The learning landscape is changing. Learning occurs throughout the day, utilizing new content areas and different methods for distribution. The purpose of this chapter is to highlight new directions in adult learning, differentiating it from more traditional forms of learning. Toward this aim, we focus on the what (i.e., content), how (i.e., delivery mechanisms), and why (i.e., issues driving change) of new directions in adult learning. We discuss recent trends in learning content, highlighting what areas are important for adults to know in today’s dynamic work environment. Then we review new directions in learning methodology, looking at how individuals access content (i.e., key evolving formal and informal delivery mechanisms). A discussion of underlying factors driving these workforce changes is then presented, specifying the issue of why there is a need for new directions. We end with points for consideration, drawing up years of training and education literature.

Abstract. Usability evaluation has received extensive attention in both academic and applied arenas. Despite this, there have been few formal attempts to integrate past research and best practices in an effort to develop a newly updated and adaptable approach. This poster provides an overview of the types of results yielded by a novel usability assessment approach (i.e., Experienced-based Questionnaire for Usability Assessments Targeting Elaborations [EQUATE]) when applied to a post mission reporting tool. The goal of this study was to develop software to automate performance tracking for anti-submarine aircraft, digitize performance and training information, and automate the display of post mission summaries. Although some of these technologies exist, the prototype tested during this research was the first, of which the authors are aware, to provide a single point of access for data entry, analysis and reporting. Due to the potential benefits across a variety of naval aviation platforms, the program's usability goals focused on identifying means to optimize the tool by gathering novice user feedback. Traditional methods for end-user feedback have tended to focus on user performance and satisfaction, rather than providing prescriptive inputs to identifying and rectifying issues. The results of this study provided usability input for post mission reporting, as well as identified and narrowed the heuristic dimensions used for final validation.

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During the content validation phase of the development of a hybrid heuristic/survey-based usability assessment technique, the authors of this paper conducted a card sort with Subject Matter Experts (SMEs) in Human Factors Psychology. The information provided would inform the initial usability dimensions (e.g., Help, Learnability, Graphic Design & Aesthetics) used in the resulting technique. While Hierarchical Cluster Analysis (HCA) is the standard approach to data analysis with card sorts, several factors (e.g., size of data set, application of system, nature of construct measured) led the research team to consider alternatives. As a result, data derived from this card sort were analyzed in two ways: 1) using Confirmatory Factor Analysis (CFA) and, 2) Using (HCA). The use of the two methods provided an opportunity for researchers to compare their quality and utility in informing dimensions used during the construct validation phase. While HCA was more detailed and provided a better visual description as to how individual items were related to others and broad dimensions, the results were far from parsimonious, making it difficult to determine the meaning of broad dimensions and exactly where those dimensions deviated from others. In contrast, CFA resulted in clear and distinct dimensions with individual items loading on no more than two dimensions (e.g., one primary and one secondary). With that said, the CFA resulted in the rejection of more items as many did not load on the extant dimensions. The general conclusions of this psychometric methodological comparison, for the validation of a Usability evaluation method, is that while HCA is an excellent method for understanding the intercorrelations and intricacies of a construct (i.e., Usability), it may not be the best suited approach for developing a usability method intended to provide systematic feedback to system developers. If the goal is the development of a usability assessment technique, researchers and practitioners alike can use CFA to analyze their card sort data and inform the construct validation phase of their measure.

The cost to train a basic qualified U.S. Navy fighter aircraft pilot is nearly USD 10 M. The training includes primary, intermediate, and advanced stages, with the advanced stage involving extensive flight training, and, thus, is very expensive as a result. Despite the screening tests in place and early-stage attrition, 4.5% of aviators undergo attrition in this most expensive stage. Key reasons for aviator attrition include poor flight performance, voluntary withdrawals, and medical reasons. The reduction in late-stage attrition offers several financial and operational benefits to the U.S. Navy. To that end, this research leverages feature extraction and machine learning techniques on the very sparse flight test grades of student aviators to identify those with a high risk of attrition early in training. Using about 10 years of historical U.S. Navy pilot training data, trained models accurately predicted 50% of attrition with a 4% false positive rate. Such models could help the U.S. Navy save nearly USD 20 M a year in attrition costs. In addition, machine learning models were trained to recommend a suitable training aircraft type for each student aviator. These capabilities could help better answer the need for pilots and reduce the time and cost to train them.