Enabling active learning using prediction markets - Patrick Buckley
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Presentation at the HEA-funded workshop 'Using technology-based media to engage and support students in the disciplines of Finance, Accounting and Economics' The workshop presented a variety of innovative approaches, which use technology, to engage and support learning in business disciplines that students find particularly challenging. Delegates had the opportunity to share and evaluate good practice in implementing and developing online teaching resources and to reflect on how to develop their own teaching practice, using technologies available in most institutions. This presentation is part of a related blog post that provides an overview of the event: http://bit.ly/1o1WfHU For further details of the HEA's work on active and experiential learning in the Social Sciences, please see: http://bit.ly/17NwgKX
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Enabling active learning using prediction markets - Patrick Buckley
- 1. Enabling Active Learning Using Prediction Markets Enabling Active Learning Using Prediction Markets Dr. Patrick Buckley
- 2. Enabling Active Learning Using Prediction Markets Introduction Prediction markets are “designed and run for the primary purpose of mining and aggregating information scattered among traders and subsequently using this information in the form of market values in order to make predictions about specific future events” Introduction Pedagogical Application Pedagogical Advantages
- 3. Enabling Active Learning Using Prediction Markets Introduction A Simplified Analogy: Prediction Markets can work like multiple choice questions. So they are • Scalable • Flexible But • We can also ask students to calculate values • They operate in real-time • They allow students to change their mind • They are obviously a group activity Introduction Pedagogical Application Pedagogical Advantages
- 4. Enabling Active Learning Using Prediction Markets Pedagogical Application Our core insight – they can be used to create realistic decision scenarios that link to learning outcomes and allow learners to apply knowledge delivered via traditional educational channels to real-world problems. Example: The National Budget Forecasting Project Learning Outcomes: ‘Consider and assess the impact of the annual tax budget’ ‘Explain the theory underpinning a tax system’ Introduction Pedagogical Application Pedagogical Advantages
- 5. Enabling Active Learning Using Prediction Markets Introduction Pedagogical Application Pedagogical Advantages Pedagogical Application We have pioneered two modes of operations • Repetitive • Continuous We have pioneered across a range of disciplines • Tax (Continuous) • Finance (Repetitive) • Risk Management (Repetitive)
- 6. Enabling Active Learning Using Prediction Markets Pedagogical Advantages Cognitive Domain of Learning • Improved Decision Making • Improved Information Literacy Introduction Pedagogical Application Pedagogical Advantages
- 7. Enabling Active Learning Using Prediction Markets Pedagogical Advantages Cognitive Domain of Learning • Improved Decision Making • Improved Information Literacy Introduction Pedagogical Application Pedagogical Advantages Affective Domain of Learning • Improved Motivation
- 8. Enabling Active Learning Using Prediction Markets Pedagogical Advantages Cognitive Domain of Learning • Improved Decision Making • Improved Information Literacy Introduction Pedagogical Application Pedagogical Advantages Affective Domain of Learning • Improved MotivationAdvantages for Staff • Scalable • Flexible • Real-world, real-time examples
- 9. Enabling Active Learning Using Prediction Markets Publications Cognitive Domain of Learning • Improved Decision Making • Improved Information Literacy Introduction Pedagogical Application Pedagogical Advantages Affective Domain of Learning • Improved MotivationAdvantages for Staff • Scalable • Flexible • Real-world, real-time examples Buckley, P. and Doyle, E., 2012, Enabling Active Learning Using Prediction Markets, EDiNEB 2012, Haarlem, Holland, May 2-5. (Awarded Best Paper) Buckley, P., Garvey, J. & McGrath, F., 2011, A Case Study on Using Prediction Markets as a Rich Environment for Active Learning, Computers and Education, 56(2) Buckley, P. & Garvey, J. 2011 Using Technology to Encourage Critical Thinking and Optimal Decision-Making in Risk Management Education. Risk Management and Insurance Review, Vol. 14, No. 2, pages 299–309, Fall 2011 Garvey, J. & Buckley, P. 2010. Teaching the Concept of Risk: Blended Learning Using a Custom-Built Prediction Market. Journal of Teaching in International Business, 21(4)
- 10. Enabling Active Learning Using Prediction Markets Publications Cognitive Domain of Learning • Improved Decision Making • Improved Information Literacy Introduction Pedagogical Application Pedagogical Advantages Affective Domain of Learning • Improved MotivationAdvantages for Staff • Scalable • Flexible • Real-world, real-time examples Buckley, P. and Doyle, E., 2012, Enabling Active Learning Using Prediction Markets, EDiNEB 2012, Haarlem, Holland, May 2-5. (Awarded Best Paper) Buckley, P., Garvey, J. & McGrath, F., 2011, A Case Study on Using Prediction Markets as a Rich Environment for Active Learning, Computers and Education, 56(2) Buckley, P. & Garvey, J. 2011 Using Technology to Encourage Critical Thinking and Optimal Decision-Making in Risk Management Education. Risk Management and Insurance Review, Vol. 14, No. 2, pages 299–309, Fall 2011 Garvey, J. & Buckley, P. 2010. Teaching the Concept of Risk: Blended Learning Using a Custom-Built Prediction Market. Journal of Teaching in International Business, 21(4)