Mixed-Methods Study of Enhancing Business Interpreting Competence: The Role of a Knowledge Graph-Integrated BOPS Instructional Model Mediated by Learning Engagement and Affective Commitment
DOI:
https://doi.org/10.17507/jltr.1701.15Keywords:
BOPS instructional model, knowledge graph, business interpreting pedagogy, learning engagement, affective commitmentAbstract
This mixed-methods study evaluates the efficacy of a knowledge graph-integrated BOPS instructional model—where BOPS refers to Bridge-in, Objective problems, Participatory learning, and Summary—in blended business interpreting education. A quasi-experiment was conducted with 236 undergraduate students (experimental group, n=73; control group, n=62) over a 16-week semester to examine the model’s impact on learning outcomes. Quantitative analysis revealed that the experimental group achieved significantly higher interpreting accuracy (M=82.35) than the control group (M=78.21, p<0.01), with this improvement mediated by learning engagement (β=0.47) and affective commitment (β=0.39). Structural Equation Modeling (SEM) confirmed robust model fit (χ²/df=2.14, CFI=0.937, RMSEA=0.052), further validating that learning engagement positively predicts student performance (e.g., translation confidence, λ=0.69) and affective commitment, while affective commitment partially mediates the relationship between engagement and BOPS learning benefits (indirect effect=0.11, p=0.018). Qualitative thematic analysis of 30 open-ended responses identified four critical success factors for the model: technical usability (68%), content interactivity (72%), feedback timeliness (61%), and personalization (79%), alongside contextual challenges such as initial platform navigation difficulties. The model bridges cognitive-affective learning dynamics by embedding formative assessments and iterative achievement-reflection cycles within BOPS phases, fostering both emotional investment in learning and practical interpreting competence. These findings advance theoretical understanding of technology-enhanced language education—particularly the role of semantic scaffolding (via knowledge graphs) and affective mediation in interpreter training—while providing empirically grounded strategies for optimizing AI-integrated blended learning in business interpreting pedagogy.
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