“Education lies at the foundation of many issues in South Africa today” (Anderson et al., 2001).
These authors also state that it is not possible to investigate racial, social and economic issues without looking at the role of education. The current study indicates that such socio-economic issues do play a significant role in the way learners perform at school. In a discussion on spatial visualisation in engineering, Strong and Smith (2002) argue that the “ability to perform complex
107
mental manipulations of objects has been established as a predictor of success in several technology related disciplines”. Shalla and Schellenberg (2001) found that 50% of Canadians living in low-income households have low-levels of literacy compared to 8% of those with high-literacy levels. It is, therefore, often difficult to divorce the relationship between race, earnings and school performance.
Race and economic status appear to be directly related to school performance. Van der Berg (2002) reports that from literacy and numeracy tests conducted in 1993 with 12-18 year olds both black and white students performed badly; also while black students averaged 78% of white years of education, their literacy and numeracy scores were 55% and 47% of the white levels respectively. However, in academic terms 55% and 47% are significantly different as they can determine who could be admitted at tertiary school. It is for this reason that scholars such as Corley (2003) argues that race appears to be a persistent factor in employment statistics, educational attainment and the acquisition of literacy skills, with significantly higher
unemployment rates and lower educational attainment rates among Black and Hispanic Americans than among White Americans. However, Luckett (1995) contends that gender and race, socio-economics, access and curriculum are the major factors involved in poor
performance.
Schäfer (2003) working with secondary schools in the Eastern Cape found that rural, or township, schools performed poorly in spacial and visualisation constructs. Also, rural schools performed poorly in 3D problems and those characterized by special orientation constructs. In this study, it was found that most learners lacked skills to comprehend the combination of rectangles, circles and/or triangles. They also lacked the ability to visualise rearranged objects;
participants performed better in questions related to 3D than to 2D visualization; and that learners from UniZulu and the three secondary schools lacked the ability to visually transform 2D flat sheets into 3D objects. In every case participants from UKZN outperformed scholars from other groups in the tests associated with visualization.
Anderson et al. (2001) found that there are no gender gaps in South African schooling but that there is a racial gap that is influenced by school quality (i.e. economics). This was also
discovered by Osodo (1999) who found that White learners performed better than their Indian and Black counterparts did in visualisation skills and that Black and Indian learners were better in 3D than in 2D visualisation skills. Osodo (1999) also found that Black and Indian students improved their visualisation skills with increased exposure to visual objects and advances in their studies.
108
One of the most valuable sources of data on education is household survey data (Anderson et al., 2001). In this regard, Schäfer (2003) found that there was a high correlation between poor performance and poor socio-economic background. Such a relationship may explain the difference found in this study. Strong and Smith (2002) state that age, gender, individual differences, experience of sufficient length may improve performance. Thomas and Higbee (1996) argue that educators need to change their techniques in the classroom to incorporate a variety of methods which include those that stimulate visualisation skills. The Pearson
correlations between the visualisation test and those for logical (0.183), numerical (0.257) and communication skills (0.777) were similar and significant at the 0.01 level; while between visualisation and family income (Pearson Correlation -0.008) there was no statistical relationship. Therefore, it appears likely that visualisation skills may be related to prior experience and that educational practice should include many such activities to overcome this problem.
In a recent study in Ghana, Blunch (2002) found that cultural norms and background were important determinants of literacy and cognitive skills and that females were far less likely to be literate than males. This is supported by Amorim et al. (2004) who stated that for any training to be effective facilitators should be well aware of the backgrounds and contexts of the
participants, such as age, religion, geographic location, culture or personal experience, etc. The results attained in the present study confirm the assertions made by Blunch that the level of parents’ education also plays an important role in the development of children’s literacy and cognitive skills as the results showed that performance was higher for children from families with more educated parents. The results illustrate those learners who come from the higher income brackets perform better than those coming from families that are poor. The gap increases directly with the income gap. The study also shows that performance of learners is high among learners whose parents are educated. Anderson et al. (2001) state, “It is not clear what causal mechanisms drive this relationship”. However, these authors argue that such results may be due to the ability of parents to help their children with their schoolwork or it may be that such families live in better neighbourhoods with better amenities and schooling facilities which influence the children’s performance at school.
Results from this study found that for the section on numeracy and logic participants scored an average of 46%. Many, but not all, UKZN learners were able to answer most of these questions.
With respect to literacy (reading and writing) learners performed well in structuring sentences but many were weak in constructing simple present tense sentences. Most learners were not able
109
to tell if expressions were well written. Results from the reading comprehension also indicate that South African learners cannot read and understand short passages; especially learners are not able to make sense of meaning by inference. These results, especially those from university scholars, are difficult to explain fully. Literacy rates have increased by 10% over the past 10 years (EarthTrends Country Profile, http://earthtrends.wri.org). However, Pearson correlations between communication skills and visualisation (0.777) and between communication skills and logical (0.757) were quite high which meant that there was a strong positive linear relationship between these skills. The correlation between communication and numerical skills demonstrated a positive linear relationship of 0.509. For the mathematical proficiency test questions were written in English, which could lead to the argument that poor performance in this skill is a result of poor understanding of the English language (communication skills) used by most learners. However, factors other than language are also involved, as there is also a strong relationship between communication and visualisation, which did not require the use of English.
Spearman correlations between home language and visualization, logic, communication and numeracy were -0.339, -0.476, -0.492 and -0.448 respectively. English speakers also performed better than did isiZulu speakers. However, factors other than language might be involved as numerical and visualisation skills appear to be strongly related to home language.
The results also indicate that there may be a fundamental problem with the educational system as the system does not fully equip learners with basic skills. Learners go through 12 years of primary and high school education without learning visual, logic, numerical, reading, and writing skills. While the socio-economical status of participants may influence their
performance, it is necessary to seek ways to transform the educational practices. It is suggested that a more social constructivist approach to learning in primary and secondary schools might assist learners in promoting participation, hands-on approach and communication. In addition, the issues related to language competency need to be addressed as these might affect other skills.
110
Figure 6:25: Addition of quantified and demographic data to describe the Persona Outlining Model.
Newman and Lamming (1995) propose the use of fictitious user, or persona, in the development of software. However, Amory and Seagram (2003) argue that persona data are not built from real data (both quantitative and qualitative) and can, therefore, not be subjected to vigorous evaluations. The results obtained using an instrument designed to evaluate basic skills could also be used to describe the Personal Outlining Model in terms of real data (Fig. 25). Such a visualization clearly indicates the average skills (3D visualization and writings), and those that need particular attention (for example, mathematics).
Therefore, POM allows us, through research, to provide a persona definition that forms the basis to determine effects of the use of interactive and other learning resources. Results from this instrument have provided insights into the skills of many young South Africans. POM proved to be a useful theoretical basis for the design of the instrument and allowed us to quantify the characteristics of our intended audience. However, such qualitative data should only be used in conjunction with other qualitative and quantitative assessments as suggested by Reeves (2000).
Persona
Literacy Visual
Logic
Mathematical Communication
Reading Writing
Age Sex Education Occupation Properties
2D Visualization 3D Visualization 2D to 3D Conversion
16-21
57.7% male; 42.3% female Grade 12,
1st year University Students
111