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Inaugural lecture: The power of learning analytics to give students (and teachers) what they want!

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Join us at the Berrill Theatre and online on Tuesday 30 January 2018, 6-7pm for the Inaugural Lecture of Professor Bart Rienties, in which he will talk about the power of learning analytics in teaching and learning. Bart Rienties is Professor of Learning Analytics at the Institute of Educational Technology (IET) at The Open University. He is programme director Learning Analytics within IET and head of Data Wranglers, whereby he leads of group of learning analytics academics who conduct evidence-based research and sense making of Big Data at the OU.

As educational psychologist, he conducts multi-disciplinary research on work-based and collaborative learning environments and focuses on the role of social interaction in learning, which is published in leading academic journals and books. His primary research interests are focussed on Learning Analytics, Computer-Supported Collaborative Learning, and the role of motivation in learning. Furthermore, Bart is interested in broader internationalisation aspects of higher education. He has successfully led a range of institutional/national/European projects and received a range of awards for his educational innovation projects.

Bart is World Champion Transplant cycling Team Time Trial 2017, the first academic with a transplant to be promoted to full professor, and a keen explorer of life.

In The power of learning analytics to give students (and teachers) what they want!, Bart will describe how his research into learning analytics is enabling him to predict which learning strategy might work best for each student, and provide different, unique experiences for each depending on what they want. In particular, he will explore how student dispositions like motivation, emotion, or anxiety encourages or hinders effective online learning, and how we may need to adjust our approaches depending on individual differences.

Event programme:

18:00 - 18:45 – The power of learning analytics to give students (and teachers) what they want!

18:45 - 19:00 – Q&A

19:00 - 19:45 – Drinks Reception

There will be time for questions and comments. We very much hope you will be able to attend what promises to be an inspiring event and have your say.

Published in: Education
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Inaugural lecture: The power of learning analytics to give students (and teachers) what they want!

