Research Article

Assessing the Impact of Dynamic Software Environments (MATLAB) on Rural-Based Pre-Service Teachers’ Spatial-Visualisation Skills

G. Amevor 1 , Anass Bayaga 2 * , Michael J. Bossé 3
More Detail
1 Department of Mathematics, Science, and Technology Education, University of Zululand, South Africa2 Nelson Mandela University, Faculty of Education, Port Elizabeth, South Africa3 Appalachian State University, Boone, NC, USA* Corresponding Author
Contemporary Educational Technology, 13(4), October 2021, ep327, https://doi.org/10.30935/cedtech/11235
OPEN ACCESS   1732 Views   1079 Downloads
Download Full Text (PDF)

ABSTRACT

Dynamic visual tools such as MATLAB have inbuilt features which are believed to be able to empower students to learn through the visualisation of three-dimensional objects. While student learning through MATLAB has been investigated regarding students in urban settings, only a handful of studies have investigated how MATLAB can assist students in rural settings. Spatial visualisation (SV) as a measure or reflection of one’s cognitive reasoning is affected by family social economic status (SES). For instance, it is argued that SES in combination with other components, do enhance cognitive development in different ways. What is meant is that components such as but not exclusively, economic and occupational components of SES may vary and hence provide opportunities for generating better understanding of education (cognition). In this study, we randomly selected 100 second-year rural-based pre-service teachers in a vector calculus class at University of Zululand (UNIZULU). Students need SV skills to learn vector calculus and the Purdue spatial-visualization test/rotations (PSVT/R) is well established for measuring individuals’ spatial reasoning. In this study, spatial reasoning skills were assessed through a vector calculus pre-test and through a post-test using the Purdue spatial-visualization test/rotations (PSVT/R). The experimental group of students learned the vector calculus topics supported by activities and investigations using MATLAB. Duval’s Theory of Register of Semiotic Representation (TRSR) was employed to comprehend the impact of MATLAB on rural-based pre-service teachers’ spatial-visualisation skills. From using an independent sample t-test, our findings indicated that, for participants in this study, using MATLAB had positive impact on the rural-based pre-service teachers’ SV skills.
Keywords: spatial-visualisation skills, dynamic visual tool and learning, spatial reasoning, vector calculus and mental rotation

CITATION (APA)

Amevor, G., Bayaga, A., & Bossé, M. J. (2021). Assessing the Impact of Dynamic Software Environments (MATLAB) on Rural-Based Pre-Service Teachers’ Spatial-Visualisation Skills. Contemporary Educational Technology, 13(4), ep327. https://doi.org/10.30935/cedtech/11235

