Data of the i-MASTER project: A novel initiative in maritime education and training experiencedoi:10.18710/T1VQLADataverseNO2023-09-202i-MASTER consortium, 2023, "Data of the i-MASTER project: A novel initiative in maritime education and training experience", https://doi.org/10.18710/T1VQLA, DataverseNO, V2Data of the i-MASTER project: A novel initiative in maritime education and training experiencedoi:10.18710/T1VQLAi-MASTER consortiumUSN University of North-Eastern NorwayUiT The Arctic University of NorwayGU University of GothenburgVTI Swedish National Road and Transport Research InstituteFhG Fraunhofer CMLWU Wirtschaftsuniversität Wien – Vienna University of Economics and BusinessNovia University of Applied SciencesUiT The Arctic University of Norway101060107DataverseNOUiT The Arctic University of NorwayKim, Tae EunMunim, Ziaul HaqueSellberg, CharlottPraetorius, GesaGrundmann, RobertSchramm, Hans-JoachimSalokorpi, MirvaBhagat, Sahil2023-08-18Otheri-MASTER projectMaritime education and trainingSimulatorsNautical trainingIntelligent Tutoring System (ITS)i-MASTER datasets. The i-MASTER project is an EU funded project under grant agreement No. 101060107. The project's objective is to study and develop an AI-based intelligent learning system with learning analytics and adaptive learning function for students engaged in both remote (home-based) and on-site maritime simulator education and training. The project will give insights to simulation performances. The research will work on digitalised navigational performance assessments to provide unbiased student performance evaluation and to find new ways for students and instructors to understand the learning progress. The project has a series of deliverables to reach the project objectives. Many deliverables have collected data as part of the studies of the respective deliverables. The data collected are part of simulation scenarios, interviews, questionnaires etc. The i-MASTER project is an open to public project.The data collected are stored in a database and made available to the public and researchers for re-use. Each deliverable has a ReadMe file to make it easy for users to understand the link between the data and project.2022-09-01Norway18.9718.9769.6769.67Survey dataMachine-readable dataData of the AHFE_paper: Web of Science<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>AHFE paper: Literature review carried out using scinetometric method to develop Learning Analytics Dashboard.Munim, Z., Kim, T. (2023). A Review of Learning Analytics Dashboard and a Novel Application in Maritime Simulator Training. In: Salman Nazir (eds) Training, Education, and Learning Sciences. AHFE (2023) International Conference. AHFE Open Access, vol 109. AHFE International, USA. http://doi.org/10.54941/ahfe1003158https://doi.org/10.54941/ahfe1003158Munim, Z., Kim, T. (2023). A Review of Learning Analytics Dashboard and a Novel Application in Maritime Simulator Training. In: Salman Nazir (eds) Training, Education, and Learning Sciences. AHFE (2023) International Conference. AHFE Open Access, vol 109. AHFE International, USA. http://doi.org/10.54941/ahfe1003158Methodologies and Intelligent Systems for Technology Enhanced Learning, Workshops - 13th International Conferencehttps://doi.org/10.1007/978-3-031-42134-1Methodologies and Intelligent Systems for Technology Enhanced Learning, Workshops - 13th International 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