Problem-based learning supported by semantic techniques

Problem-based learning has been applied over the last three decades to a diverse range of learning environments. In this educational approach, different problems are posed to the learners so that they can develop different solutions while learning about the problem domain. When applied to conceptual modelling, and particularly to Qualitative Reasoning, the solutions to problems are models that represent the behaviour of a dynamic system. The learner?s task then is to bridge the gap between their initial model, as their first attempt to represent the system, and the target models that provide solutions to that problem. We propose the use of semantic technologies and resources to help in bridging that gap by providing links to terminology and formal definitions, and matching techniques to allow learners to benefit from existing models

A Herpesvirus Encoded Deubiquitinase Is a Novel Neuroinvasive Determinant

The neuroinvasive property of several alpha-herpesviruses underlies an uncommon infectious process that includes the establishment of life-long latent infections in sensory neurons of the peripheral nervous system. Several herpesvirus proteins are required for replication and dissemination within the nervous system, indicating that exploiting the nervous system as a niche for productive infection requires a specialized set of functions encoded by the virus. Whether initial entry into the nervous system from peripheral tissues also requires specialized viral functions is not known. Here we show that a conserved deubiquitinase domain embedded within a pseudorabies virus structural protein, pUL36, is essential for initial neural invasion, but is subsequently dispensable for transmission within and between neurons of the mammalian nervous system. These findings indicate that the deubiquitinase contributes to neurovirulence by participating in a previously unrecognized initial step in neuroinvasion

Intelligent User Interface Design for Teachable Agent Systems

Betty's Brain [1] is a learning-by-teaching environment where students "teach" Betty by constructing a concept map that models relations between domain concepts. The relations can be causal, hierarchical, and property links between the entities that represent the domain. The goal is for students to understand and then teach Betty about interdependence and balance among entities in a river ecosystem. As a part of the teaching process, students can query and quiz Betty to assess her understanding based on what she has been taught

ABSTRACT Intelligent User Interface Design for Teachable Agent Systems

This paper describes the interface components for a system called Betty’s Brain, an intelligent agent we have developed for studying the learning by teaching paradigm. Our previous studies have shown that students gain better understanding of domain knowledge when they prepare to teach others versus when they prepare to take an exam. This finding has motivated us to develop computer agents that students teach using concept map representations with a visual interface. Betty is intelligent not because she learns on her own, but because she can apply qualitative-reasoning techniques to answer questions that are directly related to what she has been taught through the concept map. We evaluate the agent’s interfaces in terms of how well they support learning activities, using examples of their use by fifth grade students in an extensive study that we performed in a Nashville public school. A critical analysis of the outcome of our studies has led us to propose the next generation interfaces in a multi-agent paradigm that should be more effective in promoting constructivist learning and self-regulation in the learning by teaching framework