6.5. Analysis of Literacy and Communication Proficiency
6.5.1. Literacy
6.5.1.1. Literacy versus Overall Performance
In order to ascertain the importance of functional literacy on academic performance an investigation was carried out to find out if there is a correlation between this and overall performance (Fig. 6.10). The majority of learners did not perform well in the literacy test with only UKZN scoring above 50% on both literacy (73.8%) and overall performance (76.8%).
Tholokuhle learners had the lowest literacy mean of 40.5% and overall performance of 42.5%.
Buhlebemfundo students scored 42.4% on literacy to 43.1% on overall performance; while those from UniZulu had 44.6% for literacy and 46.5% on the overall performance. Qhakaza participants had a mean literacy score of 45.3% and overall performance of 51.6%.
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Literacy vs. Performance
20.0 40.0 60.0 80.0 100.0
UND Unizulu Buhlebemfundo Qhakaza Tholokuhle
Schools
Marks in Percent
Literacy Overall Performance
Figure 6.10: Relationship between literacy and overall performance per school.
Overall Performance
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60 40
20 0
LITERACY
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Figure 6.11: Relationship between literacy and overall performance
The Pearson correlation was used to determine whether there was any linear relation between the two skills. The low significance value of p < 0.0001 (Correlation is significant at the p <
0.05 level, 2-tailed) indicates that the variables are significantly different, however, the high correlation coefficient of 0.909 means that the two are directly positively related. The Scatter Plot (Fig. 6.11) shows the existence of a linear relationship between literacy and the overall means. As the literacy skills improve so is the general performance which means that students with high literacy skills perform better than those with low literacy proficiency.
94 6.5.1.2. Literacy versus Communication
There was a need to ascertained the correlation between literacy and communication skills as most students who took the test came from backgrounds where English was not just their second language but third or forth language. The Pearson correlation (SPSS) was used to determine any linear relation between the two skills. The low significance value of p < 0.0001 indicates that the variables are significantly different. However, the high correlation coefficient of 0.926 suggests that the two are directly positively related, therefore, as literacy is strongly related to communication skill (Fig. 6.12). This indicates that proficiency when learners develop proficiency in literacy their communication skills also improve and vice versa. This is the reason learners from the community schools are poor on both skills while UKZN learners demonstrated high competence levels on both skills.
LITERACY
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80 70
60 50
40 30
20
COMMUNICATION
100
80
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Figure 6.12: Relationship between literacy and communication
6.5.1.3. Literacy and Gender
An investigation was undertaken to determine whether gender has any significant role in the literacy performance. The results indicate that literacy performance by gender is skewed towards males who attained a mean score of 46.4% while their female counterparts had 44.8%.
The UKZN males had the highest literacy score of 74.6% followed by UKZN females with 71.2%. Tholokuhle females had the lowest mean score of 39.8%, followed by Qhakaza males
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with 41%. UniZulu females had literacy mean of 41.2%, UniZulu males 45.2%, Buhlebemfundo females 42.6%, Buhlebemfundo males 42.3%, Qhakaza females 48.5%).
In order to determine the correlation between the performances of the different gender groups the Independent Samples t-test, which compares the two group means, was used and equal variances for both groups were assumed because of the low significant value of p < 0.004 on Levene’s Test. Because of the high significance value (p= 0.1449) and the confidence interval (-7.16377 and 1.060968) the two groups were not considered any different. Therefore, regarding the performances on literacy by the two sexes, the t-test shows that they are not statistically different (Fig. 6.13).
GENDER
Male Female
LITERACY
100
80
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0
Figure 6.13: Representation of performance on literacy skills by gender.
Three aspects of literacy were examined in this study and these are visualisation, numeracy and logical skills as discussed below.
6.5.1.4. Visualisation Skills
The Pearson correlations results between visualisation and logical skills was (0.270) and visualisation and numerical (0.257), and visualisation and communication (0.287) illustrating that there exist a weak linear correlation between these variables. The low significance levels between visualisation skills and these skills (all with p < 0.001) indicated that all groups were significantly different.
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The role of gender in articulating the visual world was assessed and females obtained 53.3%
outperformed their male counterpart who scored 50.1% (Fig. 6.14). The results were subjected to the Independent Samples t-test, which compared the two means was carried out and the unequal variance was assumed because of the high Levene’s test. The high significant value for the t-tests (p = 0.271) and the confidence interval which contains zero (-0.737 and 2.608) meant that performance for the two groups was not significantly different.
