Meredith Taylor

A Comparison of learning gains when using a 2D simulation tool versus a 3D virtual world

The use of three-dimensional Virtual Worlds in the classroom offers the promise of engaging and authentic learning experiences. Given the effort and investment involved in the creation of these worlds, more studies are needed which measure the learning gains. In particular, greater understanding is needed concerning what aspects of the technology are beneficial for learning what sort of content and in what context. We conducted two experimental studies involving 129 biology students over a two-year period that sought to make a contribution towards this understanding. Our study compared the knowledge of students after a traditional classroom lecture about a biological theorem known as Marginal Value Theorem, with their knowledge after they were exposed to simulations of two-dimensional models developed in NetLogo and three-dimensional models developed in Unity3D. Perhaps due to cognitive overload and distractors in the virtual world, it appears that the two-dimensional NetLogo model delivered better learning outcomes. Cost-benefit analysis of 3D virtual worlds over alternative learning methods and representations is scarce.In two separate studies, 129 biology students explored an animal behaviour theory in six scenarios represented in 2D and 3D.Posttests measured learning gains compared to a pretest following traditional classroom presentation of the theory.2D simulations were modelled in NetLogo and 3D simulations used Unity3D.Perhaps due to cognitive overload and distraction-conflict theory, 2D Netlogo delivered better learning outcomes.

Designing in virtual reality (DesIRe): a gesture-based interface

In this paper, our purpose is to focus on detection of dynamic gestures and find the thresholds to detect gestures for a gesture-based system, DesIRe (DESigning In virtual REality). DesIRe treats virtual reality as a platform of virtual clay and allows realtime dynamic interaction with an object to perform a number of design operations. We developed an interface using a stereoscopic display environment and combining elements of gesture recognition and motion capture. The system architecture of DesIRe include following components: Vizard Virtual Reality Toolkit, an immersive projection system (VISOR), an optical tracking system (specifically the PPT system and the in-house gesture recognition software), and Data Input System (a data glove). In DesIRe, the user has control of a hand-shaped cursor which is guided by a single infrared LED and linked to the data glove finger states and orientation in order to provide an immediate, visual feedback. In this paper, we present an approach to represent the finger states using binary digits in gesture detection and demonstrate the thresholds. We found that thresholds are glove specific.

Expertise Recommendation: A Two-Way Knowledge Communication Channel

In our service-based society, expertise is becoming increasingly recognized as a valuable and scarce resource. Thus, the demand for an integrated expert finder that understands whether it has found a current, relevant and credible expert will grow. We offer a three pronged approach which combines data mining, self reporting and referral by others together with a number of strategies which provide feedback and validation of the data gathered via data mining and self-reporting. The feedback mechanism offers a two-way communication channel between the service requester and the service provider, the expert.

Discovering Areas of Expertise from Publication Data

Expertise recommender systems are a valuable tool for keeping track of who has expertise and in what areas within an organization. The key problem is acquiring validated knowledge of expertise and keeping that information up to date. In research organizations, publications are one source of evidence of expertise which can be used to identify who knows about what. In this paper we focus on evaluating the feasibility of a simple technique for uncovering expertise used as the foundation and starting point of maintaining a profile of validated expertise within an organization.

Incremental learning via exceptions for agents and humans: evaluating KR comprehensibility and usability

Acquiring knowledge directly from the domain expert requires a knowledge representation and specification method that is comprehensible and feasible for the holder and creator of that knowledge. The technique, known as multiple classification ripple down rules (MCRDR), is novelly applied to the problem of building and maintaining a library of training scenarios for use by customs and immigration officer trainees in our agent-based virtual environment which may be indexed for retrieval based on the rules associated with them. Our evaluation study aims to demonstrate the utility of the MCRDR combined case and exception structure rule-based approach over standard rules alone and a non-case-based approach.