When training Soldiers, it is crucial to monitor the progress being made in Soldier performance to evaluate the effectiveness of a new training method. Whether an organization is considering the effectiveness of training or evaluating Soldiers’ fitness for a task, the true performance cannot be judged without data. During two courses taken by military personnel in The Netherlands, and other data collections during the Bold Quest series, experimental use was made of an automated survey tool, QUEST, to record progress made by Soldiers and eventually to quantify course effectiveness. The data collection consisted of students’ course-content test scores, and multiple choice survey questions asked to the Soldiers. Included were factual tests and questions seeking subjective feedback on certain aspects of the training, looking for areas to improve. These aspects and demographics of the Soldiers were collected in a single database, connecting all data. The Bold Quest series of exercises provides an operational test environment where new technologies and training methods are demonstrated, and the subjective data collected was paired post-event with the objective systems data, providing another use case. When training feedback and effectiveness data is collected from several consecutive courses into a central database, insight into improvements in the training can be gained and trends clarified. It provides the means to correlate demographics, experience and skills, course scores, Soldiers’ feedback and direct tests. Training methods can be improved using this analysis by tailoring training to candidates based on backgrounds and skills. Correlation between demographics, experience, skills and the shown aptitude to the training can also improve the effectiveness of recruiting. This paper focuses on the benefits of using automated surveys for these training improvements, as well as the long-term goal of integrating this collection technique with other technology-enhanced learning capabilities like Experience API (xAPI) (Mueller, Dikke, Dahrendorf, 2014).