Improving Students’ Mathematical Connections in Linear Programming through a GeoGebra-Supported Local Learning Design Based on Realistic Mathematics Education

Authors

DOI:

https://doi.org/10.56294/saludcyt20252292

Keywords:

Mathematical Connection Abilities, Realistic Mathematics Education, Local Instructional Theory, GeoGebra, Blended Learning

Abstract

Introduction: Students often struggle to develop mathematical connection abilities in linear programming due to its abstract nature and procedural teaching methods. While Realistic Mathematics Education (RME) and Local Instructional Theory (LIT) offer structured learning trajectories, and GeoGebra provides dynamic visualization, their integration into a cohesive learning design for linear programming remains underexplored.
Objective: This study aimed to develop and evaluate a GeoGebra-supported local learning design grounded in RME to improve students' mathematical connection abilities in linear programming.
Method: A design-based research methodology was employed, involving iterative development and testing. A quasi-experimental pretest-posttest control group design was used to assess effectiveness with 68 undergraduate mathematics education students. The experimental group (n=34) received instruction via the developed learning design, while the control group (n=34) received conventional instruction. Data were collected using a Mathematical Connection Ability Test (MCAT), observations, and questionnaires.
Results: The expert validation showed high scores for content validity (M=4.64), construct validity (M=4.51), and practicality (M=4.17). Quantitatively, the experimental group significantly outperformed the control group in post-test scores (t(66)=5.94, p<.001) with a large effect size (Cohen's d=1.45), demonstrating a greater improvement in connecting concepts to real-world contexts and within problem-solving processes. Qualitatively, students valued the contextualized approach and GeoGebra's interactivity for facilitating deeper understanding.
Conclusions: The GeoGebra-assisted LIT based on RME significantly enhanced students' mathematical connection abilities. The study proposes an effective, integrated instructional design for linear programming and recommends future research to optimize time allocation and extend the model to other mathematical topics.
Key Words: Mathematical Connection Abilities; Realistic Mathematics Education; Local Instructional Theory; GeoGebra; Blended Learning.

 

References

1. N. F. Alfiyah, I. Rosdianti, and L. S. Zanthy, “Analisis Kemampuan Koneksi Matematik dan Self Confidence Siswa SMP melalui Model Pembelajaran Think Pair Share,” Desimal J. Mat., vol. 2, no. 3, pp. 289–295, Sep. 2019, doi: 10.24042/djm.v2i3.4469. DOI: https://doi.org/10.24042/djm.v2i3.4469

2. D. Amsari, I. M. Arnawa, and Y. Yerizon, “Development of a local instructional theory for the sequences and series concept based on contextual teaching and learning,” Linguist. Cult. Rev., vol. 6, pp. 434–449, Jan. 2022, doi: 10.21744/lingcure.v6nS2.2136. DOI: https://doi.org/10.21744/lingcure.v6nS2.2136

3. Armiati, A. Fauzan, Y. Harisman, and F. Sya’bani, “Local instructional theory of probability topics based on realistic mathematics education for eight-grade students,” J. Math. Educ., vol. 13, no. 4, pp. 703–722, Dec. 2022, doi: 10.22342/jme.v13i4.pp703-722. DOI: https://doi.org/10.22342/jme.v13i4.pp703-722

4. A. Arnellis, A. Fauzan, I. M. Arnawa, and Y. Yerizon, “The Effect of Realistic Mathematics Education Approach Oriented Higher Order Thinking Skills to Achievements’ Calculus,” J. Phys. Conf. Ser., vol. 1554, no. 1, p. 012033, May 2020, doi: 10.1088/1742-6596/1554/1/012033. DOI: https://doi.org/10.1088/1742-6596/1554/1/012033

5. V. Braun and V. Clarke, “Using thematic analysis in psychology,” Qual. Res. Psychol., vol. 3, no. 2, pp. 77–101, Jan. 2006, doi: 10.1191/1478088706qp063oa. DOI: https://doi.org/10.1191/1478088706qp063oa

6. A. Chakraborty, V. Chandru, and M. R. Rao, “A linear programming primer: from Fourier to Karmarkar,” Ann. Oper. Res., vol. 287, no. 2, pp. 593–616, Apr. 2020, doi: 10.1007/s10479-019-03186-2. DOI: https://doi.org/10.1007/s10479-019-03186-2

