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This paper examines approaches to improve training technology design with the goal of enhancing transfer of training, the effect that knowledge and skills acquired during training have on problem solving or knowledge acquisition in work or operational environments. Specifically, we address the design of adaptive instructional systems (AISs), a category of learning technology that includes intelligent tutoring systems (ITSs), intelligent mentors and other recommender systems that tailor instruction to more efficiently meet the needs of individual learners and teams. Since tailored training requires more content and instructional decisions and therefore more authoring time than non-tailored computer-based training, we also examine how AIS design and transfer of training might be influenced by AIS authoring processes and interfaces. Design challenges are also discussed and include factors such as the ease of authoring that influence transfer of training.

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.

Realistic augmented reality systems require both accurate localization of the user and a mapping of the environment. In a markerless environment this is often done with SLAM algorithms which, for localization, pick out features in the environment and compare how they have changed from keyframe to current frame. However, human head agility, such as seen in video gaming tasks or training exercises, poses a problem; fast rotations will cause all previously tracked features to no longer be within the field of view and the system will struggle to localize accurately. In this paper we present an approach that is capable of tracking a human head's agile movements by using an array of RGB-D sensors and a reconstruction of this sensor data into 360 degrees of features that is fed into our SLAM algorithm. We run an experiment with pre-recorded agile movement scenarios that demonstrate the accuracy of our system. We also compare our approach against single sensor algorithms and show a significant improvement (up to 15 to 20 times better accuracy) in localization. The development of our sensor array and SLAM algorithm creates a novel approach to accurately localize extremely agile human head movements.