OER ACHIEVEMENTS, CHALLENGES, AND NEW OPPORTUNITIES
the game with other players, chat with wolf biologists, and share artwork and stories about wolves.
These examples barely scratch the surface in some of the unusual ways these immersive environments provide new opportunities for creating powerful experiential learning environments. But the design of such environments transcends standard pedagogy and theories of learning based on direct transfer. Such environments, especially ones that combine the social with the experiential, can be used to powerfully augment more traditional learning modes and materials.
Immersive environments such as Second Life[1] enable users to create their own avatars and have their avatars participate in a virtual space such as a classroom or amphitheater or replica of some archeological/architectural site under study. This opens up quite a new opportunity for distributed, distance learning by creating a sense of co-presence among the users allowing all kinds of natural interactions among themselves or between themselves and the speaker. It is now even possible to do a simulcast from a physical setting into the virtual setting, allowing a distributed set of students to join a physical class or gathering.
3.2.5 Emerging Deeper Understanding of Human Learning
Many traditional theories of pedagogy have focused on the best ways to transfer knowledge from the teacher to the student. More recent theories have focused on ways to help students internalize that information in a way that makes it both personally meaningful and applicable to new situations. New computer-enhanced learning environments have played a significant role in accelerating this internalization process. For example, in training simulators for complex, real-time decision-making, AI-based automated tutors are used skillfully for after action reviews to get the student to reflect on questionable decisions. In a similar manner, CMU in its OLI have led the way in getting intelligent tutoring systems to watch over the shoulder of students solving homework problems in physics and steer them back on a useful path when they wonder too far astray. Acuitus[2] is now testing an AI-based tutoring system for teaching Navy personnel how to become expert network administrators and troubleshooters. This system includes a structure motivation model for governing the tutor, keeping it from speaking too much or too little, along with a model of tutoring inferred from studying master tutors. The initial results of this system indicate that it outperforms the best human tutors and reportedly reduces time to mastery by 60 percent or more.
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