REFERENCES

  1. Andreatos, A. S., & Zagorianos, A. (2009). MATLAB GUI application for teaching control systems. In Proceedings of the 6th WSEAS International Conference on engineering education, 208-218.
  2. Arıcı, S., & Aslan-Tutak, F. (2015). The effect of origami-based instruction on spatial visualization, geometry achievement, and geometric reasoning. International Journal of Science and Mathematics Education, 13(1), 179-200. https://doi.org/10.1007/s10763-013-9487-8
  3. Baltaci, S., & Yildiz, A. (2015). GeoGebra 3D from the Perspectives of Elementary Pre-Service Mathematics Teachers Who Are Familiar with a Number of Software Programs. Online Submission, 10(1), 12-17.
  4. Bodner, G. M., & Guay, R. B. (1997). The Purdue visualization of rotations test. The Chemical Educator, 2(4), 1-17. https://doi.org/10.1007/s00897970138a
  5. Bollen, L., van Kampen, P., & De Cock, M. (2015). Students’ difficulties with vector calculus in electrodynamics. Physical Review Special Topics-Physics Education Research, 11(2), 234-250. https://doi.org/10.1103/PhysRevSTPER.11.020129
  6. Casey, B. M., Pezaris, E., Fineman, B., Pollock, A., Demers, L., & Dearing, E. (2015). A longitudinal analysis of early spatial skills compared to arithmetic and verbal skills as predictors of fifth-grade girls’ math reasoning. Learning and Individual Differences, 40, 90-100. https://doi.org/10.1016/j.lindif.2015.03.028
  7. Ćurčić, M., Milinković, D. and Radivojević, D. (2018). Educational Computer Software in the Function of Integrating and Individualization in Teaching of Mathematics and Knowledge of Nature. EURASIA Journal Maths, Science and Technology Education 14(12). 1607-1619. https://doi.org/10.29333/ejmste/93808
  8. Duval, R. (1995). Representation, Vision and Visualization: Cognitive Functions in Mathematical Thinking. Basic Issues for Learning. Sage.
  9. Ferrer, F. P. (2016). Investigating students’ learning difficulties in integral calculus. People: International Journal of Social Sciences, 2(1), 45-57. https://doi.org/10.20319/pijss.2016.s21.310324
  10. Fleisch, D. A. (2011). A student’s guide to vectors and tensors. Cambridge University Press. https://doi.org/10.1017/CBO9781139031035
  11. Gire, E., & Price, E. (2012). Graphical representations of vector functions in upper-division E&M. In AIP Conference Proceedings, 141(1), 27-30. https://doi.org/10.1063/1.3679985
  12. Hodanbosi, C. L. (2001). A comparison of the effects of using a dynamic geometry software program and construction tools on learner achievement. Kent State University.
  13. Hoffkamp, A. (2011). The use of interactive visualizations to foster the understanding of concepts of calculus: design principles and empirical results. ZDM, 43(3), 359-372. https://doi.org/10.1007/s11858-011-0322-9
  14. Höffler, T. N. (2010). Spatial ability: Its influence on learning with visualizations—a meta-analytic review. Educational psychology review, 22(3), 245-269. https://doi.org/10.1007/s10648-010-9126-7
  15. Hubbard, E. M., Piazza, M., Pinel, P., & Dehaene, S. (2005). Interactions between number and space in parietal cortex. Nature Reviews Neuroscience, 6(6), 435-450. https://doi.org/10.1038/nrn1684
  16. Jansen, P., Schmelter, A., Quaiser-Pohl, C., Neuburger, S., & Heil, M. (2013). Mental rotation performance in primary school age children: Are there gender differences in chronometric tests? Cognitive Development, 28(1), 51-62. https://doi.org/10.1016/j.cogdev.2012.08.005
  17. Joshi, D. R., & Rawal, M. (2021). Mathematics Teachers Standing on the Utilization of Digital Resources in Kathmandu, Nepal. Contemporary Mathematics and Science Education, 2(1), 45-67. https://doi.org/10.30935/conmaths/9679
  18. Koch, D. S. (2006). The effects of solid modeling and visualization on technical problem solving (Doctoral dissertation), Virginia Tech.
  19. Mailizar, M., & Fan, L. (2021). Secondary School Mathematics Teachers’ Instructional Practices in the Integration of Mathematics Analysis Software (MAS). International Electronic Journal of Mathematics Education, 16(1). 45-68. https://doi.org/10.29333/iejme/9293
  20. Mix, K. S., & Cheng, Y. L. (2012). The relation between space and math: Developmental and educational implications. In Advances in child development and behavior, 42, 197-243. https://doi.org/10.1016/B978-0-12-394388-0.00006-X
