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This study investigates the efficacy of Latent Dirichlet Allocation (LDA) and PyLDAvis in analyzing feedback from final-year engineering students. By dissecting responses to strategically selected questions, we identify key themes that include collaboration, innovation, and technical skills development. This research provides actionable insights for educators to refine teaching strategies and curriculum design to align with industry trends and student experiences. Our findings underscore the unique capabilities of LDA and PyLDAvis in capturing and visualizing students’ nuanced perspectives, thereby offering a valuable tool for enhancing responsive engineering education.

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Decoding Student Feedback in Engineering Education: A Latent Dirichlet Allocation and PyLDAvis Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Ilyas Alhassan,
  • Manjeet Rege

摘要

This study investigates the efficacy of Latent Dirichlet Allocation (LDA) and PyLDAvis in analyzing feedback from final-year engineering students. By dissecting responses to strategically selected questions, we identify key themes that include collaboration, innovation, and technical skills development. This research provides actionable insights for educators to refine teaching strategies and curriculum design to align with industry trends and student experiences. Our findings underscore the unique capabilities of LDA and PyLDAvis in capturing and visualizing students’ nuanced perspectives, thereby offering a valuable tool for enhancing responsive engineering education.