking to improve current courses or develop new curriculum offerings; and • for institutional administrators taking decisions on matters such as marketing and recruitment or efficiency and effectiveness measures.
  16. 16. Defining Sample Size Small: Individuals on one unit (i.e. Medical Anatomy) Medium: Individuals on the unit stretching back 5 years (i.e. Medical Anatomy) Large: Similar units across the sector (i.e. Medical Anatomy unit in all the Med Schools) (Context: looking at a subject area within a discipline)
  17. 17. Defining Scope Narrow: Outcomes for one session on – i.e. Philosophy Logic and Reason, session 4 Medium: Outcomes for a whole unit – all of Philosophy Logic and Reason Wide: Outcomes for all units that are taken by the student at UoL. (Context: A Philosophy student)
  18. 18. Defining Scope Narrow: 1 student’s information at UoL and their programme of study Medium: A whole cohort of student’s information at UoL on one course Wide: All of the students at UoL (Context: Student experience)
  19. 19. Sample Size/Scope Big Medium Small Scale of challenge! MOOCS VIRTUAL LEARNING ENVIRONMENTS INSTITUTIONAL DATA UNIT ACTIVITY PROGRAMMES SCHOOLS DEPARTMENT Learning Analytics can take place at every level
  20. 20. Sample Size/Scope • Historically, information at the small sample size and scope has been available for analysis – thinking about module information, marks, information with a school or department. • A focus on quantitative data • New platforms such as MOOCs and other at-scale platforms (VLEs now included) have enable the potential for more information to be produced and collected. • This works well within specific contexts (such as a one MOOC or platform) • Challenges are across education as a whole to liberate and find multiple sets of institution-wide data.
  21. 21. HOW CAN LEARNING ANALYTICS BE PERFORMED? – EXAMPLES
  22. 22. Examples • VLE – Applied and Professional Ethics Online Masters IDEA CETL (University of Leeds) – Discussion forum analytics used to keep track of activity – http://www.slideshare.net/MeganKime/meaningful-discussion-activity-for-online-distance-learners • VLE - Advanced Nursing Studies (University of Sheffield) – Gradebook kept track of formative and summative assignments – Useful for predicting and supporting student needs – http://www.sheffield.ac.uk/snm/postgraduatetaught/mmed-sci-advanced-nursing-studies • Tools used those available within a specific platform and for a specific course. • Sample size and scope small.
  23. 23. Examples • MOOCS / FutureLearn – Introduction to Anatomy • Statistics produced on: – Signups – Completion – Discussion engagement – Drop-out rate • Used to inform next approaches to MOOC design • Sample size large, scope small.
  24. 24. Examples • MOOCS / FutureLearn – First 8 Courses – https://about.futurelearn.com/blog/measuring-our-first-eight-courses/
  25. 25. Examples • MOOCS / FutureLearn – First 8 Courses – https://about.futurelearn.com/blog/measuring-our-first-eight-courses/
  26. 26. Examples • Sample size large, scope medium. • MOOCS / FutureLearn – First 8 Courses – https://about.futurelearn.com/blog/measuring-our-first-eight-courses/
  27. 27. Examples – Quiz/Polling tool – Can be used to analyse understanding of a group – Often used within one session/unit – Potential to combine information across sessions
  28. 28. Examples • Past examples have been re-active and aimed at those running the course (academics and professional support) • Power lies in personalising data for the individuals taking part and also being pro-active
  29. 29. Examples • Stanford Lytics Labs – Sherif Halawa – http://web.stanford.edu/~halawa/cgi-bin/ • Developing Tools that will analyse a large data set on online courses: – Perform analysis to predict when addition support needed – Predict potential drop-outs – Offer tailored support • Sample-size large, scope large.
  30. 30. Examples – WorldWide • The Glass Classroom • Santa Monica College’s Glass Classroom initiative strives to enhance student and teacher performance through the collection and analysis of large amounts of data. Using real-time feedback, adaptive courseware adjusts based on an individual’s performance in the classroom in order to meet educational objectives. • jPoll at Griffith University • jPoll is an enterprise-wide tool developed by Griffith University in Australia, directed at capturing, maintaining, and engaging students in a range of interactive teaching situations. Originally developed as a replacement for clicker-type technologies, jPoll is helping educators identify problem areas for students via learning analytics. • Predictive Learning Analytics Framework • The American Public University System is working with Western Interstate Commission for Higher Education’s Cooperative for Educational Technologies to share a large data pool of student records across ten universities. Their goal is for this data to inform strategies for improving student learning outcomes. • Stanford University’s Multimodal Learning Analytics • In partnership with the AT&T Foundation, Lemann Foundation, and National Science Foundation, Stanford is exploring new ways to assess project-based learning activities through students’ gestures, words, and other expressions. Information from: http://www.edudemic.com/learning-analytics-in-education/
  31. 31. Tools Summary • For small and large sample sizes – but small scope: – Analytics can take place within a specific platform (VLE/FutureLearn) – Also can be analysed using ‘standard’ statistical tools such as: • R • SPSS • MatLab
  32. 32. Tools Summary • “Big Data” – (multiple sets of data from different areas) requires: – Sorting (combining and standardising the data) – Analysis – driven by needs (carried out by new techniques i.e. Social Network Analysis / Pattern Recognition) – Re-surfacing • To individuals to look at their fit within the wider context • To individual providing support to identify support within a unit of study • To committees making specific strategic decisions
  33. 33. WHAT IMPACTS CAN IT HAVE ON YOUR LEARNING, TEACHING OR RESEARCH?
  34. 34. What impacts can it have on your learning, teaching or research? • Learning & Teaching: – Using data produced by platforms or specific tools can directly inform the learning and education process in situ (formative assessment). • Can be used by the academic • Can be used by the student – Looking at overall trends with a unit, programme or field can enable decisions for next time it is run.
  35. 35. What impacts can it have on your learning, teaching or research? • Research – Connect any research data back into teaching activities – Use information generated from learning and teaching to generate research
  36. 36. Impacts - Challenges • To liberate and find multiple sets of institution-wide data. • Re-surfacing information in a timely and specific way
  37. 37. Next Steps… • What could you do with information already available to you? – Feedback to students – Enable students to access – Share with colleagues • Could you enable more information to be generated, through use of new tools or techniques? • Could you use information from multiple sources to provide support? • What scope and sample size would be involved?
  38. 38. Further Resources & References • Learning Analytics Community Exchange – http://www.laceproject.eu/lace/ – http://www.laceproject.eu/blog/using-data-to-improve-student-success/ – http://www.laceproject.eu/blog/moocs-learning-analytics/ • Stanford Lytics Lab – http://lytics.stanford.edu/ • Edudemic http://www.edudemic.com/learning-analytics-in-education/ • Greller, Wolfgang; Drachsler, Hendrik (2012). "Translating Learning into Numbers: Toward a Generic Framework for Learning Analytics." (pdf). Educational Technology and Society 15 (3): 42–57. • Cooper, Adam. A Brief History of Analytics A Briefing Paper. CETIS Analytics Series. JISC CETIS, November 2012. http://publications.cetis.ac.uk/wp- content/uploads/2012/12/Analytics-Brief-History-Vol-1-No9.pdf.
  39. 39. THANK YOU
  40. 40. QUESTIONS & DISCUSSION

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