To ensure readiness of the 5th generation Air Force weapon platforms and systems, maintenance personnel must be able to quickly adapt to new competency and qualification needs. To effectively meet these demands, the current training for maintenance personnel must change from a formalised, classroom, pre-planned and one-size-fits-all training strategy to a more distributed and personalised training strategy. This requires not only a new way of training but also requires the deployment of complex technologies.

The IDTEAM project explores the possibility of a personalised training strategy and an integrated digital learning environment. Based on user-oriented use cases, technology demonstrators are being developed to evaluate the Total Learning Architecture (TLA). The technology demonstrators contain a performance observation application, a game for maintenance pre-flight check, xAPI² link and a recommender system. In this paper we will briefly touch upon every demonstrator and will elaborate on the innovative recommendation system. 

The recommender is a system that uses learner performance to recommend the next learning task that is most suitable to the learning need of the student. The recommender is built upon educational principles and translated to a technical application with the use of a progressive scoring system. The recommendation system provides a personalized learning trajectory based on competencies regarding large datasets and many learning tasks. The recommendation system also provides innovative evaluation techniques of the training curriculum. 

Keywords
COMPETENCY BASED TRAINING,EMERGING TECHNOLOGIES,LEARNING ANALYTICS,LEARNING TECHNOLOGY STANDARDS,PERSONALIZED TRAINING

Additional Keywords
Learning Ecosystems