Avoiding Death by Discussion Board: Asynchronous Online Chats in Aviation History

Universities and colleges are increasingly turning to online course offerings, especially in aviation education. Faculty are increasingly asked to turn their in-person courses into online flavors. Typically, faculty are creating online courses with discussion boards to mimic the scholarly community that exists in a face-to-face classroom. Faculty often create discussion boards, with varying degrees of effectiveness, to provide for the immersion of community. However, the actual effectiveness of discussion boards is debated in recent research. This research examines Asynchronous Online Chats as a replacement for the Death by Discussion Board model. Data from the past two semesters will be examined from an Aviation History course taught at the undergraduate level at the collegiate and university level. Survey instruments used include: Community of Online Learning Patterns of Adaptive Learning Social Achievement Goal

Implementation of Team-Based Learning in Aviation Education

Recent research in the field of Aviation Education and Educational Psychology has shown that students are in need of greater interaction and social skills. Additionally, although Part 141 flight training programs and ground school classes offer many opportunities for collaboration and for dynamic teamwork, often those opportunities are missed as flight training is still largely a one-on-one effort between the student and the certificated flight instructor. Within the last decade, Team-Based Learning has come to prominence in a variety of disciplines across the academic landscape. Team Based-learning incorporates both individual test taking, and group based test taking into one academic environment. Social benefits include development of organic bonds between students and faculty, higher retention rates, and increased patterns of adaptive learning. Team-Based Learning has been implemented in three sections of ground schools at the freshman and sophomore level, and have shown a positive increase in Stage Exam and FAA Written Exam results. Additionally, upcoming research shows how this implementation of Team-Based Learning has created new positive classroom dynamics. This presentation guides fellow aviation educators step-by-step on what Team-Based Learning is, and how to implement Team-Based Learning in their aviation education environments. Major pitfalls, and surprising benefits will be discussed at length

Exploring VR with PilotEdge in a University Part 141 Environment

Flight Simulators have become an integral part of the part 141 training environment. Reliance on flight simulators has only increased in recent years as the pilot shortage has increased the utilization rate on training airplanes. At the same time, demands by flight schools and universities have all but assured that training airplane production has lagged behind the needs of the industry. The ability to simulate an accurate replica of a cockpit of an advanced airplane has long been desired as aviation training is inherently problematic from an educational psychology perspective. Airplanes are loud, cramped, and operationally can be cost prohibitive. All of these factors lead to a desire for more advanced, cheap, and educationally beneficial flight simulators. Early simulators such as the Link Trainer operated pneumatically. As computers became more readily available, the focus centered on software modeling a graphical representation of the yoke. At the aviation education level, full scale mockups of regional and mainline jets are in most serious Part 141 collegiate based flight schools. Today, Virtual Reality (VR) is entering the mainstream vernacular after being a subcomponent of the larger gaming community and is showing potential to disrupt the typical flight simulator model of a traditional full mockup of a cockpit. Many flight simulators such as Microsoft\u27s Flight Simulator X and X-Plane are incorporating Virtual Reality. Additionally, many leading producers of after-market airplane models for the aforementioned flight simulators. In this research, undergraduate students pursuing a Restricted Airline Transport License were each given specified hours of flight training in a flight simulator with Virtual Reality. The students were told to conduct training flights utilizing the PilotEdge software, which offers a high-fidelity simulator of interaction with Air Traffic Control (ATC) personnel within the National Airspace Systems. The purpose of this qualitative and quantitative exploratory research is to gauge interest of student pilots in using VR in flight simulators. A second purpose of this research was to gauge the effectiveness of VR technology in their current state with differing software platforms (ie., Flight Simulator X, X-Plane, etc). A purpose of this presentation is to explore with fellow aviation faculty and research the benefits and drawbacks of particular software from the viewpoint of both the researchers and future aviation professionals

Integrated Organizational Machine Learning for Aviation Flight Data

Increased availability of data and computing power has allowed organizations to apply machine learning techniques to various fleet monitoring activities. Additionally, our ability to acquire aircraft data has increased due to the miniaturization of small form factor computing machines. Aircraft data collection processes contain many data features in the form of multivariate time series (continuous, discrete, categorical, etc.) which can be used to train machine learning models. Yet, three major challenges still face many flight organizations: 1) integration and automation of data collection frameworks, 2) data cleanup and preparation, and 3) developing an embedded machine learning framework. Data cleanup and preparation have been a well-known challenge since database systems were first invented. While integration and automation of data collection efforts within many organizations is quite mature, there are special challenges for flight-based organizations (i.e., the automatic and efficient transmission of aircraft flight data to centralized analytical data processing systems). Furthermore, this creates additional constraints for the operationalization of embedded machine learning methods for classical tasks such as classification and prediction; and magnifying design challenges for the more novel ‘prescriptive-based’ architectures. Our research is focused on the application of a design pattern for a) the integration and automation of data collection and b) an organizationally embedded ensemble machine learning method