7. A. Fauzan and F. Diana, “Learning trajectory for teaching number patterns using RME approach in junior high schools,” J. Phys. Conf. Ser., vol. 1470, no. 1, p. 012019, Feb. 2020, doi: 10.1088/1742-6596/1470/1/012019. DOI: https://doi.org/10.1088/1742-6596/1470/1/012019

8. A. Fauzan, E. Musdi, and J. Afriadi, “Developing learning trajectory for teaching statistics at junior high school using RME approach,” J. Phys. Conf. Ser., vol. 1088, p. 012040, Sep. 2018, doi: 10.1088/1742-6596/1088/1/012040. DOI: https://doi.org/10.1088/1742-6596/1088/1/012040

9. Yarman, A. Fauzan, Armiati, and Lufri, “Hypothetical Learning Trajectory for First-Order Ordinary Differential Equations,” 2020. doi: 10.2991/assehr.k.201209.245. DOI: https://doi.org/10.2991/assehr.k.201209.245

10. P. Fitriasari, “PEMANFAATAN SOFTWARE GEOGEBRA DALAM PEMBELAJARAN MATEMATIKA,” J. Pendidik. Mat. RAFA, vol. 3, no. 1, pp. 57–69, Oct. 2017, doi: 10.19109/jpmrafa.v3i1.1441. DOI: https://doi.org/10.19109/jpmrafa.v3i1.1441

11. Yerizon, I. M. Arnawa, N. Fitriani, and N. M. Tajudin, “Constructing Calculus Concepts through Worksheet Based Problem-Based Learning Assisted by GeoGebra Software,” HighTech Innov. J., vol. 3, no. 3, pp. 282–296, Aug. 2022, doi: 10.28991/HIJ-2022-03-03-04. DOI: https://doi.org/10.28991/HIJ-2022-03-03-04

12. R. R. Hake, “Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses,” Am. J. Phys., vol. 66, no. 1, pp. 64–74, Jan. 1998, doi: 10.1119/1.18809. DOI: https://doi.org/10.1119/1.18809

13. J. C. Hortelano and M. Prudente, “Effects of the theory of didactical situations application in mathematics education: A metasynthesis,” J. Pedagog. Res., Aug. 2024, doi: 10.33902/JPR.202426908. DOI: https://doi.org/10.33902/JPR.202426908

14. Yerizon, Triwani, and E. Musdi, “Effectiveness of Mathematics Learning Devices Based on Flipped Classroom to Improve Mathematical Critical Thinking Ability Students,” Int. J. Educ. Manag. Eng., vol. 12, no. 3, pp. 41–46, Jun. 2022, doi: 10.5815/ijeme.2022.03.05. DOI: https://doi.org/10.5815/ijeme.2022.03.05

15. W. W. Porter, C. R. Graham, K. A. Spring, and K. R. Welch, “Blended learning in higher education: Institutional adoption and implementation,” Comput. Educ., vol. 75, pp. 185–195, Jun. 2014, doi: 10.1016/j.compedu.2014.02.011. DOI: https://doi.org/10.1016/j.compedu.2014.02.011

16. O. Rızvanoğlu, S. Kaya, M. Ulukavak, and M. İ. Yeşilnacar, “Optimization of municipal solid waste collection and transportation routes, through linear programming and geographic information system: a case study from Şanlıurfa, Turkey,” Environ. Monit. Assess., vol. 192, no. 1, p. 9, Jan. 2020, doi: 10.1007/s10661-019-7975-1. DOI: https://doi.org/10.1007/s10661-019-7975-1

17. N. Listiawati et al., “Analysis of implementing Realistic Mathematics Education principles to enhance mathematics competence of slow learner students,” J. Math. Educ., vol. 14, no. 4, pp. 683–700, Aug. 2023, doi: 10.22342/jme.v14i4.pp683-700. DOI: https://doi.org/10.22342/jme.v14i4.pp683-700

18. P. Mishra and M. J. Koehler, “Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge,” Teach. Coll. Rec. Voice Scholarsh. Educ., vol. 108, no. 6, pp. 1017–1054, Jun. 2006, doi: 10.1111/j.1467-9620.2006.00684.x. DOI: https://doi.org/10.1177/016146810610800610

