Aerospace Technic and Technology

The subject matter of the article is the domain model of the future air traffic controllers’ adaptive pre-training system using the principles of the self-directed learning. This article aims to develop an ontological domain model of the future air traffic controllers’ adaptive pre-training system using synthetic simulators for practicing potential conflict solving activities, which involves the formation of self-directed learning skills at the pre-simulator training stage. The tasks: research the main modeling approaches of the adaptive learning domain; create the main requirements for the domain model of the future air traffic controllers’ adaptive pre-training system; develop an ontological domain model for the future air traffic controllers’ adaptive pre-training to solve air traffic congestion problems with self-directed learning skills formation. The methods used are analysis, comparison and synthesis; system analysis; graphic and object modeling; decomposition methods and graph theory. The following results were obtained. The ontological domain model for the future air traffic controllers’ adaptive pre-training presented in the paper can be used as the basis for developing an air traffic controllers' automated pre-training quality management system. Conclusions. The scientific novelty of the results obtained is as follows: first-time a complex ontological domain model for future air traffic controllers’ adaptive pre-training was developed. The model is based on the domain model of pre-simulator training, the cyclic model of self-directed learning and the domain model to provide an adaptive environment for self-directed learning. The model can provide generate individual training strategies for future air traffic controllers at the pre-simulator training stage. Such a model should comprehensively provide: firstly, guaranteed achievement of program-level learning outcomes by learners within a certain training period; secondly, the adaptive teaching of specialized self-directed learning competencies, in particular, special cognitive and meta-cognitive professional skills and the ability of the strategic and tactical management of their own learning.

adaptive learning; air traffic controller; pre-simulation training; automated system; self-directed learning; domain model; ontology

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