i-MASTER aims to enhance the accessibility and effectiveness of maritime simulator-based education by developing and integrating an Intelligent Learning System (ILS). The ILS will leverage state-of-the-art artifical intelligence techniques to facilitate a personalized, analytical, and adaptive way of learning in order to harmonize digital simulation infrastructure, teachers, and learners for the mission of learning.
In light of the industrial demands and educational challenges in current maritime education and training under the pandemic, this research and innovation project on “Integrating Adaptive Learning in Maritime Simulator-Based Education and Training with Intelligent Learning System (i-MASTER)” aims to unleash the potential of emerging technology application in maritime simulator-based education and training through bringing machine learning, education science, psychology and cognitive science together to formulate and deliver an innovative, personalized and adaptive training solution to students in maritime education.
An interdisciplinary approach to maritime simulator-based training.
Using artificial intelligence to redefine maritime education for students and teachers.
A roadmap towards improved maritime training.
Lead Participant
| Tasks | Methodology |
|---|---|
| T1.1 – Project management and administration: managing the day-to-day operation of the project according to approved plans, monitoring of the progress, schedule and resources, managing the data according to the FAIR principles of the EU | i-MASTER adopts an agile project management methodology to manage the project by breaking it up into several phases and constantly collaboration with all stakeholders and continuously improve through planning, executing, and evaluating at every development phase. |
| T1.2 – Innovation and societal impact management strategy development: maximizing the impact of the project throughout all stages and ensuring that the required outputs are prepared and delivered to the market in a timely manner | Applying mixed research methodology using both qualitative and quantitative indicators of impact to establish the strategy and measure the innovation and societal impact. |
| T1.3 – Communication and coordination: establishing and implementing a communication plan for the project, ensuring remote communication infrastructure is in place for the secure exchange of project information, results, deliverables, etc. | Coordinating with the project management team to establish a common communication network that ensures quick and accurate distribution of essential information related to the project. |
| T1.4 – Data Management Plan (DMP), quality assurance and risk management: Implementing procedures for quality management, monitoring, tracking, and controlling deviations and risks throughout the project duration to ensure effective implementation | Following the Horizon Europe Data Management Plan (DMP) template and recommendations regarding data management and update the DMP, quality assurance and risk management in time with the periodic evaluation and assessment of the project. |
Lead Participant
| Tasks | Methodology |
|---|---|
| T2.1 – State-of-the-art review: Analyse the state-of-the-art intelligent learning technologies and provide recommendations regarding the types of AI-enabled learning interventions for i-MASTER | Conducting rigorous knowledge syntheses regarding the state-of-the-art intelligent learning technologies through systematic literature review and industrial surveys. The analysis also contains a SWOT analysis and an examination of ILS utility and functions for i-MASTER. |
| T2.2 – Vessel navigation competency mapping: Map out the overall Knowledge, Understanding & Proficiency (KUP) competences for ship navigation from basics to advanced level | Reviewing the 66 KUPs items under STCW Table A-II/2 (for masters and chief mates) and Table A-II/1 (for officers in charge of a navigational watch). Conducting both qualitative and quantitative exploration regarding the technical and non-technical competencies that should be developed by ship navigators today and in the future. |
| T2.3 – Performance metrics and KPIs: Define the measurable Key Performance Indicators (KPIs) for each Knowledge, Understanding & Proficiency (KUP) item for ship navigation | Analytical Hierarchy Process (AHP) analysis and focus group discussions with Subject Matter Expert (SMEs) to define the obtainable KPIs for each KUP item based on the trials and ship simulation data. |
| T2.4 – Measurement methodology specification: Determine the KPI measurement methodology, frequency, and data needs | Focus group discussions and expert validation sessions would be used as a basis to determine the validity, frequency, and data needs for the KPIs |
Lead Participant
| Tasks | Methodology |
|---|---|
| T3.1 – Training scenarios design and ship simulation trials: Define the scope of training scenarios to be designed for the intelligent learning system for remote and on-site simulation and create the educational objectives and the corresponding performance metrics for each simulation exercise | Analysing various navigational scenarios and soliciting expert opinions to evaluate its appropriateness and effectiveness for students at different stages of their learning paths. |
| T3.2 – Performance patterns and instructional strategy: Conduct research on the students’ performance patterns with the developed training scenarios, determine the appropriate tutoring or instructional strategy and identify user requirements and needs to develop learning analytics and adaptive learning functions | Conducting both qualitative and quantitative explorations regarding students’ performance patterns with the developed training scenarios and soliciting the appropriate tutoring or instructional strategies. |
| T3.3 – Architecture design of the i-MASTER learning analytics and ILS: Design the architecture for the i-MASTER learning analytics dashboard and ILS by identifying the components, data requirement, their functionalities and interconnections | Applying various techniques and principles to define the modules and process of the learning analytics and ILS in sufficient detail to permit its physical coding. |
