Michael Carbonaro

Merging social networking environments and formal learning environments to support and facilitate interprofessional instruction.

This study describes the redesign of an interprofessional team development course for health science students. A theoretical model is hypothesized as a framework for the redesign process, consisting of two themes: 1) the increasing trend among post-secondary students to participate in social networking (e.g., Facebook, Second Life) and 2) the need for healthcare educators to provide interprofessional training that results in effective communities of practice and better patient care. The redesign focused on increasing the relevance of the course through the integration of custom-designed technology to facilitate social networking during their interprofessional education. Results suggest that students in an educationally structured social networking environment can be guided to join learning communities quickly and access course materials. More research and implementation work is required to effectively develop interprofessional health sciences communities in a combined face-to-face and on-line social networking context.

Healthcare education with virtual-world simulations

Becoming a skilled professional requires the acquisition of theoretical knowledge and the practice of skills under the guidance of an expert. The idea of learning-through-apprenticeship is long accepted in medicine and, more generally, in the health sciences, where practicum courses are an essential part of most curricula. Because of the high cost of apprenticeship programs -- mentors can usually supervise few trainees and trainees may need long apprenticeship periods - simulation has long been adopted as a learning-by-doing training method that can supplement apprenticeship in many professional and engineering programs, including the health sciences. In this paper, we describe our experience developing virtual world-based training systems for two healthcare contexts. In one, procedural training was emphasized, while the other focused on teaching communication skills. In each case, we developed a custom set of tools to meet the needs of that context. We present an analysis of the case studies, and lessons drawn from this analysis.

Using Extra Output Learning to Insert a Symbolic Theory into a Connectionist Network

This paper examines whether a classical model could be translated into a PDP network using a standard connectionist training technique called extra output learning. In Study 1, standard machine learning techniques were used to create a decision tree that could be used to classify 8124 different mushrooms as being edible or poisonous on the basis of 21 different Features (Schlimmer, 1987). In Study 2, extra output learning was used to insert this decision tree into a PDP network being trained on the identical problem. An interpretation of the trained network revealed a perfect mapping from its internal structure to the decision tree, representing a precise translation of the classical theory to the connectionist model. In Study 3, a second network was trained on the mushroom problem without using extra output learning. An interpretation of this second network revealed a different algorithm for solving the mushroom problem, demonstrating that the Study 2 network was indeed a proper theory translation.

fAR-PLAY: A framework to develop Augmented/Alternate Reality Games

Augmented/Alternate Reality Games are a relatively new, yet increasingly interesting, class of social applications. These games are fundamentally intertwined with the players real life, in that the players everyday activities generate events that advance the game logic. At the same time, they augment the players “regular” activities with game-related tasks and challenges and with rich digital content that is communicated to the players through a variety of devices, usually augmenting their mobile-device views. In our work, we have been developing a software framework, fAR-Play, to support the development of educational AARG games, in the treasure-hunt style, using space as the context for communicating information. Our toolkit extends the stereotypical notion of AARGs to include a virtual world in which the players can also interact and play. Our toolkit has been evaluated through (and has evolved based on our experience with) the development of several specific games.

Making a Connection between Computational Modeling and Educational Research

Bruner, Goodnow, and Austins (1956) research on concept development is re-examined from a connectionist perspective. A neural network was constructed which associates positive and negative instances of a concept with their corresponding attribute values. Two methods were used to help preserve the ecological validity of the input: 1) closely mapping the input to the actual visual stimuli; and 2) structuring the output layer based on Gagnes (1962, 1985) work on human concept learning. This resulted in the addition of output units referred to as attribute context constraints. These units required the network to demonstrate the identification of attributes both relevant and irrelevant to the task of classification. Results suggest that the simultaneous learning of attributes guided the network in constructing a faster and more generalizable representation than when attribute constraints were absent. Results are discussed with respect to the advantages of computational approaches to studying learning.