全球智库信息集成服务系统

IST classification is designed to optimize training success but not other important outcomes

• With average graduation rates of 95 percent, further efforts to optimize training success might yield minimal gains.
• However, ML models may be effective in predicting early separation and reenlistment.

Increasing the number of relevant variables can increase the accuracy of ML predictions

• The types of ML models used differed by less than 1 percent in predicting outcomes.
• Expanding the set of predictor variables in the ML models generally decreased prediction errors by approximately 5 percent.

Reclassification is a manual process and can be optimized to achieve different outcomes

• USAF might not be using the minimal cost solution for IST reclassifications or the solution that produces the maximum number of positive training and career outcomes.
• Reclassifying airmen with optimization models to achieve optimal training and career outcomes will increase the costs of reclassification.
• Alternative solutions that achieve slightly better training and career outcomes while also reducing the costs currently associated with reclassification are also possible.

Focus group discussions with airmen in IST for selected AFSs identified factors contributing to IST success and challenges and identified suggested improvements

• Airmen characteristics (e.g., motivation) and prior experiences (e.g., education), supportive instructors, and study groups contribute to IST success.
• IST challenges involve both airmen characteristics and the training base environment.
• Improvements cover such areas as prior knowledge of AFSs and what to expect from IST, curriculum design, non-IST requirements, and dormitory arrangements.

Recent trends in initial skills training (IST) for Air Force specialties (AFSs) indicate that the number of United States Air Force (USAF) enlisted personnel reclassified into other occupational specialties has increased in recent years, with a steady rise having occurred between fiscal years 2013 and 2017. Career field reclassification can result in a wide range of negative outcomes, including increased costs, delayed manning, training schedule challenges, and decreased morale. To understand and address the challenge of IST reclassification, the authors considered options for improving processes to classify and reclassify enlisted active-duty, non–prior service airmen for IST. In this report, they outline key findings from a 2019 study that employed qualitative and quantitative analyses, including machine learning (ML) models, to assess predictors of IST success (and failure). They also describe their test of an optimization model designed to identify opportunities for revising reclassification decisions in order to not only reduce the numbers of reclassified airmen but also to achieve greater job satisfaction and productivity for airmen and improve USAF retention rates.

• Chapter One

  Introduction and Background

• Chapter Two

  Air Force Classification and Reclassification Processes

• Chapter Three

  Data Available for Predicting Air Force Training and Career Outcomes

• Chapter Four

  Models to Predict Success

• Chapter Five

  Optimization Model for Reclassifying Training Eliminations

• Chapter Six

  Airmen Experiences in Initial Skills Training for Select Specialties

• Chapter Seven

  Conclusions and Recommendations

• Appendix A

  Defining and Measuring Success in Personnel Selection

• Appendix B

  Descriptive Statistics and Analytic Modeling Results

• Appendix C

  Optimization Model Methodology

• Appendix D

  Focus Group Methodology