Interfaces with full-mission ship simulators and eye tracking software facilitate performance assessments.
Learn maritime navigation at any time, any where.
Reshaping the way maritime training is conducted from the ground up.
Adaptive learning technologies help shape individual learning pathways for all students.
Individually adapted training programmes, remote and on-site.
Integrating Adaptive Learning in Maritime Simulator-Based Education and Training with Intelligent Learning System (I-MASTER)
The primary objective of i-MASTER is to study and develop an Intelligent Learning System (ILS) with maritime learning analytics and adaptive learning function for students engaged in both remote and on-site maritime simulator-based education and training.
Lead Participant
Task 1.1 – 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
Task 1.2 – 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
Task 1.3 – 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.
Task 1.4 – 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
Lead Participant
Task 2.1 – 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
Task 2.2 – Vessel navigation competency mapping: Map out the overall Knowledge, Understanding & Proficiency (KUP) competences for ship navigation from basics to advanced level
Task 2.3 – Performance metrics and KPIs: Define the measurable Key Performance Indicators (KPIs) for each Knowledge, Understanding & Proficiency (KUP) item for ship navigation
Task 2.4 – Measurement methodology specification: Determine the KPI measurement methodology, frequency, and data needs
Lead Participant
Task 3.1 – 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
Task 3.2 – 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
Task 3.3 – 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
Task 3.4 – Consolidate KPI and performance metrics for the learning analytics algorithm and the visualization dashboard
Lead Participant
Task 4.1 – Development of the maritime learning analytics algorithms and visualization dashboard
Task 4.2 – Creation of database systems, data integration, configuration, test environment setup
Task 4.3 – 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
Task 4.4 – Experimental testing for on-site simulation exercises with full-mission simulators and eye tracking devices
Task 4.5 – Consolidate KPI and performance metrics for the learning analytics algorithm and the visualization dashboard
Lead Participant
Task 5.1 – Software Development of the Maritime Intelligent Learning System (ILS)
Task 5.2 – Data Analysis and Construction of the Adaptive Learning Functions
Task 5.3 – Data Generation and Improvement of the ML Algorithms
Task 5.4 – Maritime ILS Testable Prototype Establishment and System Integration
Lead Participant
Task 6.1 – Prototype Assessment of the Maritime ILS for Remote / Desktop Simulations
Task 6.2 – Advancement of the Maritime ILS for On-Site Full-Mission Simulator Training
Task 6.3 – Iterative Usability Testing and System Improvement
Task 6.4 – System Functional Verification and Performance Evaluation
Lead Participant
Task 7.1 – Large-Scale Demonstration Programme
Task 7.2 – Experimental Evaluation and Improvement of the Maritime ILS Performance
Task 7.3 – Multi-Dimensional Impact Analysis of the Maritime ILS on the Domain of Maritime Simulator-Based Education and Training
Task 7.4 – 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
Lead Participant
Task 8.1 – Communication, Dissemination and Exploitation Strategies for the i-MASTER Project
Task 8.2 – i-MASTER Showcase and Instructor Training Activities
Task 8.3 – EC and EU Dissemination Routes
Task 8.4 – Communication and Dissemination Activities
Task 8.5 – Exploitation and Impact-Maximization Activities
High-quality professional education is the cornerstone of effective youth transitions into the labour market for the European society. During the pandemic period, the suspension of maritime simulator-based training sessions has led to many challenges with regards to skill development, certification and competency examination.
By accounting for the needs, knowledge gaps and challenges faced by today’s maritime education and training sector, the i-MASTER project has been developed to integrate emerging technologies in maritime education and training to develop an innovative Intelligent Learning System (ILS) with learning analytics and adaptive learning functions to facilitate both remote and on-site maritime simulator-based education and training. The i-MASTER solution will enhance the effectiveness and accessibility of simulator-based education and further improve safety of maritime operations in the future.
