CAPÍTULO II. Metodología para el análisis cuantitativo de riesgo de accidentes mayores probables en la
3.20. Representación gráfica de los perfiles de riesgo
From the 90 participating learners, all participants were English second language
speakers with other South African languages as their home languages, Setswana
was however the dominative home language. The predominance of Setswana was
also mainly influenced by the fact that the region where the research was conducted
was dominated by first language Setswana speakers.
In the four participating schools when asked about their gender (as reflected in
appendix A question 3), there was a higher sample size of males in comparison with
females with 51% being males while 49% were females. The difference in the
gender participants grouping was found to be marginal and could not have a
significant influence on the results.
When asked if learners had a problem of understanding content because of the
language it was presented through (as reflected in appendix A question 10), a total
of 47% said they had a problem while 53% of learners answered that they had no
problem in understanding the learning content as a result of the language, this is
illustrated in the graph presented in figure 4.3. When analysing the data further
through gender, 53% of female learners stated that they had a problem in
understanding content as a result of the language in which it was presented
through, and the remaining female learners reflecting that they were never
challenged by understanding content as a result of the language. In comparison
with male learners, only 48% of them stated that they were having trouble
understanding content as a result of the language. Even though the differences
were marginal, more females expressed that they experienced challenges in
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Figure 4. 3 The total percentage of participants who had problems in understanding learning content as a result of the language it was presented in
The next question aimed to establish how many learners would code-switch while
learning (which is reflected in appendix A question 13). The nature of code-
switching signified their challenge of communication and learning through the
language of instruction without having to use their home languages in the process.
The learners were thus asked if they ever code-switched during lessons. The results
illustrated in figure 4.4 below reflected that a total of 62% of the participants code-
switched during lessons while 38% of learners reflected that they had never code-
switched while learning during lessons. When analysing the data further, 54% of all
participating females code-switched while learning in comparison with 45% of males
who code-switched. Once again more female learners had more challenges of
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Figure 4. 4 The total percentage of participants who code-switched while learning during lessons.
Schools grouping analysis
The data was also further grouped into schools to analyse the responses based on
the school location. Referring back to the question: do you have difficulties in understanding learning content through the language of instruction, the data also revealed that there was a difference in the responses to the question when
analysing the responses per school. Illustrated in figure 4.5, the rural based schools
(schools C and D) had the highest percentage of learners answering yes to having
this problem with 67% of learners, followed by the township based school (school B)
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Figure 4. 5 The individual results in percentages per school, of learners who had a problem understanding content as a result of language
As mentioned earlier in section 4.7 when defining the hypothesis to be tested,
during the interview process learners from the school A were fluent in English in
comparison with learners from schools C and D. The interviewed participating
learners also revealed that school A learners often have more training and good
English language foundation classes in the English language and thus could
communicate well in comparison with their cohorts. When asked if learners code-
switched while learning in lessons, 81% of learners in rural schools (school C and
D) stated that they do code-switch, compared to only 63% of learners in school A as
illustrated in figure 4.6.
42%
60 %
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Figure 4. 6 The individual results in percentages per school, of learners who code- switched while learning during lessons
In figure 4.6 we note that in the township school (school B) there was a very low
percentage of learners who code-switch while learning during lessons. This was
influenced by the fact that their teacher was not a South African and the learners
could not code-switch as the teacher could only teach in the English language.
When evaluating the responses of learners with regards to having a problem in
understanding content due to language and acknowledging to code-switching, there
was a rise in the participants who admitted to code-switching in comparison to the
learners who admitted to having problems learning using only one language when
looking at figure 4.7.
81 % 63 %
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Figure 4. 7 The percentages of learners who had problem understanding language as a result of the language it was presented and learners who code-switch per school
Figure 4.7 results report a high rise of learners acknowledging that they code-switch
in all schools when compared to the responses of learners who acknowledge having
a problem of language when learning. The learners in the township based school
(school B) were not code-switching in class as previously stated, their teacher could
not communicate in any of the native South African languages
In analysing these data further, learners from school B who are based in a township,
will be excluded reducing the sample size to 65 as they did not code-switch during
lessons. Using a cross tabulation between the different groups of participating
schools, a Pearson’s test of correlation was used on the data to further establish if there was any association with the learners geographic backgrounds and their
code-switching behaviour. A hypothesis was thus established to test any
association between the two.
