An Adaptive Learning Model for Predicting and Analyzing Student Performance on Flight Training Tasks

As complexity and diversity of military assets increase, ensuring that military forces are well-trained becomes complex and costly when relying on traditional classroom instruction. Human instructors bear the burden of manually creating training datasets with tools that are often not geared to their missions, tasks, and objectives. Further, analyzing how well students learned their tasks often requires collecting and managing student performance over time, which may not be feasible in time-critical situations, and may consume instructor time and attention that could be spent facilitating learning. In addition, while one-to-one human tutoring has proven to be effective, it is costly and impractical to provide in every task domain. We present Multi-task Adaptive Learning Tutor (MALT), a concept for an intelligent tutoring system (ITS) for the psychomotor domain that flexibly responds to a user's current tasking, information needs, and cognitive ability to interpret information. As the user performs a series of complex psychomotor sub-tasks drawn from flight procedures implemented in a simulator, MALT will learn to predict which features contributed most to their performance. In a proof-of-concept study, we trained MALT using data collected from pilots, ranging from new student pilots to Certified Flight Instructors, while performing different flight procedures. This paper presents the MALT concept, and methods and results associated with the proof-of-concept study focused on MALT's diagnostic capability. We believe MALT to be among the first to expand the ITS beyond traditional cognitive tasks such as problem solving to include complex psychomotor tasks.