AN APPLICATION OF COGNITIVE LOAD THEORY: ASSESSMENT OF STUDENT PILOT PERFORMANCE
Pilot training and certification have largely remained the same since the Practical Test Standards (PTS) were issued more than twenty years ago by the Federal Aviation Administration (FAA). Within the last several years, the general aviation training sector has acquired the capability to collect and analyze digital data from certain training aircraft. With the implementation of digital information analysis, a more accurate picture of the capabilities of student pilots is possible. These advancements could be used by flight instructors in the assessment process of flight students. With the inclusion of digital data from the aircraft, the cognitive load necessary to make an accurate assessment of a student’s performance could be affected, ideally in a positive manner. Cognitive load researchers typically focus on three aspects to enhance the likelihood of success in learning or task completion. There are three techniques to reduce cognitive load: (a) reduce extraneous load, (b) manage intrinsic load, and (c) optimize germane load (Young, Cate, O’Sullivan, & Irby, 2016). The current research project focused on the impact to the cognitive load of flight instructors who were presented with digital information retrieved from an airplane during their assessment of a student pilot’s aircraft landing competence, endorsement readiness for initial solo, the willingness of the instructor to mentor the student, and how well they liked the student pilot. The study found that a digital condition, when presented alone, created extraneous cognitive load and did not enable flight instructors to accurately rate student landing performance. Additionally, flight instructors were not able to use a combined digital + traditional condition to accurately assess student landing performance. When student performance was on the extreme (i.e. ‘poor’ and ‘good’), flight instructors were better able to determine whether or not a student was ready for a solo endorsement, but instructors did have difficulty distinguishing an ‘average’ student from a ‘good’ performing student. Lastly, all of the conditions presented failed to provide the proper visualizations to allow participants to make assessments of their willingness to mentor the students, and participants indicated that they did not like the students presented with the digital condition. Digital visualizations from aircraft data will require careful development in order to limit the extraneous load and reduce the intrinsic load for student flight assessment, and should be developed in collaboration with flight instructors to provide information to assist the analysis of student flight performance.