GENDER
Male Female
VISUALISATION
120
100
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40
20
0
Figure 6.14: Representation of performance on visual skills by gender.
Regarding the three different components of visualisation (2D, 3D and 2D – 3D) more analyses were undertaken to investigate the relationship of gender and these skills. The overall means for 2D skills were 42% for females and 41.7% for males. Regarding the 3D skills, the overall mean was 59.5% for females and 64.2% males. When 2D and 3D tests were subjected to the
Independent Sample t-test, the unequal variances for both means were assumed because of the high significant value on the Levene’s test of p = 0.293 for 2D skills and p= 0.624 for 3D skills tests. The high significant values for the t-tests of p= 0.943 for the 2D and p= 0.173 for the 3D coupled with the presence of zeros in the confidence intervals for 2D (-0.627 and 0.674) and 3D (-1.592 and 0.288) demonstrated that there was no significant difference between performances of the two genders. These results confirm assertions made by Burin et al. (2000) that research on “visualisation factor” had not shown any differences between the genders or at times “the difference is small”. Burin et al. (2000) explain that there are many ways of solving 2D problems and men and women may differ in their approaches but would still arrive at the same result.
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Osodo (1999) found that male students were more ably to mentally rotate 3D objects than their female peers. These results are also supported by Burin et al. (2000) who agreed with the established norms that the skills to rotate 3D objects favours males. However, the results for 3D rotation in this study indicate that performance between these genders is not significant different (t-tests show negligible difference between the 3D skills means of 4.78 for males and 4.76 for females).
Regarding changing the flat 2D objects to 3D objects (2D - 3D) the t-tests results show different means for both genders (females 3.63 and males 4.21). Only university students passed in this section (UKZN: Females 57.1% and males 80% and UniZulu: Males 50% and females 50%).
No school students passed as they all obtained means below 50% (Qhakaza females 23.5%, Qhakaza males 27.4%, Tholokuhle females 36.1%, Tholokuhle males 42.4%, Buhlebemfundo females 25.5%, and Buhlebemfundo males 44.2%) (Fig. 6.15).
GENDER
Male Female
Means in Percent
120
100
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20
0
2-D 3-D 2D - 3D
Figure 6.15: The Box plot showing the means on visual skills for both gender.
The Independent Sample t-test gave a low significance value (p = 0.0361) in the Levene’s test, leading to the assumption that the two means for both sexes were equal. However, the t-test gave a low significance value of p = 0.039 and the absence of zero in the confidence interval (- 1.144 and -.018) illustrated that there was a significant difference between the two genders
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regarding their ability to transform 2D planes to 3D objects with males more mentally able to transform 2D objects into 3D objects than females.
6.5.1.5. Logical Skills
The correlations between the logical skills and other skills are varied as some showed strong and others weak linear relationship. According to the Pearson correlation test the relationship between logical and numerical skills is weak (0.556), the logic and communication relationship demonstrated a strong correlation (0.757) and the correlation between logical and visualisation skills (0.183) is not significant. The significance levels between logical skills and the other skills (numerical and communication) was p < 0.001 and between logical and visual skills p = 0.012. These low significant values illustrate that the groups were significantly different.
Therefore, there is a strong positive correlation between logical and communication literacy while logical and numerical skills give a weak correlation. However, there is an insignificant relationship between logical skills and visualisation skills (Fig. 6.16).
Normal Q-Q Plot of Logical Skills
Observed Value
120 100
80 60
40 20
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Expected Normal Value
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Figure 6.16: Illustrate the normal distribution of learners’ logical skills.
6.5.1.6. Numerical Skills
The Pearson correlations between numerical skills and other skills are relatively weak for numerical and logical skills (0.556) and for numerical and communication (0.509). The correlations are linear and positive but are very weak between the numerical skills and logical
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and communication skills. Again the low significance levels of p < 0.001 between the numerical skills and other skills indicate that the skills are significantly different. The exception was with the relationship between numerical and visualisation skills at p = 0.257 and with the significant value of p < 0.001. This demonstrated that there is no significant association between the two variables (Fig. 6.17).
Normal Q-Q Plot of Numerical Skills
Observed Value
120 100
80 60
40 20
0 -20
Expected Normal Value
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-20
Figure 6.17: Normal Q-Q graph showing the distribution of learners’ numerical skills.