Case Study - Virtual Worlds and Learning Analytics
1. A Case Study inside Virtual Worlds
Using Analytics for immersive spaces
Short Paper submitted at LAK13
Vanessa Camilleri, Sara de Freitas, Matthew Montebello, Paul McDonagh-Smith
2. Overview
• Building the case
• VWs: Immersion &
Engagement
• Using Models for Predictions
• Analytics: Tracing the Steps
• Making Sense out of Data
• Implications
• Conclusion
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3. Introduction: building the case
• Pre-service teachers and technology
• Attitudes & beliefs
• Experience or lack of
• Teacher training programs
• Our problem: Engage through activity
• Our proposal: Immersion through use of
3D spaces
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4. VWs: Immersion & Engagement
• Immersion: Dede & Barab (2009) – more
focused on learner experience, less on
tools
• Immersion: Calleja (2011) – not overly
dependent on fidelity but more on
emotions generated
• Immersion: Dede (2009) & Freedman
(2011) – situated, authentic learning
experiences
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5. VWs: Immersion & Engagement
• Engagement: Csìkszentmihàlyi (1991) –
reaching the optimal ‘flow’ between
challenge and boredom
• Engagement: Portelli & McMahon (2004)
– learner achieves a deeper level of
critical inquiry; learner is autonomous and
responsible for learning
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6. Using Models for Predictions
TAM (Davis, 1993)
• Virtual Worlds – building on the
exploratory framework model (de Freitas &
Oliver, 2006)
• Technology acceptance – building on the
Exploratory Framework
TAM (technology acceptance model) & Oliver,
(de Freitas
2006)
(Davis, 1993)
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7. Analytics: Tracing the steps
• Analytics: Siemens (2010)learner-
produced data to discover information and
social connections
• Our case study: With a degree of
flexibility, and just in time learning
environment, what connections have our
learners established?
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9. Making sense out of data
• Social Connections + Interactions
(analytics)
• Changes in Attitudes (pre/post-test
surveys)
• Reflections (focus groups)
• Assessment (theoretical reflective paper)
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10. Implications
• How do we measure and predict
engagement at a ‘deeper’ level given the
notions of autonomous learning, using an
exploratory learning framework inside an
immersive 3D environment?
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11. Conclusions
• How can we use data to monitor how the
learner progresses inside the 3D space,
and how can the 3D space adapt itself to
the data generated by the learner?
• Finally can we design a predictive model,
that would enable us to determine
changes in real world behavior arising
from the virtual world interactions?
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12. Thank you
Contact: vanessa.camilleri@um.edu.mt
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13. References
Calleja, G. (2011). In-Game: from immersion to incorporation. London, UK: MIT Press.
Csìkszentmihàlyi, M. (1991). Flow: The Psychology of Optimal Experience. New York, USA:
HarperCollins Publisher Inc.
Davis, F. (1993). User Acceptance of Information Technology: system characteristics, user
perceptions and behavioural impacts. International Journal of Man-Machine Studies , 475-487.
de Freitas, S., & Oliver, M. (2006). How can exploratory learning with games and simulations within
the curriculum be most effectively evaluated? Computers & Education (46), 249-264.
Dede, C. (2009). Immersive Interfaces for Engagement and Learning . Science , 323, 66-69.
Dede, C., & Barab, S. (2009). Emerging Technologies for Learning Science: A Time of Rapid
Advances. Journal of Scientific Educational Technology , 18, 301–304.
Freedman, T. (2011). Authentic Learning and ICT. Retrieved June 2011, from ICT in Education:
http://www.ictineducation.org/home-page/2011/6/16/authentic-learning-and-ict.html
Portelli, J., & McMahon, B. (2004). Why Critical-Democratic Engagement? . Journal of Maltese
Education Research , 2 (2), 39-45.
Siemens, G. (2010). What are Learning Analytics? Retrieved 2012, from ELEARNSPACE:
http://www.elearnspace.org/blog/2010/08/25/what-are-learning-analytics/
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