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

Anass Bayaga,Michael J Bossé,Godfred Amevor

doi:10.30935/cedtech/11235

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.

Highlights

While spatial-visualisation (SV) skills are only one facet of a person’s overall intelligence, research suggests that spatial reasoning is an important predictor of achievement in science, technology, engineering, and mathematics disciplines (STEM) (Stieff & Uttal 2015; Uttal et al, 2013; Wai et al, 2009)
The format for each item on the Purdue spatial-visualization test/rotations (PSVT/R) is that an object is pictured in one position; it is paired with another image which is reoriented to a different position
The results obtained from Purdue spatial-visualisation/rotation test revealed that the experimental group developed spatial-visualisation skills while learning vector calculus with MATLAB

Summary

Introduction

While spatial-visualisation (SV) skills are only one facet of a person’s overall intelligence, research suggests that spatial reasoning is an important predictor of achievement in science, technology, engineering, and mathematics disciplines (STEM) (Stieff & Uttal 2015; Uttal et al, 2013; Wai et al, 2009). While similar conclusion has been suggested by Mailizar and Fann (2021), Ćurčić et al (2018) revealed that the integration of software has a link with students’ knowledge and increase the interest in studying content, and the teaching These interconnected notions have provided researchers rich opportunities to investigate student understanding and SV skills through the integration of dynamic software (e.g., GeoGebra, Maple, MATLAB, CAS and other computer environments) in various learning disciplines. While ongoing research points to pervasive usage of dynamic software environments in urban settings, little to no evidence, this far exist in the context of rural settings with the connections that link SV skills, mental rotations, and learning through the dynamic visual tool MATLAB. In rural -settings, little to no evidence exist to unpack a) digital technology and learning, b) mental rotation skills as a measure of spatial reasoning skill, c) spatial reasoning and cognitive reasoning, and d) vector calculus

Methods

Findings

Discussion

Conclusion