Model of Gamified Cross-Disciplinary Project Teams Matchmaking in Oil and Gas Educational Process

The paper is devoted to the problem of cross-disciplinary student teams organization using matchmaking technologies based on the educational rating, as well as gamified activity scores in oil and gas educational process. Due to these teachers are able to create teams that bring together students with diverse skills, knowledge and level of competencies, improving the learning experience. The paper describes the problem and the process of forming interdisciplinary teams, including students of various fields of oil and gas training, ranging from the search and exploration of hydrocarbon deposits to the IT direction. Students and connections in the context of team formation are considered as a multipartite graph, where each student is characterized by a set of indicators, such as academic performance and activity score. The matchmaking models and the software module presented in the work allow the formation of equivalent interdisciplinary teams of students in real time to solve practical industry problems in case of education. The models presented in the work use machine learning components in particular different types of clustering.