Predictive Learning Analytics Professor Bart Rienties (Open University)

Predictive Learning Analytics Professor Bart Rienties (Open University)

• 1.
  Predictive Learning Analytics #HUCBMS2018 Headsof University Centres of Biomedical Sciences 25th anniversary conference If you want to vote and share, log into: https://pollev.com/bartrienties552 @DrBartRienties Professor of Learning Analytics
• 2.
  A special thanksto 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, Saman Rizvi, Jekaterina Rogaten, Matt Schencks, Mike Sharples, Dirk Tempelaar, Belinda Tynan, Lisette Toetenel, Thomas Ullmann, Denise Whitelock, Zdenek Zdrahal, and others…
• 3.
  Dyckhoff, A. L.,Zielke, D., Bültmann, M., Chatti, M. A., & Schroeder, U. (2012). Design and Implementation of a Learning Analytics Toolkit for Teachers. Journal of Educational Technology & Society, 15(3), 58-76.
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  Big Data ismessy!!!
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  Prof Paul Kirschner(OU NL) “Learning analytics: Utopia or dystopia”, LAK 2016 conference
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  Learning Design isdescribed as “a methodology for enabling teachers/designers to make more informed decisions in how they go about designing learning activities and interventions, which is pedagogically informed and makes effective use of appropriate resources and technologies” (Conole, 2012).
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  Assimilative Finding and handling information CommunicationProductive Experiential Interactive/ Adaptive Assessment Type of activity Attending to information Searching for and processing information Discussing module related content with at least one other person (student or tutor) Actively constructing an artefact Applying learning in a real-world setting Applying learning in a simulated setting All forms of assessment, whether continuous, end of module, or formative (assessment for learning) Examples of activity Read, Watch, Listen, Think about, Access, Observe, Review, Study List, Analyse, Collate, Plot, Find, Discover, Access, Use, Gather, Order, Classify, Select, Assess, Manipulate Communicate, Debate, Discuss, Argue, Share, Report, Collaborate, Present, Describe, Question Create, Build, Make, Design, Construct, Contribute, Complete, Produce, Write, Draw, Refine, Compose, Synthesise, Remix Practice, Apply, Mimic, Experience, Explore, Investigate, Perform, Engage Explore, Experiment, Trial, Improve, Model, Simulate Write, Present, Report, Demonstrate, Critique Conole, G. (2012). Designing for Learning in an Open World. Dordrecht: Springer. 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 Open University Learning Design Initiative (OULDI)
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  Merging big datasets • Learning design data (>300 modules mapped) • VLE data • >140 modules aggregated individual data weekly • >37 modules individual fine-grained data daily • Student feedback data (>140) • Academic Performance (>140) • Predictive analytics data (>40) • Data sets merged and cleaned • 111,256 students undertook these modules
• 10.
  Toetenel, L., Rienties,B. (2016). Analysing 157 Learning Designs using Learning Analytic approaches as a means to evaluate the impact of pedagogical decision-making. British Journal of Educational Technology, 47(5), 981–992.
• 12.
  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!
• 13.
  Constructivist Learning Design Assessment Learning Design Productive LearningDesign 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
• 14.
  So what happenswhen you give learning design visualisations to teachers? Toetenel, L., Rienties, B. (2016) Learning Design – creative design to visualise learning activities. Open Learning: The Journal of Open and Distance Learning, 31(3), 233-244.
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  Toetenel, L., Rienties,B. (2016) Learning Design – creative design to visualise learning activities. Open Learning: The Journal of Open and Distance Learning, 31(3), 233-244.
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  So what happenswhen you give learning analytics data about students to teachers? 1. How did 240 teachers within the 10 modules made use of PLA data (OUA predictions) and visualisations to help students at risk? 2. To what extent was there a positive impact on students' performance and retention when using OUA predictions? 3. Which factors explain teachers' uses of OUA?
• 17.
  Usage of OUAdashboard by participating teachers 1 Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z., Hlosta, M., & Naydenova, G. (2017). Implementing predictive learning analytics on a large scale: the teacher's perspective. Paper presented at the Proceedings of the Seventh International Learning Analytics & Knowledge Conference, Vancouver, British
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  18 Which factors betterpredict pass and completion rates? Regression analysis Student characteristics Age Gender New/c ontinu ous Disability Ethnicity Educat ion IMD band Best previous score Sum of previous credits Teacher characteristics Module presentations per teacher Students per module presentation OUA usage module design Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z., Hlosta, M. (Submitted: 01-08-2017). Using Predictive Learning Analytics to Support Just-in-time Interventions: The Teachers' Perspective Across a Large-scale Implementation.
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  19 Significant model (pass:χ2= 76.391, p < .001, df = 24). Logistic regression results (pass rates) ●Nagelkerke’s R2 = .185 (model explains 18% of the variance in passing rates) ● Correctly classified over 70% of the cases (prediction success overall was 70.2%: 33.5 % for not passing a module and 88.7% for passing a module). ●Significant predictors of both pass and completion rates: ●OUA usage (p=.006) ●Best previous module score achieved (p=.005) ● All other predictors were not significant. Best predictors of pass rates OUA usage Best previous score Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z., Hlosta, M. (Submitted: 01-08-2017). Using Predictive Learning Analytics to Support Just-in-time Interventions: The Teachers' Perspective Across a Large-scale Implementation.
• 20.
  Conclusions and movingforwards 1. Teachers and professional development key in world of learning analytics 2. Learning design and teachers strongly influences student engagement, satisfaction and performance 3. Learning analytics can be quite powerful to understand complexities of learning in- and outside class
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  Conclusions and movingforwards 1. Learning analytics approaches can help researchers and practitioners to test and validate big and small theoretical questions 2. I am open for any collaborations or any wild ideas 
• 22.
  Predictive Learning Analytics HUCBMS2018 Heads of University Centres of Biomedical Sciences 25th anniversary conference If you want to vote and share, log into: https://pollev.com/bartrienties552 @DrBartRienties Professor of Learning Analytics