Using Machine Learning Methods to Understand Students' Performance in an Engineering Course

Wei Lek Kwan; Gim-Yang Maggie Pee; Li Ling Apple Koh; Mei Xuan Tan

doi:10.1109/EDUCON52537.2022.9766744

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Conference proceeding

Using Machine Learning Methods to Understand Students' Performance in an Engineering Course

Wei Lek Kwan, Gim-Yang Maggie Pee, Li Ling Apple Koh and Mei Xuan Tan

IEEE Global Engineering Education Conference, Vol.2022-, pp.537-540

28/03/2022

DOI: https://doi.org/10.1109/EDUCON52537.2022.9766744

Abstract

analytical thinking

Conferences

engineering

Engineering education

Machine learning

student learning

undergraduate

Machine learning methods were applied to analyze students' responses to clicker questions and individual exam questions. It was found that students who performed poorly in a sub-set of clicker questions tend to not do well in an exam question that requires analytical thinking skills (cognitively demanding) even though they fare well in other application type (procedurally focused) questions. We conjecture that such students can be identified early using clicker questions to facilitate appropriate interventions.

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Title: Using Machine Learning Methods to Understand Students' Performance in an Engineering Course
Creators - without role: Wei Lek Kwan - Singapore University of Technology and Design
Gim-Yang Maggie Pee - Singapore University of Technology and Design
Li Ling Apple Koh - Singapore University of Technology and Design
Mei Xuan Tan - Singapore University of Technology and Design
Publication Details: IEEE Global Engineering Education Conference, Vol.2022-, pp.537-540
Publisher: IEEE
Number of pages: 4
Identifiers: 9912380109846
Academic Unit: SCI,MATH & Technology
Language: English
Resource Type: Conference proceeding

Using Machine Learning Methods to Understand Students' Performance in an Engineering Course

Abstract

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