Presented at the UCISA Digital Education Group event 'Beyond lecture capture: enhancing the student experience', 14 June 2018, Woburn House, London. Link to video now included - see slide 22. Note - Unfortunately the audio and video are out of sync by about 6 seconds; a fix is being investigated by the provider. Note 2 - I recommend listening at 1.5x speed!

1. Following the traces
What learning analytics can tell us about
student use of lecture recordings
Moira Sarsfield
Imperial College London
@msars

2. Following the traces
Learning analytics study of student use of video recordings on 17
modules across Natural Sciences in 2014-15 academic year.
• Advice for staff and students on how best to use recordings
• Topics for further investigation
• Insights into requirements for a learning analytics system
@msars

3. “The measurement, collection, analysis and reporting of
data about learners and their contexts, for purposes of
understanding and optimizing learning and the
environments in which it occurs.”
International Conference on Learning Analytics (LAK 2011)
Descriptive
Learning analytics
Predictive
Analysis
Click data
Student data
Context data

4. Used legally and ethically
Visualization of all editing activity by user "Pearle" on Wikipedia (Pearle is a robot).

5. Comprehensive

6. Student
Study habits
Learning differences
EAL
Year of study
Lecture attendance
Procrastination
Recording
Type of lecture
Content
Lecturer
characteristics
Duration
Date/time
Wider context
Institution
Regulations
Tech implementation
Subject area
Timetabling
Assessment regime
Categorised
What factors might affect student use of lecture recordings?

7. Validated
Heteroaromatic Chemistry Problem class

8. Comparable

9. Comparing like with like
Macromolecular Structure and FunctionDifferential Equations

10. Comparing like with like
Macromolecular Structure and FunctionDifferential Equations

11. Differences between subjects
Subject Students
who
viewed (%)
Average of all
recorded minutes
viewed (%)
Initial accesses
in the learning
period (%)
Mathematics 89 10.5 87.5
Life Sciences 87.6 30.6 42.2
Macromolecular
Structure and
Function
Statistics

12. Looking at subgroups
Macromolecular Structure and Function

13. Looking at subgroups
Macromolecular Structure and Function

14. Looking at subgroups
Macromolecular Structure and FunctionMacromolecular Structure and Function

15. Use over time
Applied Molecular Biology

16. Slide courtesy of Dr Steve Cook, Biological Sciences FirstYear Convenor, Imperial College

17. Use over time
2014-15
36% of all mins viewed
2016-17
32% of all mins viewed
Applied Molecular Biology

18. Impact of delayed release
Do not delay the release of recordings; delayed release results in lower usage.
Differential Equations

19. Impact of delayed release
2014-15
– 11% of all mins viewed
– 49% in learning period
No grade data available
2016-17
provisional data
– 19% of all mins viewed
– 65% in learning period
Statistical Physics

20. Descriptive learning analytics
Data – student and context data / click data and clock data
• Used ethically and legally
• Comprehensive
• Categorised
• Validated
Analysis – report / visualise and investigate differences
• Compare subgroups
• Investigate use over time
Outcomes – improving teaching, learning, implementation
• Specific actions, advice, topics for further investigation

21. Following the traces
Moira Sarsfield, Imperial College London
msars@imperial.ac.uk
Picture Credits
Animal footprints in the snow cc-by-sa/2.0 - © Evelyn Simak - geograph.org.uk/p/5697439
Allsorts Public domain - Johnny Magnusson - http://www.freestockphotos.biz/stockphoto/2149
Apples cc-by/2.0 - © KateTer Haar - https://www.flickr.com/photos/katerha/5241746230
Pearle cc-by/2.0 - © Fernanda B.Viégas - https://commons.wikimedia.org/wiki/File:Viegas-
UserActivityonWikipedia.gif
@msars