Summary of the project
The course Monte Carlo Simulations for Energy Applications (4EM80) has been added to the specialization courses of the Mechanical Engineering master program. This project has been creating and implementing a Challenge-Based Learning (CBL) approach where students are challenged to design and work on research-based projects using Monte Carlo simulations applicable to societal challenges like reducing pollutant emissions and developing innovative energy storage solutions. Moreover, the course has been adding new experiences and resources to the students' toolbox, such as MC simulations for adsorption-based systems, preparing them to approach engineering problems from multiple perspectives. The students have been expected to design, manage, and evaluate small research projects in groups.
Aim of the project
The aim of the project is to offer a high-level learning experience through the implementation of this new course. Students have had the opportunity to immerse themselves in a research-based program in teams, learn how to coordinate projects, and communicate effectively with team members and external assessors like tutors, committees, and other participants. The dissemination part of the research project has been designed as a mock conference/workshop, where teams showcase their findings and compete for a Best Project Award. This combination of elements is expected to foster high engagement and an effective learning experience.
Results and learnings
The implementation of the Monte Carlo Simulation for Energy Applications course had promising outcomes, as evidenced by the positive responses from participating students.
To ensure uninterrupted progress, students were granted access to an exclusive queue in a dedicated cluster, enabling them to conduct essential simulations within the designated course timeframe. Before starting, students were tasked with completing a questionnaire to facilitate the formation of cohesive teams. The initial sessions of the course focused on imparting methodological insights and practical tutorials on software utilization. The teams selected research challenges, conducted bibliographic searches, and presented their research idea. The early determination of project focus allowed ample time for in-depth exploration and analysis.
LIS's involvement proved instrumental in creating accounts, facilitating access, and promptly resolving technical challenges, such as temporary downtime of the computational cluster. To maintain optimal guidance and support, class size was limited, with four groups comprising five students each. All groups successfully obtained reliable results, undertook post-processing and data analysis, and contextualized findings within the scope of their selected topics.
During an informal evaluation, students were asked to provide feedback on the teaching methodology, content, and structure of the course. The results were very positive. The stronger points were the enthusiasm for working in groups, collaborating, dividing tasks, and discussing the results with their teammates, tutors, and teachers. The high attention and fast response of the teachers involved were very appreciated.
As an improvement point, that will be implemented in the following editions, students expressed an interest in more theoretical instruction to fortify foundational knowledge.
Conclusion
The students participating in The Monte Carlo Simulation for Energy Applications course achieved the learning objectives. The CBL methodology instilled a sense of ownership and enthusiasm among participants. While the success of collaborative learning is evident, the feedback received emphasizes the importance of augmenting theoretical instruction for enhanced skill transferability beyond the confines of the classroom. We consider the pilot's results a success and will keep improving for the best learning experience.
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