What can institutional big data tell us - Mark Northover - Auckland University of Technology
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For many years AUT has used the Wimba Voice Authoring tools for a range of learning and teaching support activities. Most recently, and most comprehensively, the VA Presenter function has been used by our School of Languages to support a group of papers for a Translating and Interpreting programme, as well as for a Sign Language qualification. Since the announcement of ‘end of life’ for Voice Authoring and its replacement with Voice Thread, we have been working to understand how we can best replicate the previous functionality. After just a short period of the tools being available to us, we have made some progress in understanding how this new toolset will provide what we want, as well as potentially offering us much more. This session will present some of our initial findings, as well as invite others to discuss their experiences and opinions.
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What can institutional big data tell us - Mark Northover - Auckland University of Technology
- 1. Some initial findings and thoughts What can Institutional BIG DATA tell us?
- 2. Some initial work • The following few slides are courtesy of the AUT Strategy and Planning department • Used with permission and thanks 2
- 3. Auckland University of Technology • Student numbers on the rise (currently 2nd largest university in NZ) • Student retention rates protect 5% of our funding. 3 15000 17000 19000 21000 2012 2013 2014 2015 Total students Retention rate
- 4. Institutional survey • What stage are you at – use the TLCANZ app to respond to the poll 4
- 5. Behavioural Correlation with Success • There is a positive relationship between borrowing library books and student success • More pronounced in Pacific students • Very similar pattern when you look at ‘interactions’ with Blackboard 5
- 6. But what about attendance? 6
- 7. What measures can tell us about students on campus? 7 You’re ‘around’ here Wi-Fi PC Lab - Using Printing - Collecting Timetabled Lectures Timetabled Tutorials Library - Books Cashiers Payment Kiosks Laptop Loan Attendance Register You ‘should’ be here You ‘are’ here Attendance Audit Someone ‘is’ here
- 8. Some typical students? 8 JessLily Stan StephBobby
- 9. Attendance Rate 9 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1 2 3 4 5 6 7 8 9 10 11 12 AverageWeeklyAttedanceRate Semester week Jess: No stamina! Lily: Teacher’s pet Stan: Where’s the campus? Steph: OMG! Exam time! Bobby: Late bloomer
- 10. How do they do? 10 Size of Different Attendance Group Teacher's pet No stamina OMG, exam time Late bloomer 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Teacher's Pet Late Bloomer OMG, Exam Time No Stamina Where's the Campus? Paper Pass Rate of Different Attendance Groups
- 11. Learning Analytics Collecting, analysing and reporting data about students in order to improve learning outcomes and student success
- 12. What the students think 12 “I would love an app so I can keep up with my classes, grades and appointments all in one place. I would really really like to know where I am with grades compared to the rest of the class” “This system is fun. It will motivate and makes you feel more inclined to put more effort in, and the lecturers can measure your effort through this system”
- 13. Where to next? • Blackboard data imported nightly • Exploring what data is available • What, why and how??? 13
- 14. PowerView 14
- 15. Can we trust the data? 15 Data warehouse Google Analytics ???
- 16. 16 What happened to the 2014 login count?
- 17. Understanding what the data means 17
- 18. Some emerging Bb discoveries 18 Student grade correlates with average Bb login count No surprises there!
- 19. GPA against school decile rating 19
- 21. What are your expectations of institutional data? • padlet.com/mark_northover/TLCANZ16 21
- 22. What are we hoping to achieve? 22 • As yet, still unsure • Waiting for a lightning bolt of inspiration 940 × 379 - advertisementfeature.cnn.com
- 23. The End 23 • Thank you so much mark.northover@aut.ac.nz @kiwimarc
Editor's Notes
- Initial investigations show clear correlations between ‘learning activity’ and success Two early identified learning activities are using the library, and accessing Blackboard
- We know where you ‘should’ be from the timetable We know the vicinity of where your devices are – which implies where you are Few examples of knowing definitely where you are
- Here are some of our students Lily is 18 and a full time student coming straight from school last year. She has excellent NCEA results Lily is on campus every day and doesn’t miss a class. She uses Blackboard a lot too for resources, assessment , contacting her lecturer She has created a study group with friends from school Stan Stan is a part time student who has a family. He never did NCEA but has held jobs since he left school 10 years ago. He lives in West Auckland, has a part time job of 35 hours per week on the North Shore and comes to AUT for two classes, Monday and Wednesdays. He has missed about a third of his classes. He hasn’t got into Blackboard and has trouble getting access to a computer at home or at work. He knows he can contact the lecturers but doesn’t really have the time to do so. Jess Jess is 18, came from school, with good NCEA results. She is the first in her family to come to university. She started off really positively with her 4 papers but is having trouble managing her time. She has a part time job. None of her school friends came to university so she gets pressure to join them at nights and on weekends. They all have jobs and money and she is wondering about whether she is doing the right thing coming to university. Bobby Bobby is a first year student from school who had average NCEA results. He had a great time at Orientation, made lots of friends and has spent a lot of time socialising with them early in the semester. He is a full time student, does some part time work for his mother in her business. He thought he wouldn't have any problems at University but the first few assessments were a shock and he is trying hard to get up to date with his work and keep on track. Steph Steph had a gap year and is still working pretty much fulltime in the job she had last year. She is also a full time student. She hasn’t used blackboard much at all and attends about half her classes. She has just realised that the end of semester is upon her and she hasn’t handed in some pieces of work. What are their chances?
- Attendance is the most important indicator of success Lily is a full time student coming straight from school last year. Highest paper success Lowest average age (21) More likely to be full-time Highest Blackboard usage Highest NCEA Results Stan Lowest paper success Highest average age (26) Part time Lowest Blackboard usage Lowest NCEA Results or none Jess HighBlackboard usage Second lowest NCEA results More likely to be female Bobby Far more likely to be first year students Likely to be full-time Average, average, average Steph Otherwise average More likely to be female
- But our big focus has been on learning analytics We found that students who engage with AUT (e.g. use the library, Blackboard, attend lectures, etc.) are more likely to pass their courses. Got us thinking about engagement and success in general– how we can support students to succeed. Known indicators – demographics, secondary school performance, deprivation status, first family, past pass rates Behavioural indicators– library usage, LMS engagement, attendance, computer lab usage
- Data fields currently being archived into the data warehouse
- Had to use PhotoShop to shift the decile 10 column as the columns are sorted as strings not as numbers
- Still learning to use the PowerView system – eg in this chart, haven’t found a way of resetting the 0 point for the y-axis so the variations are exaggerated.