«Learning Analytics at the Open University and the UK»
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In this seminar, Prof Bart Rienties will reflect on how the Open University UK has become a leading institution in implementing learning analytics at scale amongst its 170K students and 5K staff. Furthermore, he will discuss how learning analytics is being adopted at other UK institutions, and what the implications for higher education might be. eMadrid seminar on «Review and challenges in Learning Analytics»
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«Learning Analytics at the Open University and the UK»
- 1. @DrBartRienties Professor of Learning Analytics Learning analytics at the Open University and the UK Online presentation 08 May 2020 eMadrid seminar on «Review and challenges in Learning Analytics»
- 2. Adeniji, B. (2019). A Bibliometric Study on Learning Analytics. Long Island University. Retrieved from https://digitalcommons.liu.edu/post_fultext_dis/16/
- 4. “In the UK the Open University (OU) is a world leader in the collection, intelligent analysis and use of large scale student analytics. It provides academic staff with systematic and high quality actionable analytics for student, academic and institutional benefit (Rienties, Nguyen, Holmes, Reedy, 2017). Rienties and Toetenel’s, 2016 study (Rienties & Toetenel, 2016) identifies the importance of the linkage between LA outcomes, student satisfaction, retention and module learning design. These analytics are often provided through dashboards tailored for each of academics and students (Schwendimann et al., 2017). The OU’s world-class Analytics4Action initiative (Rienties, Boroowa, Cross, Farrington-Flint et al., 2016) supports the university-wide approach to LA. In particular, the initiative provided valuable insights into the identification of students and modules where interventions would be beneficial, analysing over 90 large-scale modules over a two-year period… The deployment of LA establishes the need and opportunity for student and module interventions (Clow, 2012). The study concludes that the faster the feedback loop to students, the more effective the outcomes. This is often an iterative process allowing institutions to understand and address systematic issues. Legal, ethical and moral considerations in the deployment of LA and interventions are key challenges to institutions. They include informed consent, transparency to students, the right to challenge the accuracy of data and resulting analyses and prior consent to intervention processes and their execution (Slade & Tait, 2019)” Wakelam, E., Jefferies, A., Davey, N., & Sun, Y. (2020). The potential for student performance prediction in small cohorts with minimal available attributes. British Journal of Educational Technology, 51(2), 347-370. doi: 10.1111/bjet.12836
- 5. 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. DOI: 10.1016/j.chb.2017.03.028. 69% of what students are doing in a week is determined by us, teachers!
- 6. 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. DOI: 10.1016/j.chb.2017.03.028. Communication
- 7. Start FSF RFSOFS ORFN O SRFROF OR ORSORFS OS RS FSF RFSOFS ORFN O SRFROF OR ORSORFS OS RS FSF RFSOFS ORFN O SRFROF OR ORSORFS OS RS FSF RFSOFS ORFN O SRFROF OR ORSORFS OS RS FSF RFSOFS ORFN O SRFROF OR ORSORFS OS RS Pass Fail No submit TMA-1time VLE opens Start Activity space FSF RFSOFS ORFN O SRFROF OR ORSORFS OS RS
- 8. FSF RFSOFS ORFN O SRFROF OR ORSORFS OS RS Start FSF RFSOFS ORFN O SRFROF OR ORSORFS OS RS FSF RFSOFS ORFN O SRFROF OR ORSORFS OS RS FSF RFSOFS ORFN O SRFROF OR ORSORFS OS RS FSF RFSOFS ORFN O SRFROF OR ORSORFS OS RS Pass Fail No submit TMA-1time VLE opens Start VLE trail: successful student FSF RFSOFS ORFN O SRFROF OR ORSORFS OS RS
- 9. FSF RFSOFS ORFN O SRFROF OR ORSORFS OS RS Start FSF RFSOFS ORFN O SRFROF OR ORSORFS OS RS FSF RFSOFS ORFN O SRFROF OR ORSORFS OS RS FSF RFSOFS ORFN O SRFROF OR ORSORFS OS RS FSF RFSOFS ORFN O SRFROF OR ORSORFS OS RS FSF RFSOFS ORFN O SRFROF OR ORSORFS OS RS Pass Fail No submit TMA-1time VLE opens Start VLE trail: student who did not submit
- 10. Probabilistic model: all students time TMA1 VLE start
- 11. OU Analyse demo http://analyse.kmi.open.ac.uk
- 12. Herodotou, C., Rienties, B., Hlosta, M., Boroowa, A., Mangafa, C., Zdrahal, Z., (2020). Scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study. Internet and Higher Education, 45, 100725.
