Second Life as an Experiential Learning Opportunity

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Presentation for the Virtual Worlds@Purdue Computers and Writing conference, May 2010

Transcript of Second Life as an Experiential Learning Opportunity

  • 1. Second Life as an Experiential Learning Opportunity
    Computers and Writing 2010:
    Virtual Worlds @ Purdue Conference
    May 21, 2010
    Slides available:

2. Panel Overview
Purdue University Calumet Campus Second Life Initiative Team:
Anastasia Trekles, School of Education
Sherrie Kristin, Library and Information Services
Michael Roller, Computer Graphics Technology
Kim Nankivell, Computer Graphics Technology
Ge Jin, Computer Graphics Technology
Mark Mabrito, English and Philosophy
3. Objectives of EXL Program
Present and describe the intended purpose and application of the construction or activity they have created;
Design and implement an activity or construction that meets the supervising staff or faculty members needs, and accomplishes his/her expected educational outcomes;
Conduct him/herself professionally as a member of a project team;
Communicate routinely with supervising faculty/staff, as well as related Subject Matter Experts where appropriate, on the details and status of the project;
Demonstrate problem-solving and troubleshooting abilities when appropriate.
4. From Concept to Reality
Purchasing the island and restrictions: What cannot be done on an educational platform?
Challenges once purchased: Once the island is paid for, how do we do what we want?
In a budget reduction environment, what is the future for the project?
Our approach to the Experiential Learning credit adaptation: Why we chose the avenues we did
5. Factors and Challenges to Implementation
What other schools are doing
Alternatives (per Kim)
Determining the learners affective state
6. Learning in Virtual Environments and the Student Affective State
Virtual Classroom in Second Life
Training in Virtual Environment
Disaster & Response
Intelligent Tutoring System
Effective human instructors are able to monitor student affective state and tailor their teaching methods to maintain high levels of affect
It is difficult to assess the learners affective state (motivation, interest, engagement level) in virtual learning environment.
7. Sensory Channels Related with Affective State
Visual Channel
Facial Expressions
Auditory Channel
Physiological Channel
Respiration rate
Heart rate
Skin temperature
Electrocardiography (EEG)
Accuracy of affective state detection using facial expressions>70%
8. Affective State Detection from Facial Images
A prototype affective state detection system from single camera image sequences
9. Face Detection
Face Size Estimation Face Triangle
The size of eyes and mouthalso can be estimated from face triangle.
eyes / mouth
Haar feature based face detection
(Jones & Viola 2001)
10. Image Processing Approaches Used

  • Thresholding

11. Histogram Equalization 12. Finding Corners 13. Convex Hull Approximation