Linear Programming Models: Identifying Common Errors in Engineering Students’ Work with Complex Word Problems

Rachael Kenney, Tuyin An, Sung Hee Kim, Nelson A. Uhan, Ji Soo Yi, Aiman Shamsul

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

In linear programming, many students find it difficult to translate a verbal description of a problem into a valid mathematical model. To better understand this, we examine the existing characteristics of college engineering students’ errors across linear programming (LP) problems. We examined textbooks to identify the types of problems typically found in introductory linear programming courses. We then developed a comprehensive set of tasks and analyzed students’ work to create a taxonomy of the errors and issues that students exhibited. From our findings, we define four categories of identified error types: (1) decision variable errors, (2) variable relationship errors, (3) notation errors, and (4) form errors. This study contributes to the research by investigating students’ work in an area of undergraduate mathematics that has not been heavily explored before. Findings suggest specific areas of focus for future work in helping students develop their understanding of linear programming models and mathematical modeling in word problems in general.

Original languageEnglish
Pages (from-to)635-655
Number of pages21
JournalInternational Journal of Science and Mathematics Education
Volume18
Issue number4
DOIs
StatePublished - Apr 1 2020

Scopus Subject Areas

  • Education
  • General Mathematics

Keywords

  • Engineering
  • Error analysis
  • Linear programming
  • Mathematization
  • Word problems

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