Data of the i-MASTER project: A novel initiative in maritime education and training experience

DOI

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.

Identifier
DOI https://doi.org/10.18710/T1VQLA
Related Identifier https://doi.org/10.54941/ahfe1003158
Related Identifier https://doi.org/10.1007/978-3-031-42134-1
Metadata Access https://dataverse.no/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18710/T1VQLA
Provenance
Creator i-MASTER consortium ORCID logo
Publisher DataverseNO
Contributor Kim, Tae Eun; Munim, Ziaul Haque; Sellberg, Charlott; Praetorius, Gesa; Grundmann, Robert; Schramm, Hans-Joachim; Salokorpi, Mirva; UiT The Arctic University of Norway; USN University of North-Eastern Norway; GU University of Gothenburg; VTI Swedish National Road and Transport Research Institute; FhG Fraunhofer CML; WU Wirtschaftsuniversität Wien – Vienna University of Economics and Business; Novia University of Applied Sciences
Publication Year 2023
Funding Reference European Union 101060107
Rights CC0 1.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/publicdomain/zero/1.0
OpenAccess true
Contact Kim, Tae Eun (UiT The Arctic University of Norway); Munim, Ziaul Haque (USN University of North-Eastern Norway); Sellberg, Charlott (GU Gothenburg University); Praetorius, Gesa (VTI Swedish National Road and Transport Research Inst); Grundmann, Robert (FhG Fraunhofer CML); Schramm, Hans-Joachim (WU Wirtschaftsuniversität Wien – Vienna University of Economics and Business); Salokorpi, Mirva (Novia University of Applied Sciences)
Representation
Resource Type Survey data; Dataset
Format text/plain; text/comma-separated-values; application/vnd.openxmlformats-officedocument.wordprocessingml.document; application/pdf; application/vnd.openxmlformats-officedocument.spreadsheetml.sheet; image/png
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Version 2.0
Discipline Other
Spatial Coverage (18.970W, 69.670S, 18.970E, 69.670N)