  1. 1. The power of learning analytics to give students (and teachers) what they want! Prof Bart Rienties #OUTalks @DrBartRienties https://pollev.com/bartrienties552
  2. 2. A special thanks to Avinash Boroowa, Shi-Min Chua, Simon Cross, Doug Clow, Chris Edwards, Rebecca Ferguson, Mark Gaved, Christothea Herodotou, Martin Hlosta, Wayne Holmes, Garron Hillaire, Simon Knight, Nai Li, Vicky Marsh, Kevin Mayles, Jenna Mittelmeier, Vicky Murphy, Quan Nguygen, Tom Olney, Lynda Prescott, John Richardson, Jekaterina Rogaten, Matt Schencks, Mike Sharples, Belinda Tynan, Lisette Toetenel, Thomas Ullmann, Denise Whitelock, Zdenek Zdrahal, and others…
  3. 3. A special thanks to Simon Beausaert (Maastricht University), Katerina Bohle Carbonell (Maastricht University), Natasa Brouwer (Universiteit van Amsterdam), Bas Giesbers (Erasmus University Rotterdam), Wim Gijselaers (Maastricht University), Therese Grohnert (Maastricht University), YingFei Heliot (University of Surrey), Nuria Hernandez Nanclares (University of Oviedo), Juliette Hommes (Maastricht University), Anesa Hosein (University of Surrey), Divya Jindal- Snape (University of Dundee), Ian Kinchin (University of Surrey), Piet Kommers (Universiteit Twente), Karen Konings (Maastricht University), Simon Lygo-Baker (University of Surrey), Emma Medland (University of Surrey), Lynn Minnaert (New York University), Alexandra Niculescu (Zurch), Eimar Nolan (Monmouth University) Martin Rehm (Duisburg Uni), Jeremy Roschelle (Stanford), Keetie Roelen (University of Sussex), Mien Segers (Maastricht University), Rhona Sharpe (University of Surrey), Dirk Tempelaar (Maastricht University), Piet van den Bossche (Universiteit Antwerpen), Leendert van Gastel (Universitiet van Amsterdam), Jeroen van Merrienboer (Maastricht University), Cees van de Vleuten (Maastricht University), Simone Volet (Murdoch University), Dominique Waterval (Maastricht University), and others…
  4. 4. But before I talk about my work What else happened that was great in 2017?
  5. 5. Valencia Robbie Tabitha
  6. 6. Valencia Robbie Tabitha Colour Black Black Black Number of paws 4 4 4 Gender Female Male Female Age 12 months 9 months 13 months Can do Favourite toy (when visiting) Soft toy when sleeping “Special” skills (when visiting) So which dog can fetch, recall, roll over, sit and wait on command? 1. Valencia 2. Robbie 3. Tabitha 4. All three 5. None of the three
  7. 7. Valencia Robbie Tabitha Colour Black Black Black Number of paws 4 4 4 Gender Female Male Female Age 12 months 9 months 13 months Can do Fetch, pick up, recall, roll over, sit, wait, etc. Fetch, pick up, recall, roll over, sit, wait, etc. Fetch, pick up, recall, roll over, sit, wait, etc. Favourite toy (when visiting) Soft toy when sleeping “Special” skills (when visiting) A purple squeaky toy is the favourite toy of: 1. Valencia 2. Robbie 3. Tabitha 4. All three 5. None of the three
  8. 8. Valencia Robbie Tabitha Colour Black Black Black Number of paws 4 4 4 Gender Female Male Female Age 12 months 9 months 13 months Can do Fetch, pick up, recall, roll over, sit, wait, etc. Fetch, pick up, recall, roll over, sit, wait, etc. Fetch, pick up, recall, roll over, sit, wait, etc. Favourite toy (when visiting) Purple toy Blue ball Blue ball Soft toy when sleeping “Special” skills (when visiting) Which dog does not sleep with a soft toy (e.g, a Panda, Moose, Monkey): 1. Valencia 2. Robbie 3. Tabitha 4. All three sleep with a soft toy 5. None sleep with a soft toy
  9. 9. Valencia Robbie Tabitha Colour Black Black Black Number of paws 4 4 4 Gender Female Male Female Age 12 months 9 months 13 months Can do Fetch, pick up, recall, roll over, sit, wait, etc. Fetch, pick up, recall, roll over, sit, wait, etc. Fetch, pick up, recall, roll over, sit, wait, etc. Favourite toy (when visiting) Purple toy Blue ball Blue ball Soft toy when sleeping Chocolate Moose Who needs a soft toy? Panda “Special” skills (when visiting) And finally, which dog will turn on a dime to come back to me when I blow the whistle & can hold a “wait” for 1 minute: 1. Robbie 2. Tabitha 3. Valencia 4. All three 5. None of the three
  10. 10. Valencia Robbie Tabitha Colour Black Black Black Number of paws 4 4 4 Gender Female Male Female Age 12 months 9 months 13 months Can do Fetch, pick up, recall, roll over, sit, wait, etc. Fetch, pick up, recall, roll over, sit, wait, etc. Fetch, pick up, recall, roll over, sit, wait, etc. Favourite toy (when visiting) Purple toy Blue ball Blue ball Soft toy when sleeping Chocolate Moose Who needs a cuddly toy? Panda “Special” skills (when visiting) Settles quickly in cafes Un-phased by loud noises – lorries, construction sites, children playing, fireworks Will turn on a dime to come back to me when I blow the whistle +
  11. 11. So why is the speed at these points so much lower? 1. Bart was riding at the front 2. Strong head winds 3. Mechanical problem 4. Riding uphill 5. Turn 180 ‘
  12. 12. 1 2 3 4 So which lap was the fastest?
  13. 13. So who answered all questions correctly thus far?
  14. 14. What have we learned thus far? • Some data and data points are really useful, while others are not useful to predict • Big and Small Data difficult to understand without contextual knowledge • But can we actually improve learning and teaching based upon what students (and teachers) say they want and what they do?