| T3.4 – Consolidate KPI and performance metrics for the learning analytics algorithm and the visualization dashboard | Materials and data gathering for maritime learning analytics dashboard and ILS development |
Lead Participant
| Tasks | Methodology |
|---|---|
| T4.1 – Development of the maritime learning analytics algorithms and visualization dashboard | Determining the architecture of the learning analytics algorithms, functionality requirements and visualization dashboards. The system architect communicates to the responsible WPs for the missing values. |
| T4.2 – Creation of database systems, data integration, configuration, test environment setup | Precising the associated data element, processing element and connecting element for the application software. |
| T4.3 – Experimental testing for remote/desktop simulation exercises to ensure that data will be processed correctly and reliable output will be produced in the desired format | Experimentally testing the learning analytics with desktop ship simulators and evaluating the data flow and the connections. |
| T4.4 – Experimental testing for on-site simulation exercises with full-mission simulators and eye tracking devices | Experimentally testing the learning analytics with on-site full mission ship simulators and evaluating the data flow and the connections. |
| T4.5 – Consolidate KPI and performance metrics for the learning analytics algorithm and the visualization dashboard | Conducting SME workshops to evaluate and validate the developed learning analytics algorithms and visualization dashboard. SME feedback could be collected through Delphi method, interviews, focus group discussions and quantitative surveys. |
Lead Participant
| Tasks | Methodology |
|---|---|
| T5.1 – Software Development of the Maritime Intelligent Learning System (ILS) | Adopting a rapid and agile model for software development that combines the iterative and incremental process and consists of several cycles of design, analysis implementation and testing. |
| T5.2 – Data Analysis and Construction of the Adaptive Learning Functions | Creating algorithms, adaptive sequence and predictive analytics that could continuously collect data and use it to guide a student through a learning path. |
| T5.3 – Data Generation and Improvement of the ML Algorithms | Validating the exhaustiveness and conceptual correctness of the models, experimentally evaluating the application validity of the required relations, and improving the quality of the student, expert and instruction model. |
| T5.4 – Maritime ILS Testable Prototype Establishment and System Integration | Establishing the first maritime ILS testable prototype |
Lead Participant
| Tasks | Methodology |
|---|---|
| T6.1 – Prototype Assessment of the Maritime ILS for Remote / Desktop Simulations | Setting up the test environment, defining the methods to solicit user feedback and expert evaluation and experimentally testing the prototype with the developed ship training scenarios. |
| T6.2 – Advancement of the Maritime ILS for On-Site Full-Mission Simulator Training | Advancing and improving the maritime ILS by extending its functionality with the information output from the on-site full-mission simulators. |
| T6.3 – Iterative Usability Testing and System Improvement | Setting up a full-mission navigational simulation environment to conduct iterative usability testing for ILS improvement. |
| T6.4 – System Functional Verification and Performance Evaluation | Performing functional verification of ILS and exploring the effectiveness of ILS on students’ operational performance through statistical analysis. |
Lead Participant
| Tasks | Methodology |
|---|---|
| T7.1 – Large-Scale Demonstration Programme | Arranging a demonstration programme among the consortium for the developed ILS prototypes for remote and full-mission simulators. |
| T7.2 – Experimental Evaluation and Improvement of the Maritime ILS Performance | Conducting experimental evaluations at multiple universities and soliciting feedback to improve the user experience and performance of the ILS. |
| T7.3 – Multi-Dimensional Impact Analysis of the Maritime ILS on the Domain of Maritime Simulator-Based Education and Training | Conducting interdisciplinary research to perform a multi-dimensional (i.e., economic, social, safety, efficiency) impact assessment of the learning analytics and the maritime ILS. |
| T7.4 – Training and Assessment Package with Pedagogical Guideline Regarding the System Capabilities of the Developed AI-Assisted ILS and how it Should be Utilized by the Simulator-Instructors | Soliciting the research and innovation outputs from phase 1 and 2 of i-MASTER to develop a comprehensive training and assessment package regarding the ILS system capabilities and functionalities in combination with pedagogical guidance to facilitate effective use of ILS in day-to-day training practices. |
Lead Participant
| Tasks | Methodology |
|---|---|
| T8.1 – Communication, Dissemination and Exploitation Strategies for the i-MASTER Project | Collaborating with the project management team in formulating and implementing i-MASTER project’s communication, dissemination, and exploitation strategies. |
| T8.2 – i-MASTER Showcase and Instructor Training Activities | Planning and organizing ship navigational training activities at multiple universities within the consortium at various phases of the project development. |
| T8.3 – EC and EU Dissemination Routes | Preparing and delivering dissemination material of i-MASTER for inclusion on the CORDIS and EUROPA websites. |
| T8.4 – Communication and Dissemination Activities | Designing distinct communication strategies for each specific audience (e.g., students, educators, shipowners, MET institutes) and developing the dissemination materials (both online social media platforms and printed materials such as posters, leaflets, brochures), participating at scientific and industrial events, exhibitions, conferences, and tradeshows to actively promote the i-MASTER outputs and to set the ground for a commercial and scientific exploitation of the project results. |
| T8.5 – Exploitation and Impact-Maximization Activities | Defining the activities to be carried out to enhance the successful exploitation of the i-MASTER technology and bring the research outputs to commercial market. |
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Tae-Eun Kim, tae.e.kim@uit.no, Mon – Fri, 8:00-16:00
