This work presents a study that explores the differences between authoring Scenario-Based Training (SBT) simulation content using Augmented Reality (AR) and a Desktop interface. Through an iterative design process two interface conditions were developed and then evaluated qualitatively and quantitatively. Our conceptual model is a graph based visualization that is presented to help designers understand the scenario learning artifacts and relationships. Our major contribution relies on the comparison made between the two authoring tools (AR, Desktop) with the same capabilities. Results show that no significant difference was found in time taken to complete tasks nor on the perceived usability of the systems. However, as expected the Desktop interface was perceived as more efficient. Based on these findings, insights on future directions for building AR immersive authoring tools are provided.

This work presents a comparison of two applications (Augmented Reality (AR) and desktop) to author Scenario-Based Training (SBT) simulations. Through an iterative design process two interface conditions are developed and then evaluated qualitatively and quantitatively. A graph based authoring visualization help designers understand the scenario learning artifacts and relationships. No significant difference was found on time taken to complete tasks nor on the perceived usability of the systems. However, Desktop was perceived as more efficient, corroborated by the significantly higher number of mistakes made in AR. Findings are presented towards building better AR immersive authoring tools.

Accurate tracking of a user in a marker-less environment can be difficult, even more so when agile head or hand movements are expected. When relying on feature detection as part of a SLAM algorithm the issue arises that a large rotational delta causes previously tracked features to become lost. One approach to overcome this problem is with multiple sensors increasing the horizontal field of view. In this paper, we perform a systematic evaluation of tracking accuracy by recording several agile movements and providing different camera configurations to evaluate against. We begin with four sensors in a square configuration and test the resulting output from a chosen SLAM algorithm. We then systematically remove a camera from the feed covering all permutations to determine the level of accuracy and tracking loss. We cover some of the lessons learned in this preliminary experiment and how it may guide researchers in tracking extremely agile movements.

Through virtualization, a Beowulf cluster running on the physical hardware and network of a general-purpose teaching laboratory can provide significant computational power without compromising-in fact increasing-the educational value of the lab machines. After describing our implementation of such a virtual cluster in a teaching lab, we analyze its performance by comparing benchmark results to those of an equivalent dedicated (native) cluster and investigate expedient improvements to our design.

WebMapReduce (WMR) is a strategically simplified user interface for the Hadoop implementation of the map-reduce model for distributed computing on clusters, designed so that novice programmers in an introductory CS courses can perform authentic data-intensive scalable computations using the programming language they are learning in their course. The open-source WMR software currently supports Java, C++, Python, and Scheme computations, and can readily be extended to support additional programming languages, and configured to adapt to the practices at a particular institution for teaching introductory programming. Potential applications in courses at all undergraduate levels are indicated, and implementation of the WMR software is described.