Bridging the Contextual Realism Gap: A Learning Analytics Model for STCW-Compliant Maritime English Training
DOI:
https://doi.org/10.31849/t2fdj104Keywords:
English for specific purposes, Learning analytics, Maritime education, Maritime English, Online learningAbstract
This study addresses a critical gap in online SMCP training, aiming to enhance maritime safety through effective communication, aligned with STCW competency standards. Employing a mixed-methods instrumental case study design, this research involved 60 second-year cadets at an Indonesian maritime university, by purposive sampling. To control for confounding factors, their prior English proficiency was assessed using TOEFL-equivalent baseline scores. Methods: Data were triangulated from four sources: Learning Management System (LMS) logs, STCW-aligned academic transcripts, and a validated 20-item Likert-scale survey (Cronbach’s α = 0.89), complemented with semi-structured interviews (n=10) to explore cadet perceptions in depth. Analysis involved Pearson correlation assess relationships between engagement metrics and performance, complemented by thematic analysis of qualitative data. Findings reveal a strong positive correlation between radio simulation time and oral proficiency scores (r=0.61). Critically, cadets achieved lower scores in timed radio simulations (M=75%) compared to standard oral tests (M=82%). This discrepancy was explained by thematic analysis, uncovered a fundamental 'contextual realism gap' by which simulations failed to reflect real-life stresses aboard a ship, like engine noise. Moreover, technical barriers such as audio latency prevented 65% of cadets engaged in this manner from transferring their skills across tasks. The contribution of the study is the first data-driven evidence linking online engagement to STCW outcomes through an integrated diagnosis. These findings are critically significant for English for Specific Purposes (ESP) curriculum design, suggesting the use of immersive simulations with stressors and personalized, AI-driven pronunciation feedback to meet STCW operational competency standards.
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