<?xml version='1.0' encoding='UTF-8'?><metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns="http://dublincore.org/documents/dcmi-terms/"><dcterms:title>Data of the i-MASTER project: A novel initiative in maritime education and training experience</dcterms:title><dcterms:identifier>https://doi.org/10.18710/T1VQLA</dcterms:identifier><dcterms:creator>i-MASTER consortium</dcterms:creator><dcterms:publisher>DataverseNO</dcterms:publisher><dcterms:issued>2023-09-20</dcterms:issued><dcterms:modified>2024-02-22T05:52:24Z</dcterms:modified><dcterms:description>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.</dcterms:description><dcterms:subject>Other</dcterms:subject><dcterms:subject>i-MASTER project</dcterms:subject><dcterms:subject>Maritime education and training</dcterms:subject><dcterms:subject>Simulators</dcterms:subject><dcterms:subject>Nautical training</dcterms:subject><dcterms:subject>Intelligent Tutoring System (ITS)</dcterms:subject><dcterms:language>English</dcterms:language><dcterms:isReferencedBy>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/ahfe1003158, doi, https://doi.org/10.54941/ahfe1003158</dcterms:isReferencedBy><dcterms:isReferencedBy>Methodologies and Intelligent Systems for Technology Enhanced Learning, Workshops - 13th International Conference, doi, https://doi.org/10.1007/978-3-031-42134-1, https://link.springer.com/chapter/10.1007/978-3-031-42134-1_4</dcterms:isReferencedBy><dcterms:date>2023-09-20</dcterms:date><dcterms:contributor>Bhagat, Sahil</dcterms:contributor><dcterms:contributor>USN University of North-Eastern Norway</dcterms:contributor><dcterms:contributor>UiT The Arctic University of Norway</dcterms:contributor><dcterms:contributor>GU University of Gothenburg</dcterms:contributor><dcterms:contributor>VTI Swedish National Road and Transport Research Institute</dcterms:contributor><dcterms:contributor>FhG Fraunhofer CML</dcterms:contributor><dcterms:contributor>WU Wirtschaftsuniversität Wien – Vienna University of Economics and Business</dcterms:contributor><dcterms:contributor>Novia University of Applied Sciences</dcterms:contributor><dcterms:dateSubmitted>2023-08-18</dcterms:dateSubmitted><dcterms:temporal>2022-09-01</dcterms:temporal><dcterms:relation>AHFE paper: Literature review carried out using scinetometric method to develop Learning Analytics Dashboard.</dcterms:relation><dcterms:type>Survey data</dcterms:type><dcterms:type>Machine-readable data</dcterms:type><dcterms:source>Data of the AHFE_paper: Web of Science</dcterms:source><dcterms:spatial>Norway</dcterms:spatial><dcterms:license>CC0 1.0</dcterms:license></metadata>