60% 20% 60 % 81% 42% 63%
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H1 - The school location of the learner can be associated with their code-switching
nature
H0 - The school location of the learner cannot be associated with their code-
switching nature Correlations Codeswitch Schoollocation Codeswitch Pearson Correlation 1 .418** Sig. (1-tailed) .000 N 65 65 Schoollocation Pearson Correlation .418** 1 Sig. (1-tailed) .000 N 65 65
**. Correlation is significant at the 0.01 level (1-tailed).
Table 4. 2 A Pearson’s Correlation test between the learner’s school location and their code-switching behaviour
Using SPSS In table 4.2, we found that the correlation coefficient is 0.418 which
signifies an existing correlation between the two variables (the learners code-
switching behaviour and their school location) with the p value greater than 0.0001.
The null hypothesis was rejected for this test. We can conclude that there is an
association between the learner’s school location and their code-switching behaviour, however these results do not assume causation of the other i.e. Learners
from location A will code-switch. A larger population of schools would need to be
sampled to establish the relationship degree further.
Finance
Even though all participating schools were public/government schools, an average
learner in school A was expected to make an annual payment of R3500 of school
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would be expected to pay around R350 which is an equivalent to an estimated £25.
Schools such as school A would supplement their education with subjects and
resources that are not available in other schools in order to give learners an
enhanced learning environment. The learners who attended school A could also
mostly afford supplementary learning materials that supported their learning
process. Most of these learners live in the surrounding urban area with families
having high income rates. The further away the school was from the city the lower
the income bracket of families. Justifiably, schools in low income areas often
produce low results especially in subject areas such as mathematics and science as
they also lack resources and teachers adequately trained to facilitate these
subjects. Spending extra money to purchase learning material or access to online
material can be a challenge for learners in schools B, C and D as most of their
parents can only afford to cater for their basic needs.
During an interview with Mrs Legae from school D, she reflected that engaging
parents in activities that require additional costs often discourage parents from
actively participating in the school. The schools keep additional cost requirements to
a minimal and have to look for other means to supplement their needs. She pointed
out an example of a recent career fair where learners could not afford to attend as it
was held in a city 40 km away from their school. The school had to raise funds
through asking from donations and in other ways to enable learners to attend this
trip (Jantjies and Joy, 2012).
Technology
When evaluating the technology resources the participating schools had disparities
in terms of technology resources with only 22% of learners stating that they had
access to computers in school when asked if they had computers in schools (as
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more than half of the participating sample of learners stated that they were at times
given tasks that required the use of technology such as either typing their homework
or searching the Internet as part of their homework (Jantjies and Joy, 2012).
When asked if the learners owned a mobile phone (in appendix A, question 17 and
27) total percentage of 48% of learners however owned a mobile phone, and all
learners had access to a mobile phone. From the mobile phones owned, most of
them were on a pre-paid plan, as most South African learners cannot afford contract
plans (Foko, 2009). This also reflects that most participating learners would have to
purchase airtime in order to make a call or use their mobile Internet. Currently South
Africa has one of the highest Internet rates in Africa making it expensive for learners
to access the Internet through their own funding. However mobile phone Internet
signals have improved significantly, with villages and remote areas in the country
having better access to mobile Internet. The mobile Internet prices have also proved
to be considerably affordable as less data is presented on mobile phones as
opposed to traditional desktops.
When analysing the phone ownership data further, more females owned phones in
comparison with male participants with females at 53% and male learners at 47%.
The following hypothesis emerged to test the data further:
H1: There is a relationship between mobile phone ownership and the gender.
H0: There is no relationship between mobile phone ownership and gender.
Using the chi-square test of association the above hypothesis was tested with the
aim of establishing if there was any association between mobile phone ownership
102 Chi-Square Tests
Value Df Asymp. Sig. (2- sided) Exact Sig. (2- sided) Exact Sig. (1- sided) Pearson Chi-Square 1.304a 1 .253 Continuity Correctionb .706 1 .401 Likelihood Ratio 1.320 1 .251
Fisher's Exact Test .367 .201
Linear-by-Linear Association
1.284 1 .257
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 6.89. b. Computed only for a 2x2 table
Table 4. 3 A chi-square test between the learner’s gender and the mobile phone ownership rate
The SPSS results in table 4.3 reflected that the value of p was less than 0.05 which
signifies a significant relationship between the two variables which are the learner’s gender and their mobile phone ownership. We can thus say that there was a
relationship between the learners’ gender and their mobile phone ownership
Despite the low percentage of ownership amongst all participants, all learners either
owned or had access to a mobile phone with most of these phones having WAP
facilities. This reflects the increased market demand for mobile phones which
decreases the cost at which we acquire mobile phones with useful features such as
WAP. This also bypasses the issue of access to a technology learning device
making mobile phones the primary potential method to bridge the learning material
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In terms of mobile educational experiences (based on appendix A questions 20 and
21) only 54% of learners had previous experiences of playing mobile games or
using mobile applications with only 38% of these systems being related to their
current school content.