19. J. Confrey, G. Gianopulos, W. McGowan, M. Shah, and M. Belcher, “Scaffolding learner-centered curricular coherence using learning maps and diagnostic assessments designed around mathematics learning trajectories,” ZDM, vol. 49, no. 5, pp. 717–734, Oct. 2017, doi: 10.1007/s11858-017-0869-1. DOI: https://doi.org/10.1007/s11858-017-0869-1

20. A. S. Ulfah, Y. Yerizon, and I. M. Arnawa, “Preliminary Research of Mathematics Learning Device Development Based on Realistic Mathematics Education (RME),” J. Phys. Conf. Ser., vol. 1554, no. 1, p. 012027, May 2020, doi: 10.1088/1742-6596/1554/1/012027. DOI: https://doi.org/10.1088/1742-6596/1554/1/012027

21. I. Risdiyanti and R. C. I. Prahmana, “DESIGNING LEARNING TRAJECTORY OF SET THROUGH THE INDONESIAN SHADOW PUPPETS AND MAHABHARATA STORIES,” Infin. J., vol. 10, no. 2, p. 331, Aug. 2021, doi: 10.22460/infinity.v10i2.p331-348. DOI: https://doi.org/10.22460/infinity.v10i2.p331-348

22. A. K. ERBAŞ, M. KERTİL, B. ÇETİNKAYA, E. ÇAKIROĞLU, C. ALACACI, and S. BAŞ, “Mathematical Modeling in Mathematics Education: Basic Concepts and Approaches,” Educ. Sci. Theory Pract., Aug. 2014, doi: 10.12738/estp.2014.4.2039. DOI: https://doi.org/10.12738/estp.2014.4.2039

23. L. A. Palinkas, S. J. Mendon, and A. B. Hamilton, “Innovations in Mixed Methods Evaluations,” Annu. Rev. Public Health, vol. 40, no. 1, pp. 423–442, Apr. 2019, doi: 10.1146/annurev-publhealth-040218-044215. DOI: https://doi.org/10.1146/annurev-publhealth-040218-044215

24. Rusdi, A. Fauzan, I. M. Arnawa, and Lufri, “Designing Mathematics Learning Models Based on Realistic Mathematics Education and Literacy,” J. Phys. Conf. Ser., vol. 1471, no. 1, p. 012055, Feb. 2020, doi: 10.1088/1742-6596/1471/1/012055. DOI: https://doi.org/10.1088/1742-6596/1471/1/012055

25. L. Sarvita and H. Syarifuddin, “The developed hypothetical learning trajectory for integral topic based on realistic mathematics education,” J. Phys. Conf. Ser., vol. 1554, no. 1, p. 012032, May 2020, doi: 10.1088/1742-6596/1554/1/012032. DOI: https://doi.org/10.1088/1742-6596/1554/1/012032

26. A. Septian, Darhim, and S. Prabawanto, “Mathematical representation ability through geogebra-assisted project-based learning models,” J. Phys. Conf. Ser., vol. 1657, no. 1, p. 012019, Oct. 2020, doi: 10.1088/1742-6596/1657/1/012019. DOI: https://doi.org/10.1088/1742-6596/1657/1/012019

27. H. Crompton and D. Burke, “Research Trends in the Use of Mobile Learning in Mathematics,” in Blended Learning, IGI Global, pp. 2090–2104. doi: 10.4018/978-1-5225-0783-3.ch101. DOI: https://doi.org/10.4018/978-1-5225-0783-3.ch101

28. S. Syafriandi, A. Fauzan, L. Lufri, and A. Armiati, “Designing hypothetical learning trajectory for learning the importance of hypothesis testing,” J. Phys. Conf. Ser., vol. 1554, no. 1, p. 012045, May 2020, doi: 10.1088/1742-6596/1554/1/012045. DOI: https://doi.org/10.1088/1742-6596/1554/1/012045

29. P. Sztajn, P. H. Wilson, C. Edgington, and M. Myers, “Mathematics professional development as design for boundary encounters,” ZDM, vol. 46, no. 2, pp. 201–212, Apr. 2014, doi: 10.1007/s11858-013-0560-0. DOI: https://doi.org/10.1007/s11858-013-0560-0

30. B. Tamam and D. Dasari, “The use of Geogebra software in teaching mathematics,” J. Phys. Conf. Ser., vol. 1882, no. 1, p. 012042, May 2021, doi: 10.1088/1742-6596/1882/1/012042. DOI: https://doi.org/10.1088/1742-6596/1882/1/012042