  21. National Council of Teachers of Mathematics (NCTM) (2000). Principles and standards for school mathematics. Sage.
  22. Nguyen, D. H., & Rebello, N. S. (2011). Students’ Difficulties with Multiple Representations in Introductory Mechanics. Online Submission, 8(5), 559-569.
  23. Osman, S., Che Yang, C.A., Salleh Abu, M., Ismail, N., Jambari, H., & Amantha Kumar, J. (2018). Enhancing Students’ Mathematical Problem-Solving Skills through Bar Model Visualisation Technique. International Electronic Journal of Mathematics Education, 3(3), 273-279. https://doi.org/10.12973/iejme/3919
  24. Park, J., & Brannon, E. M. (2013). Training the approximate number system improves math proficiency. Psychological science, 24(10), 2013-2019. https://doi.org/10.1177/0956797613482944
  25. Phillips, L. M., Norris, S. P., & Macnab, J. S. (2010). The Concept of Visualization. In Visualization in Mathematics, Reading and Science Education. Springer. https://doi.org/10.1007/978-90-481-8816-1_3
  26. Price, G. R., Mazzocco, M. M., & Ansari, D. (2013). Why mental arithmetic counts: brain activation during single digit arithmetic predicts high school math scores. Journal of Neuroscience, 33(1), 156-163. https://doi.org/10.1523/JNEUROSCI.2936-12.2013
  27. Rebello, N. S., Engelhardt, P. V., & Singh, C. (2012). 2011 Physics education research conference. In American Institute of Physics Conference Series, 1413-1419. https://doi.org/10.1063/1.3679980
  28. Shepard, R. N., & Feng, C. (1972). A chronometric study of mental paper folding. Cognitive psychology, 3(2), 228-243. https://doi.org/10.1016/0010-0285(72)90005-9
  29. Shepard, R. N., & Metzler, J. (1971). Mental rotation of three-dimensional objects. Science, 171(3972), 701-703. https://doi.org/10.1126/science.171.3972.701
  30. Šipuš, Ž. M., & Cizmešija, A. (2012). Spatial ability of students of mathematics education in Croatia evaluated by the Mental Cutting Test. Annal of Maths Information, 40, 203-216.
  31. Sorby, S., Casey, B., Veurink, N., & Dulaney, A. (2013). The role of spatial training in improving spatial and calculus performance in engineering students. Learning and Individual Differences, 26, 20-29. https://doi.org/10.1016/j.lindif.2013.03.010
  32. Stieff, M., & Uttal, D. (2015). How much can spatial training improve STEM achievement?. Educational Psychology Review, 27(4), 607-615. https://doi.org/10.1007/s10648-015-9304-8
  33. Tokpah, C. L. (2008). The effects of computer algebra systems on students’ achievement in mathematics (Doctoral dissertation), Kent State University.
  34. Törnkvist, S., Pettersson, K. A., & Tranströmer, G. (1993). Confusion by representation: On student’s comprehension of the electric field concept. American Journal of physics, 61(4), 335-338. https://doi.org/10.1119/1.17265
  35. Uttal, D. H., Meadow, N. G., Tipton, E., Hand, L. L., Alden, A. R., Warren, C., & Newcombe, N. S. (2013). The malleability of spatial skills: A meta-analysis of training studies. Psychological bulletin, 139(2), 352. https://doi.org/10.1037/a0028446
  36. Verdine, B. N., Golinkoff, R. M., Hirsh‐Pasek, K., Newcombe, N. S., Filipowicz, A. T., & Chang, A. (2014). Deconstructing building blocks: Preschoolers’ spatial assembly performance relates to early mathematical skills. Child development, 85(3), 1062-1076. https://doi.org/10.1111/cdev.12165
  37. Wai, J., Lubinski, D., & Benbow, C. P. (2009). Spatial ability for STEM domains: Aligning over 50 years of cumulative psychological knowledge solidifies its importance. Journal of Educational Psychology, 101(4), 817. https://doi.org/10.1037/a0016127
  38. Zazkis, R., Dubinsky, E., & Dautermann, J. (1996). Coordinating visual and analytic strategies: A study of students’ understanding of the group D 4. Journal for research in Mathematics Education, 27(4), 435-457. https://doi.org/10.2307/749876
  39. Zhang, X., Koponen, T., Räsänen, P., Aunola, K., Lerkkanen, M. K., & Nurmi, J. E. (2014). Linguistic and spatial skills predict early arithmetic development via counting sequence knowledge. Child Development, 85(3), 1091-1107. https://doi.org/10.1111/cdev.12173