Scaled learning requires a novel set of practices on the part of professionals developing and delivering systems of scaled learning. IEEE's Industry Connections Industry Consortium for Learning Engineering (ICICLE) defines learning engineering as "a process and practice that applies the learning sciences, using human-centered engineering design methodologies, and data-informed decision-making to support learners and their development." This event will bring together learning engineering experts and other interested conference participants to further define the discipline and strategies to establish learning engineering at scale. It will also serve as a gathering place for attendees with shared interests in learning engineering to build community around the advancement of learning engineering as a professional practice and academic field of study.

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.

Instruction that adapts to individual learner characteristics is often more effective than instruction that treats all learners as the same. A practical approach to making MOOCs adapt to learners may be by integrating frameworks for intelligent tutoring systems (ITSs). Using the Learning Tools Interoperability standard (LTI), we integrated two intelligent tutoring frameworks (GIFT and CTAT) into edX. We describe our initial explorations of four adaptive instructional patterns in the PennX MOOC "Big Data and Education." The work illustrates one route to adaptivity at scale.

This paper describes methods for enhancing the experience application program interface (xAPI) to improve the assessment of domain competency modeling for adaptive instruction. xAPI is an e-learning software specification which allows individual learning experiences and achievements to be amassed in a Learning Record Store (LRS). Adaptive instruction includes tailored training or educational experiences usually delivered and guided by Intelligent Tutoring Systems (ITSs). ITSs can more effectively tailor or adapt instruction when they have more accurate models of the learner's prior knowledge or competency. This paper examines the potential effect of methods to more accurately model learner experiences and domain competency in an LRS. Specifically, we recommend five methods to improve xAPI statements by documenting: 1) achievement types; 2) experience duration; 3) experience source information; 4) domain learning and forgetting; and 5) assessment within learning experiences.

The detection and analysis of students’ domain-specific and metacognitive strategy use in Open-Ended Learning Environments (OELE) is a necessary step to support their learning and problem solving through contextualized scaffolding. We present an analysis of students’ performance from information captured in log files in UrbanSim, a turn-based simulation environment for counterinsurgency training. We illustrate the benefits of this approach within a task-model framework. Our overall goals are to implement a generalizable detection and adaptive scaffolding framework in an extended version of the GIFT tutoring system developed at ARL.

Scenario-based training exemplifies the learning-by-doing approach to human performance improvement. In this paper, we enumerate the advantages of incorporating automated scenario generation technologies into the traditional scenario development pipeline. An automated scenario generator is a system that creates training scenarios from scratch, augmenting human authoring to rapidly develop new scenarios, providing a richer diversity of tailored training opportunities, and delivering training scenarios on demand. We introduce a combinatorial optimization approach to scenario generation to deliver the requisite diversity and quality of scenarios while tailoring the scenarios to a particular learner's needs and abilities. We propose a set of evaluation metrics appropriate to scenario generation technologies and present preliminary evidence for the suitability of our approach compared to other scenario generation approaches.