IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v9y2022i1d10.1057_s41599-022-01449-1.html
   My bibliography  Save this article

Secondary school students’ attitude towards mathematics word problems

Author

Listed:
  • Robert Wakhata

    (University of Rwanda, College of Education (UR-CE))

  • Védaste Mutarutinya

    (University of Rwanda (UR))

  • Sudi Balimuttajjo

    (Mbarara University of Science and Technology)

Abstract

Students’ positive attitude towards mathematics leads to better performance and may influence their overall achievement and application of mathematics in real-life. In this article, we present the findings of an investigation on students’ attitude towards linear programming (LP) mathematics word problems (LPMWPs). An explanatory sequential quasi-experimental design involving a pre-intervention-intervention-post-intervention non-equivalent control group was adopted. A sample of 851 grade 11 Ugandan students (359 male and 492 female) from eight secondary schools (public and private) participated. Cluster random sampling was applied to select respondents from eight schools; four from central Uganda and four from eastern Uganda. The attitude towards mathematics inventory-short form (ATMI-SF) was adapted (with α = 0.75) as a multidimensional measurement tool for measuring students’ attitude towards LPMWPs. The results revealed that students’ attitude towards LPMWPs was generally negative. Enjoyment, motivation, and confidence were weekly negatively correlated while usefulness was positively correlated. Additionally, the results found no significant statistical relationship between students’ attitudes towards LPMWPs and their age, gender, school location, school status, and school ownership. The discrepancy is perhaps explained by both theoretical and/or psychometric limitations, and related factors, for instance, students’ academic background, school characteristics, and transitional beliefs from primary to secondary education. This study acknowledges the influence of and supplements other empirical findings on students’ attitude towards learning mathematics word problems. The present study provides insight to different educational stakeholders in assessing students’ attitude towards LPMWPs and may provide remediation and interventional strategies aimed at creating students’ conceptual change. The study recommends that teachers should cultivate students’ interests in mathematics as early as possible. Varying classroom instructional practices could be a remedy to enhance students’ understanding, achievement, and, motivation in learning mathematics word problems. The teachers’ continuous professional development courses should be enacted to improve instruction, assessment, and students’ attitude. Overall, the study findings support the theoretical framework for enhancing the learning of mathematics word problems in general and LP in particular.