  15. 15. What do students want? (at least what do they say they want at the end of OU Modules) Step 1: A descriptive analysis was conducted to discount variables that were unsuitable for satisfaction modelling. Step 1 also identified highly correlated predictors and methodically selected the most appropriate. Module Presentation Student Concurrency Study history Overall Satisfaction SEaM UG new, UG continuing, PG new and PG continuing students were modelled separately at Step 2. Step 2: Each subset of variables was modelled in groups. The variables that were statistically significant from each subset were then combined and modelled to identify the final list of key drivers We found at Step 3 that the combined scale provided the simplest and most interpretable solution for PG students and the whole scale for UG students. The solution without the KPI’s included was much easier to use in terms of identifying clear priorities for action. Step 3 Validation: all models have been verified by using subsets of the whole data to ensure the solutions are robust. A variety of model fit statistics have also been used to identify the optimum solutions.
  16. 16. Li, N., Marsh, V., Rienties, B., Whitelock, D. (2017). Online learning experiences of new versus continuing learners: a large scale replication study. Assessment & Evaluation in Higher Education, 42(4), 657-672. Impact factor: 1.243 According to 111,000+ students, what distinguishes excellent from good to not-so- good modules? 1) Good advice from teachers 2) Links well to professional practice 3) Links well to qualifications 4) Quality of teaching materials 5) Quality of tutors
  17. 17. Li, N., Marsh, V., Rienties, B., Whitelock, D. (2017). Online learning experiences of new versus continuing learners: a large scale replication study. Assessment & Evaluation in Higher Education, 42(4), 657-672. Impact factor: 1.243
  18. 18. How does student satisfaction relate to module performance?Satisfaction Students who successfully completed module
  19. 19. Constructivist Learning Design Assessment Learning Design Productive Learning Design Socio-construct. Learning Design VLE Engagement Student Satisfaction Student retention 150+ modules Week 1 Week 2 Week30+ Rienties, B., Toetenel, L., (2016). The impact of learning design on student behaviour, satisfaction and performance: a cross-institutional comparison across 151 modules. Computers in Human Behavior, 60 (2016), 333-341 Nguyen, Q., Rienties, B., Toetenel, L., Ferguson, R., Whitelock, D. (2017). Examining the designs of computer-based assessment and its impact on student engagement, satisfaction, and pass rates. Computers in Human Behavior, 76, November 2017, 703-714. Communication
  20. 20. Should we give students what they want? Or give them advice how to achieve their goals?
  21. 21. Nguyen, Q., Rienties, B., Toetenel, L., Ferguson, R., Whitelock, D. (2017). Examining the designs of computer-based assessment and its impact on student engagement, satisfaction, and pass rates. Computers in Human Behavior, 76, November 2017, 703-714.
  22. 22. 69% of what students are doing on a weekly basis is determined by what and how teachers design their online modules
  23. 23. Click to edit Master title style Excellent group In advance Catching up Nguyen, Q., Huptych, M., Rienties, B. (Accepted as full paper: 21-11-2017). Linking students’ timing of engagement to learning design and academic performance: A longitudinal study. LAK 2018 conference.  Nominated for best paper-award
  24. 24. Click to edit Master title style Passed group In advance Catching up Nguyen, Q., Huptych, M., Rienties, B. (Accepted as full paper: 21-11-2017). Linking students’ timing of engagement to learning design and academic performance: A longitudinal study. LAK 2018 conference.  Nominated for best paper-award
  25. 25. Click to edit Master title style Failed group In advance Catching up Nguyen, Q., Huptych, M., Rienties, B. (Accepted as full paper: 21-11-2017). Linking students’ timing of engagement to learning design and academic performance: A longitudinal study. LAK 2018 conference.  Nominated for best paper-award
  26. 26. So what are the take-home messages? • Not all data that are collected is meaningful: focus on what matters (for learning) • Big Data without context and theoretical base (e.g., Learning Design) has limited use • Listening to students’ feedback is important, but unrelated to actual learning behaviour and academic performance • Linking learning analytics with learning design shows that several students follow prescribed learning patterns, but many also go “off-piste”
  27. 27. You can help • Raising awareness • Giving time (volunteering, puppy parent, foster parent) • Donating money
  28. 28. You can help • Raising awareness • Register to become an organ donor • Talking to your family/friends about your choice
  29. 29. The power of learning analytics to give students (and teachers) what they want! Prof Bart Rienties #OUTalks @DrBartRienties https://pollev.com/bartrienties552

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