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What can we learn from learning analytics?

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Slides presented at ALT-C 2016; updated after the presentation.
What can we learn from learning analytics? A case study based on an analysis of student use of video recordings [paper 1247]. Moira Sarsfield, and John Conway

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What can we learn from learning analytics?

  1. 1. What can we learn from learning analytics? A case study based on an analysis of student use of video recordings John Conway Senior Learning Technologist Physical Sciences Imperial College London Moira Sarsfield Senior Learning Technologist Life Sciences Imperial College London
  2. 2. What is LA? JISC Learning analytics refers to the measurement, collection, analysis and reporting of data about the progress of learners and the contexts in which learning takes place. Predictive & Retention HEA Learning Analytics is the process of measuring and collecting data about learners and learning with the aim of improving teaching and learning practice through analysis of the data. Improving learning through better informed teaching practice
  3. 3. Background • The analysis covers 18 UG modules – Across Mathematics, Chemistry, Physics and Life Sciences – For the academic year 2014-2015 – Covering year 1 and year 2 cohorts • A module = a single block of teaching ending in an examination • Students = those who took the module and exam for the first time in 2014-15 • Use of lecture capture measured by accesses and by minutes viewed
  4. 4. • How much use is made of video recordings by students? • Is the use of recordings different for different: – modules and degrees? – groups of students? – types of content? • How does the use of recordings in a course vary over time? Key research questions
  5. 5. Factors in project design • Ethics/privacy important when combining student marks and grades with server access data – Mark and grade information must remain within departments; requires security – Student SpLD status is ‘sensitive data’ under Data Protection Act – Student anonymity must be preserved – anonymisation must be a one way process • Flexibility and ease of use – Excel for departmental use (known product) – R for processing (allows automated proccessing, standardised reporting)
  6. 6. Actionable insights leading to change in practice Process
  7. 7. Do students use the recordings? YES – but use varies considerably
  8. 8. Is there a pattern associated with grades? Not in general
  9. 9. Is there a pattern associated with grades? But, YES, when we look in detail
  10. 10. More/less use by 1st class students? No significant difference between grades
  11. 11. More use by SpLD students? No significant difference observed
  12. 12. More/less use by 1st class students? Yes – where viewing is required There is a pre-recorded Panopto lecture for you to look at before Lecture 2 as we will be flipping the class during the 2pm Lecture 2 session slot.
  13. 13. How much do students generally watch? Varies considerably by degree stream
  14. 14. Are students watching whole lectures? Differs by degree stream and over time
  15. 15. Are students watching whole lectures? Differs by degree stream and over time
  16. 16. Are students watching whole lectures? Differs by degree stream and over time
  17. 17. Does the pattern of use differ by degree? Yes – use in Life Sciences is very different
  18. 18. Timing of use in Life Sciences
  19. 19. Unexpected insights If recordings are released late, they are accessed much less than those released immediately after the lecture. Space in the timetable may be needed to allow students to consolidate learning from one lecture before the next.
  20. 20. Actionable insights Advice for students High-performing students: • View recordings where the lecturer says it is required (e.g. a flipped lecture). • View recordings early, right after the lecture rather than in the revision period. • Maintain their application right through the course. They don’t slack off as the end is in sight. • May or may not use the lecture recordings; likewise poorer students may or may not use them. Success is not directly correlated with lecture recording viewing.
  21. 21. Actionable insights Advice for lecturers and course/degree organisers • Do not delay the release of your recordings – this will result in lower usage. • Think about how recordings are presented – should they be given more/less/equal weight than other learning materials? • Give advice to students on the way you expect them to use lecture recordings. • Consider how lectures are timetabled. Complex lecture content requires time for students to assimilate. • If the pattern of use of recordings is not as expected, investigate why this is so, e.g. look at timing of lectures, content, pattern of assessment, etc. • You can check online which parts of each recording are being viewed most. Training on this functionality will be provided.
  22. 22. • Investigate the reasons for the difference in use of lecture recordings in Life Sciences • Investigate how students attaining different grades use the recordings • Build on the lecture recording analysis, e.g. look at other departments and changes in teaching methods, e.g. implementing flipped classroom or TBL • Apply the methodology and processes more widely to investigate use of other learning materials, e.g. formative quizzes in Blackboard, PeerWise. Future Plans
  23. 23. Lessons for other LA projects • Define what is to be analysed • Define the data to be used • Consider ethics/confidentiality • Consider needs of different stakeholders • Gather data in a structured way • Drill down and analyse at a fine level • Verify the results • Automate the analysis and report writing • Compare, analyse, make recommendations
  24. 24. Contact form Contact Us John Conway Senior Learning Technologist – Physical Sciences j.conway@imperial.ac.uk Moira Sarsfield Senior Learning Technologist – Life Sciences m.sarsfield@imperial.ac.uk

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