Suggested Citation

  • Robert Wakhata & Védaste Mutarutinya & Sudi Balimuttajjo, 2022. "Secondary school students’ attitude towards mathematics word problems," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    • Handle: RePEc:pal:palcom:v:9:y:2022:i:1:d:10.1057_s41599-022-01449-1
    DOI: 10.1057/s41599-022-01449-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-022-01449-1
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-022-01449-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sunghwan Hwang & Taekwon Son, 2021. "Students’ Attitude toward Mathematics and its Relationship with Mathematics Achievement," Journal of Education and e-Learning Research, Asian Online Journal Publishing Group, vol. 8(3), pages 272-280.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lucía Mellado & Laura Parte, 2024. "Images of the auditor’s job and associated emotions: a dynamic analysis," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Z. Taşkın & J. Smith & H. Romeijn, 2012. "Mixed-integer programming techniques for decomposing IMRT fluence maps using rectangular apertures," Annals of Operations Research, Springer, vol. 196(1), pages 799-818, July.
    2. Matthias Ehrgott & Çiğdem Güler & Horst Hamacher & Lizhen Shao, 2010. "Mathematical optimization in intensity modulated radiation therapy," Annals of Operations Research, Springer, vol. 175(1), pages 309-365, March.
    3. Timothy C. Y. Chan & Tim Craig & Taewoo Lee & Michael B. Sharpe, 2014. "Generalized Inverse Multiobjective Optimization with Application to Cancer Therapy," Operations Research, INFORMS, vol. 62(3), pages 680-695, June.
    4. Wei Chen & Yixin Lu & Liangfei Qiu & Subodha Kumar, 2021. "Designing Personalized Treatment Plans for Breast Cancer," Information Systems Research, INFORMS, vol. 32(3), pages 932-949, September.
    5. Misic, V.V. & Aleman, D.M. & Sharpe, M.B., 2010. "Neighborhood search approaches to non-coplanar beam orientation optimization for total marrow irradiation using IMRT," European Journal of Operational Research, Elsevier, vol. 205(3), pages 522-527, September.
    6. Ali Ajdari & Fatemeh Saberian & Archis Ghate, 2020. "A Theoretical Framework for Learning Tumor Dose-Response Uncertainty in Individualized Spatiobiologically Integrated Radiotherapy," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 930-951, October.
    7. Marc C. Robini & Feng Yang & Yuemin Zhu, 2020. "A stochastic approach to full inverse treatment planning for charged-particle therapy," Journal of Global Optimization, Springer, vol. 77(4), pages 853-893, August.
    8. Shabbir Ahmed & Ozan Gozbasi & Martin Savelsbergh & Ian Crocker & Tim Fox & Eduard Schreibmann, 2010. "An Automated Intensity-Modulated Radiation Therapy Planning System," INFORMS Journal on Computing, INFORMS, vol. 22(4), pages 568-583, November.
    9. Dursun, Pınar & Taşkın, Z. Caner & Altınel, İ. Kuban, 2019. "The determination of optimal treatment plans for Volumetric Modulated Arc Therapy (VMAT)," European Journal of Operational Research, Elsevier, vol. 272(1), pages 372-388.
    10. Dunbar, Michelle & O’Brien, Ricky & Froyland, Gary, 2020. "Optimising lung imaging for cancer radiation therapy," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1038-1052.
    11. Fabio Vitor & Todd Easton, 2018. "The double pivot simplex method," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 87(1), pages 109-137, February.
    12. Özge Karanfil & Yaman Barlas, 2008. "A Dynamic Simulator for the Management of Disorders of the Body Water Homeostasis," Operations Research, INFORMS, vol. 56(6), pages 1474-1492, December.
    13. Chan, Timothy C.Y. & Mahmoudzadeh, Houra & Purdie, Thomas G., 2014. "A robust-CVaR optimization approach with application to breast cancer therapy," European Journal of Operational Research, Elsevier, vol. 238(3), pages 876-885.
    14. Hao Howard Zhang & Leyuan Shi & Robert Meyer & Daryl Nazareth & Warren D'Souza, 2009. "Solving Beam-Angle Selection and Dose Optimization Simultaneously via High-Throughput Computing," INFORMS Journal on Computing, INFORMS, vol. 21(3), pages 427-444, August.
    15. Hanif Malekpoor & Nishikant Mishra & Sameer Kumar, 2022. "A novel TOPSIS–CBR goal programming approach to sustainable healthcare treatment," Annals of Operations Research, Springer, vol. 312(2), pages 1403-1425, May.
    16. Z. Caner Taşkın & J. Cole Smith & H. Edwin Romeijn & James F. Dempsey, 2010. "Optimal Multileaf Collimator Leaf Sequencing in IMRT Treatment Planning," Operations Research, INFORMS, vol. 58(3), pages 674-690, June.
    17. Sera Kahruman & Elif Ulusal & Sergiy Butenko & Illya Hicks & Kathleen Diehl, 2012. "Scheduling the adjuvant endocrine therapy for early stage breast cancer," Annals of Operations Research, Springer, vol. 196(1), pages 683-705, July.
    18. Kim, Minsun & Ghate, Archis & Phillips, Mark H., 2012. "A stochastic control formalism for dynamic biologically conformal radiation therapy," European Journal of Operational Research, Elsevier, vol. 219(3), pages 541-556.
    19. Kgalalelo Kenosi & Samuel Mutua Mutweleli & Richard Mbithi Wambua, 2024. "Relationship Between Attitudes towards Mathematics and Mathematics Performance among Form Three Students in the Southeast Sub- Region, Botswana," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(8), pages 1836-1845, August.
    20. Thomas Bortfeld & Timothy C. Y. Chan & Alexei Trofimov & John N. Tsitsiklis, 2008. "Robust Management of Motion Uncertainty in Intensity-Modulated Radiation Therapy," Operations Research, INFORMS, vol. 56(6), pages 1461-1473, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palcom:v:9:y:2022:i:1:d:10.1057_s41599-022-01449-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.