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Relationship between students’ attitude towards, and performance in mathematics word problems

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  • Robert Wakhata
  • Sudi Balimuttajjo
  • Védaste Mutarutinya

Abstract

The study explored the relationship between students’ attitude towards, and performance in mathematics word problems (MWTs), mediated by the active learning heuristic problem solving (ALHPS) approach. Specifically, this study investigated the correlation between students’ performance and their attitude towards linear programming word tasks (ATLPWTs). Tools for data collection were: the adapted Attitude towards Mathematics Inventory-Short Form (ATMI-SF), (α = .75) as a multidimensional measurement tool, and linear programming achievement tests (pre-test and post-test). A quantitative approach with a quasi-experimental pre-test, post-test non-equivalent control group study design was adopted. A sample of 608 eleventh-grade Ugandan students (291 male and 317 female) from eight secondary schools (both public and private) participated. Data were analyzed using PROCESS macro (v.4) for SPSS version 26. The results revealed a direct significant positive relationship between students’ performance and their ATLPWTs. Thus, students’ attitude positively and directly impacted their performance in solving linear programming word problems. The present study contributes to the literature on performance and attitude towards learning mathematics. Overall, the findings carry useful practical implications that can support theoretical and conceptual framework for enhancing students’ performance and attitude towards mathematics word problems.

Suggested Citation

  • Robert Wakhata & Sudi Balimuttajjo & Védaste Mutarutinya, 2024. "Relationship between students’ attitude towards, and performance in mathematics word problems," PLOS ONE, Public Library of Science, vol. 19(2), pages 1-26, February.
    • Handle: RePEc:plo:pone00:0278593
    DOI: 10.1371/journal.pone.0278593
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    1. H. Edwin Romeijn & Ravindra K. Ahuja & James F. Dempsey & Arvind Kumar, 2006. "A New Linear Programming Approach to Radiation Therapy Treatment Planning Problems," Operations Research, INFORMS, vol. 54(2), pages 201-216, April.
    2. repec:aoj:jeelre:v:8:y:2021:i:3:p:272-280:id:3198 is not listed on IDEAS
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