31. I. W. Widana, “Realistic Mathematics Education (RME) untuk Meningkatkan Kemampuan Pemecahan Masalah Matematis Siswa di Indonesia,” J. Elem., vol. 7, no. 2, pp. 450–462, Jul. 2021, doi: 10.29408/jel.v7i2.3744. DOI: https://doi.org/10.29408/jel.v7i2.3744

32. Y. Yulia, E. Musdi, J. Afriadi, and I. Wahyuni, “Developing a hypothetical learning trajectory of fraction based on RME for junior high school,” J. Phys. Conf. Ser., vol. 1470, no. 1, p. 012015, Feb. 2020, doi: 10.1088/1742-6596/1470/1/012015. DOI: https://doi.org/10.1088/1742-6596/1470/1/012015

33. Z. Zetriuslita, N. Nofriyandi, and E. Istikomah, “The effect of geogebra-assisted direct instruction on students’ self-efficacy and self-regulation,” Infin. J., vol. 9, no. 1, p. 41, Jan. 2020, doi: 10.22460/infinity.v9i1.p41-48. DOI: https://doi.org/10.22460/infinity.v9i1.p41-48

34. Zulhendri, Ahmad Fauzan, Made Arnawa, Edwin Musdi, and Yerizon, “Analysis Of Mathematics Student Error To Solve Problems Of Linear Programs,” Int. J. Humanit. Educ. Soc. Sci., vol. 1, no. 5, Apr. 2022, doi: 10.55227/ijhess.v1i5.156. DOI: https://doi.org/10.55227/ijhess.v1i5.156

35. S. Amirzadeh, D. Rasouli, and H. Dargahi, “Assessment of validity and reliability of the feedback quality instrument,” BMC Res. Notes, vol. 17, no. 1, p. 227, Aug. 2024, doi: 10.1186/s13104-024-06881-x. DOI: https://doi.org/10.1186/s13104-024-06881-x

36. N. Hazizah, R. Rusdinal, I. Ismaniar, and M. A. Rahman, "Warrior kids` games on improving the self-efficacy abilities and fine motor skills of 5-6 years old children," Retos, vol. 56, p. 639–647. 2024, doi: 10.47197/retos.v56.104892 DOI: https://doi.org/10.47197/retos.v56.104892

37. M. Zainil, A. K. Kenedi, R. Rahmatina, and T. Indrawati, "The influence of STEM-based digital learning on 6C skills of elementary school students," Open Educ. Stud., vol. 6, no. 1, p. 20240039. 2024, doi: 10.20448/jeelr.v10i1.4336 DOI: https://doi.org/10.1515/edu-2024-0039

38. A. J. Jusoh, M. K. W. Imami, A. N. M. Isa, S. Z. Omar, A. Abdullah, and S. Wahab, "Verification the reliability and validity of a Malaysian version of rathus assertiveness schedule as drug prevention scale," Islamic G. and C. J., vol. 6, no. 2, p. 1-16. 2023, doi: 10.25217/0020236369700 DOI: https://doi.org/10.25217/0020236369700

39. E. R. K. Waty, Y. K. Nengsih, M. A. and Rahman, "The quality of teacher-made summative tests for Islamic education subject teachers in Palembang Indonesia," Cakrawala Pendidikan: J. Ilmiah Pendidikan, vol. 43, no. 1, p. 192-203. 2024, doi: 10.21831/cp.v43i1.53558 DOI: https://doi.org/10.21831/cp.v43i1.53558

40. A. Arwin, A. K. Kenedi, Y. Anita, H. Hamimah, and M. Zainil, "STEM-based digital disaster learning model for disaster adaptation ability of elementary school students," Int. J. of Eva. and R. in Edu., v. 13, no. 5, p. 3248–3258. 2024, doi: 10.11591/ijere.v13i5.29616. DOI: https://doi.org/10.11591/ijere.v13i5.29616

Downloads

Published

2025-10-07

How to Cite

1.
Zulhendri Z, Arnawa IM, Musdi E. Improving Students’ Mathematical Connections in Linear Programming through a GeoGebra-Supported Local Learning Design Based on Realistic Mathematics Education. Salud, Ciencia y Tecnología [Internet]. 2025 Oct. 7 [cited 2025 Nov. 28];5:2292. Available from: https://sct.ageditor.ar/index.php/sct/article/view/2292