Periodic Reporting for period 2 - i-MASTER (Integrating Adaptive Learning in Maritime Simulator-Based Education and Training with Intelligent Learning System)
Reporting period: 2023-09-01 to 2024-08-31
The i-MASTER project aims to integrate emerging technologies into maritime education, enhancing nautical training effectiveness and operational safety through an Intelligent Learning System (ILS). This project addresses the pressing challenges highlighted by recent navigational incidents at sea and the integration of technological advancements in maritime education, aiming for transformative solutions. The project's structure comprises eight focused Work Packages (WPs):
WP 1 guides the project management, promoting collaborative decision-making and knowledge exchange among specialists.
WP 2 investigates intelligent learning methods and tools from other sectors for maritime simulator training applicability.
WP 3 develops simulations for remote and in-person training, maintaining exemplary standards and practices.
WP 4 constructs a learning analytics dashboard to track educational development and progress in maritime training.
WP 5 designs and prototypes the adaptive aspects of the ILS, improving remote simulations and AI-assisted training.
WP 6 integrates the ILS with ship simulation exercises and eye-tracking for immediate learner performance insights.
WP 7 conducts trials to evaluate the ILS's effectiveness.
WP 8 amplifies the project's influence, benefiting learners, educators and institutions, thereby serving the maritime sector and broader society.
WP 1 guides the project management, promoting collaborative decision-making and knowledge exchange among specialists.
WP 2 investigates intelligent learning methods and tools from other sectors for maritime simulator training applicability.
WP 3 develops simulations for remote and in-person training, maintaining exemplary standards and practices.
WP 4 constructs a learning analytics dashboard to track educational development and progress in maritime training.
WP 5 designs and prototypes the adaptive aspects of the ILS, improving remote simulations and AI-assisted training.
WP 6 integrates the ILS with ship simulation exercises and eye-tracking for immediate learner performance insights.
WP 7 conducts trials to evaluate the ILS's effectiveness.
WP 8 amplifies the project's influence, benefiting learners, educators and institutions, thereby serving the maritime sector and broader society.
The activities within each WP are detailed below:
WP1: Project management, communication, and coordination: This WP ensures effective management and communication among all consortium members. Project management planning tasks, development of gender equality plan, communication and coordination plan, research practice, quality assurance, risk management plan, and data management plan have all been completed.
WP2: Review of the state-of-the-art ILS technologies and KPI development, completed on 21st May 2023. This WP focused on comprehensive examination of the latest ILS technologies and potential application in MET. Several research tasks were completed in this WP, culminating in the publication of two research papers. This marked a milestone of specification of functional requirements and establishment of ILS technologies and KPIs.
WP3: WP3 has been instrumental in generating valuable research insights on maritime simulation scenario design, learning sources and performance standards for the established scenarios. Consolidated KPIs and performance metrics, which were essential for enhancing the learning analytics algorithm and the visualisation dashboard are generated. This advancement was a vital step in shaping the analytical capabilities of the ILS and contributed to the overall effectiveness of the training solution. All deliverables were submitted.
WP4: Using results from WP3, the task is to develop maritime learning analytics and develop a visualisation dashboard for simulations. A testing and evaluation of remote and on-site maritime simulations was conducted followed by expert validation. All deliverables were submitted and WP has been completed.
WP5: In WP5, adaptive learning function has been designed and implementation is underway. A user interactive model will be generated based on the integration of expert, learner, and instruction model. Both qualitative and quantitative studies will be carried out to validate the developed models. ILS testable prototype(s) for nautical training is being developed from the results of previous tasks. Currently, D5.4 is submitted which gives an insight of the research results regarding the evaluation and validation of the algorithms.
WP6: A prototype assessment of the maritime ILS will be carried out for remote simulation. ILS capabilities will be further extended to simulator outputs and eye tracking data to process real time data of users. Participating institutes will jointly conduct expert evaluation and validation regarding ILS systems.
WP7: Objective of WP7 is to deploy the developed learning analytics and ILS prototypes. The effectiveness and usability of prototype and its feasibility for expansion to larger maritime education and training industry will be evaluated.
