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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
Overview
• Building the case
• VWs: Immersion &
  Engagement
• Using Models for Predictions
• Analytics: Tracing the Steps
• Making Sense out of Data
• Implications
• Conclusion
10/04/2013          LAK13        2
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
10/04/2013           LAK13                   3
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
10/04/2013         LAK13                   4
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


10/04/2013           LAK13                      5
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)


10/04/2013              LAK13                          6
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?


10/04/2013          LAK13                     7
Analytics: Tracing the steps




10/04/2013        LAK13              8
Making sense out of data

• Social Connections + Interactions
  (analytics)
• Changes in Attitudes (pre/post-test
  surveys)
• Reflections (focus groups)
• Assessment (theoretical reflective paper)


10/04/2013          LAK13                     9
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?




10/04/2013          LAK13                    10
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?
10/04/2013          LAK13                     11
Thank you
             Contact: vanessa.camilleri@um.edu.mt




10/04/2013      LAK13                               12
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/



10/04/2013                                     LAK13                                              13

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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 10/04/2013 LAK13 2
  • 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 10/04/2013 LAK13 3
  • 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 10/04/2013 LAK13 4
  • 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 10/04/2013 LAK13 5
  • 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) 10/04/2013 LAK13 6
  • 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? 10/04/2013 LAK13 7
  • 8. Analytics: Tracing the steps 10/04/2013 LAK13 8
  • 9. Making sense out of data • Social Connections + Interactions (analytics) • Changes in Attitudes (pre/post-test surveys) • Reflections (focus groups) • Assessment (theoretical reflective paper) 10/04/2013 LAK13 9
  • 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? 10/04/2013 LAK13 10
  • 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? 10/04/2013 LAK13 11
  • 12. Thank you Contact: vanessa.camilleri@um.edu.mt 10/04/2013 LAK13 12
  • 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/ 10/04/2013 LAK13 13