Deep learning elements in maritime simulation programmes: a pedagogical exploration of learner experiences

Zakirul Bhuiyan, Md Golam Jamil

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we explore the learning and teaching of a maritime simulation programme to understand its deep learning elements. We followed the mixed methods approach and collected student perception data from a maritime school, situated within a UK university, using reflection-based survey (n = 112) and three focus groups with eleven students. Findings include the needs for defining clear learning outcomes, improving the learning content to enable exploration and second-chance learning, minimising theory–practice gaps by ensuring skills-knowledge balance and in-depth scholarship building, facilitating tasks for learning preparation and learning extension, and repositioning simulation components and their assessment schemes across the academic programme. Overall, the paper provides evidence on the importance of deep learning activities in maritime simulation and suggests guidelines on improving the existing practice. Although the findings are derived from a maritime education programme, they can be considered and applied in other academic disciplines which use simulation in their teaching and learning.
Original languageEnglish
Article number18
JournalInternational Journal of Educational Technology in Higher Education
Volume18
DOIs
Publication statusPublished - 12 Apr 2021

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