WP8: This WP covers dissemination and communication of results. WP2 results were accepted for dissemination at three international outlets i.e. MIS4TEL, IEEM and AHFE conference. i-MASTER progress is made available to public through industrial events and meetings, project website, LinkedIn and other social media platforms. Currently, D8.4 is delivered which reporting the system development workshop and clustering activities.
WP1: Project management, communication, and coordination: This WP ensures effective management and communication among all consortium members. Project management planning tasks, development of gender equality plan, communication and coordination plan, research practice, quality assurance, risk management plan, and data management plan have all been completed.
WP2: Review of the state-of-the-art ILS technologies and KPI development, completed on 21st May 2023. This WP focused on comprehensive examination of the latest ILS technologies and potential application in MET. Several research tasks were completed in this WP, culminating in the publication of two research papers. This marked a milestone of specification of functional requirements and establishment of ILS technologies and KPIs.
WP3: WP3 has been instrumental in generating valuable research insights on maritime simulation scenario design, learning sources and performance standards for the established scenarios. Consolidated KPIs and performance metrics, which were essential for enhancing the learning analytics algorithm and the visualisation dashboard are generated. This advancement was a vital step in shaping the analytical capabilities of the ILS and contributed to the overall effectiveness of the training solution. All deliverables were submitted.
WP4: Using results from WP3, the task is to develop maritime learning analytics and develop a visualisation dashboard for simulations. A testing and evaluation of remote and on-site maritime simulations was conducted followed by expert validation. All deliverables were submitted and WP has been completed.
WP5: In WP5, adaptive learning function has been designed and implementation is underway. A user interactive model will be generated based on the integration of expert, learner, and instruction model. Both qualitative and quantitative studies will be carried out to validate the developed models. ILS testable prototype(s) for nautical training is being developed from the results of previous tasks. Currently, D5.4 is submitted which gives an insight of the research results regarding the evaluation and validation of the algorithms.
WP6: A prototype assessment of the maritime ILS will be carried out for remote simulation. ILS capabilities will be further extended to simulator outputs and eye tracking data to process real time data of users. Participating institutes will jointly conduct expert evaluation and validation regarding ILS systems.
WP7: Objective of WP7 is to deploy the developed learning analytics and ILS prototypes. The effectiveness and usability of prototype and its feasibility for expansion to larger maritime education and training industry will be evaluated.
WP8: This WP covers dissemination and communication of results. WP2 results were accepted for dissemination at three international outlets i.e. MIS4TEL, IEEM and AHFE conference. i-MASTER progress is made available to public through industrial events and meetings, project website, LinkedIn and other social media platforms. Currently, D8.4 is delivered which reporting the system development workshop and clustering activities.
Our project has made significant advancements in maritime performance evaluation, focusing on two main areas: refining key KPIs and enhancing the performance evaluation process. Previously, performance assessments were subjective and inconsistent. The i-MASTER project has successfully redefined and expanded a set of clear, measurable, and standardized KPIs for navigation proficiency, covering a wide range of skills for more precise evaluations. This development marks a crucial step towards objective and automated performance assessments.
The application of multimodal data mining in maritime education has opened new research avenues. Results from WP2 revealed variations in teaching approaches across institutions, affecting graduate knowledge levels. In WP3, Machine Learning techniques applied to simulation data identified distinct learning patterns, enhancing maritime learning analytics. Insights from Phase 1 (WP2 and WP3) informed effective instructional strategies and user needs for a learning analytics dashboard. Phase 2 (WP4, WP5, and WP6) then developed practical maritime learning analytics algorithms and a dashboard that enables real-time tracking of learning progress, thereby improving educational outcomes.
The application of multimodal data mining in maritime education has opened new research avenues. Results from WP2 revealed variations in teaching approaches across institutions, affecting graduate knowledge levels. In WP3, Machine Learning techniques applied to simulation data identified distinct learning patterns, enhancing maritime learning analytics. Insights from Phase 1 (WP2 and WP3) informed effective instructional strategies and user needs for a learning analytics dashboard. Phase 2 (WP4, WP5, and WP6) then developed practical maritime learning analytics algorithms and a dashboard that enables real-time tracking of learning progress, thereby improving educational outcomes.