- 13. Herodotou, C., Rienties, B., Hlosta, M., Boroowa, A., Mangafa, C., Zdrahal, Z., (2020). Scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study. Internet and Higher Education, 45, 100725. • Amongst the factors shown to be critical to the scalable PLA implementation were: Faculty's engagement with OUA, teachers as “champions”, evidence generation and dissemination, digital literacy, and conceptions about teaching online.
- 15. STUDENT SUCCESS ANALYTICS 15 O R G AN I S AT I O N AL C APAB I LT I E S Productionised output and MI Strategic analysis Modelling / AI Data collection Data storage and access Technology architecture Learning design and delivery Student lifecycle managemen t Continuous improvemen t and innovation Creation of actionable insight Availability of data Impact the student experience Adapted from Barton and Court (2012) - https://hbr.org/2012/10/making-advanced-analytics-work-for-you
- 16. Design Inquiry First Module 2nd Module NthModule QualificationLife long learning Design Design Rienties, B., Olney, T., Nichols, M., Herodotou, C. (2019). Effective usage of Learning Analytics: What do practitioners want and where should distance learning institutions be going? Open Learning. DOI: 10.1080/02680513.2019.1690441
- 17. Design Inquiry First Module 2nd Module NthModule QualificationLife long learning Communication Integrated Design Personalisation Evidence based Integrated learning analytics solution Alumni Rienties, B., Olney, T., Nichols, M., Herodotou, C. (2019). Effective usage of Learning Analytics: What do practitioners want and where should distance learning institutions be going? Open Learning. DOI: 10.1080/02680513.2019.1690441
- 18. 18 Foster, E. (2017) Nottingham Trent University. UCISA event A-Z of learning analytics 28/06/2017.
- 19. 19 de Quincey, E., Briggs, C., Kyriacou, T., & Waller, R. (2019). Student Centred Design of a Learning Analytics System. Paper presented at the Proceedings of the 9th International Conference of Learning Analytics Knowledge, Arizona.
- 20. Conclusions I 1. A lot of data is coming into (and out of) education: 2. A lot of “semi-standardised” data is gathered within and across institutions 3. Great opportunities to harvest fine- grained and longitudinal data
- 21. Conclusions II 1. What about the ethics? 2. What can be standardised (and what not)? 3. Are we optimising the record player?
- 22. @DrBartRienties Professor of Learning Analytics Learning analytics at the Open University and the UK
- 23. The power of learning analytics to visualise evidence of learning T: drBartRienties E: bart.rienties@open.ac.uk W: www.bartrienties.nl W: https://www.organdonation.nhs.uk/ W: https://www.sportentransplantatie.nl/
Notas do Editor
- Cluster analysis of 40 modules (>19k students) indicate that module teams design four different types of modules: constructivist, assessment driven, balanced, or socio-constructivist. The LAK paper by Rienties and colleagues indicates that VLE engagement is higher in modules with socio-constructivist or balanced variety learning designs, and lower for constructivist designs. In terms of learning outcomes, students rate constructivist modules higher, and socio-constructivist modules lower. However, in terms of student retention (% of students passed) constructivist modules have lower retention, while socio-constructivist have higher. Thus, learning design strongly influences behaviour, experience and performance. (and we believe we are the first to have mapped this with such a large cohort).
- Case-based reasoning (reasoning from precedents, k-Nearest Neighbours) Based on demographic data Based on VLE activities Classification and Regression Trees (CART) Bayes networks (naïve and full) Final verdict decided by voting
- ‘Creating better opportunities with and for learners, enabling more to achieve their goals’ Creating– includes our content, people, systems and support services Better – means continuous improvement, always striving to be better than we were yesterday Opportunities – different learning options to meet different needs With and For Learners– all learners from all backgrounds etc. which recognises their diversity and emphasising partnership Enabling – making it easy to engage with us and our products More – again, continuous improvement, striving to attract more students To achieve their goals – which may be a new job or career, a qualification, an accreditation or learning for pleasure.