LINK DOWNLOAD MIỄN PHÍ TÀI LIỆU "Tài liệu Factors Affecting Teaching and Learning in South African Public Schools pdf": http://123doc.vn/document/1049817-tai-lieu-factors-affecting-teaching-and-learning-in-south-african-public-schools-pdf.htm
analysis that follows points to the critical importance of viewing the prevalence of
HIV/AIDS among educators in relation to the factors that impact on teaching and
learning. Analysing the extent and severity of HIV/AIDS among educators without
looking at the overall teaching and learning environment in schools provides a partial
understanding of the immense educational challenges that the schooling sector faces.
The central argument that runs through this study is that the analysis of HIV/AIDS among
educators should be linked to the material conditions in schools, given the history of
differential educational provision where some sectors of the population (particularly black
people in rural areas) have been neglected (Graaf 1991).
The main objective of this study was to examine the material conditions in which the
sampled educators work in relation to the prevalence of HIV/AIDS among educators.
The following key questions were investigated:
• What are the typical characteristics of the schools in which the educators work?
• Is there variation between and within provinces?
• What possible interventions can be proposed for addressing the problems identified?
1
1. Introduction
Free download from www.hsrcpress.ac.za
The data upon which this report is based were derived mainly from educator and
institutional questionnaires, the latter completed by principals. The Education Labour
Relations Council study included an instrument on conditions in schools, such as total
number of learners and educators, average class size, formal contact hours with learners
(time on task), school fees, the quantity and quality of pass rates in Grade 12 (matric) and
a host of other factors – all aimed at giving a sense of the conditions in which educators
work.
The sample consisted of three types of institution: (a) primary schools (b) secondary/
high schools (c) combined/intermediate schools and (d) special schools. It comprised
11 463 primary school educators, 7 275 secondary/high school educators, 1 719 educators
from combined schools, and 31 educators from special schools. In total, 20 488 educators
were reached. The educators were drawn from a wide spectrum of learning areas:
• Languages;
• Arts and Culture;
• Economics and Management Science;
• Life Orientation;
• Mathematics;
• Natural Sciences; and
• Social Sciences.
This report adopts the following structure. Firstly, issues external to the classroom that
have a major impact on overall school performance, such as shortage of funds, are
discussed. Secondly, issues within the classroom environment, such as educator-learner
ratios or class sizes and formal contact hours (time on task), are analysed. Thirdly,
attention is paid to school performance as illustrated by matric results over a three-year
period (2001–2003). Finally, the data are located within the literature, and some
concluding remarks are offered.
In the analysis that follows, the three types of institution (primary, secondary and
combined schools) have been integrated, as in most cases disaggregation according to
school type did not produce significant differences. This is not to deny such differences
but rather to report on major areas cutting across school types.
2
2. Methodology
Free download from www.hsrcpress.ac.za
3.1 Factors outside the classroom
3.1.1 Resource base of schools by province
The data upon which this section is based were taken from the institutional
questionnaire, which was completed by principals. Figure 3.1 gives a profile of the
provinces’ average annual school fees. The results show that there are major variations in
the mean annual school fees, with the Free State charging the least and Gauteng charging
the most. The Western Cape and Northern Cape have higher average annual school fees
compared with the Eastern Cape, Kwa-Zulu Natal (KZN) and Mpumalanga.
What is interesting to note is that the three provinces with the highest annual school
fees have relatively low HIV/AIDS prevalence, less than 6 per cent, whereas the three
provinces with the lowest school fees have an HIV/AIDS prevalence of more than 13 per
cent, with KZN at 21.72 per cent. This interpretation is not to suggest a link between
school fees and HIV/AIDS status but rather to indicate that a serious educational
challenge exists if those schools with a high incidence of HIV/AIDS have poor financial
resources. Learners in such schools are doubly disadvantaged.
An analysis of average annual school fees by geographic location (urban-formal, urban-
informal and non-urban) and type of school (primary or secondary) revealed no
significant differences.
Average number of learners by province
An analysis of the average number of learners by province indicates no significant
increase in the three-year period for schools that supplied the relevant information.
Increases range from 1 per cent to 3 per cent. The province with the highest number of
learners per school is Gauteng. It is followed by Mpumalanga and KwaZulu-Natal. The
Free State and North West have fewer learners per school compared with Gauteng,
Mpumalanga and KwaZulu-Natal.
3
3. Key findings
900
800
700
600
500
400
300
200
100
0
Rand
WC EC NC FS KZN NW GT MP LP
Province
Figure 3.1: Mean annual school fees
Free download from www.hsrcpress.ac.za
Figure 3.2: School-learner enrolment by province
4
Factors affecting teaching and learning
900
800
700
600
500
400
300
200
100
0
Number of
learners
WC EC NC FS KZN NW GT MP LP
Province
2001
2002
2003
Table 3.1: Educator-school ratio by source of payment
Province Source Year
2001 2002 2003
Western Cape Government 17 17 16.9
School governing body 4 4.1 4
Eastern Cape Government 12.2 12.1 12.5
School governing body 4.8 4.1 4.1
Northern Cape Government 12.3 12.9 12.6
School governing body 5.3 4.9 5.3
Free State Government 9.1 9.1 9.3
School governing body 3 3 3.1
KwaZulu-Natal Government 12 11.9 11.9
School governing body 3 3 3.3
North West Government 12.1 12.1 12.1
School governing body 4.6 4.2 4.5
Gauteng Government 23.3 24.9 25.6
School governing body 5.7 6 5.9
Mpumalanga Government 13.2 13.4 13.8
School governing body 3.8 3.9 3.6
Limpopo Government 12.9 12.6 15.3
School governing body 4.4 4.7 4.6
Free download from www.hsrcpress.ac.za
Table 3.1 indicates that school governing bodies (SGBs) pay for about 5 per cent of
educators in all the provinces. The contribution of parents, in the form of creating
teaching posts paid for entirely with funds raised by the schools, needs to be
acknowledged, especially as it helps to ease the financial burden on the Department
of Education. This enables the department to direct money saved from the budget for
educators’ salaries to other areas of need within the education system.
3.2 Factors within the classroom
3.2.1 Class size (educator-learner ratio)
In this study, educators were asked about the average number of learners in the classes
they taught from 2001 to 2003. Figure 3.3 indicates that the province with the largest class
size is Limpopo. Almost 70 per cent of the sampled educators in Limpopo reported
teaching classes of about 46 learners. Mpumalanga (followed closely by the Eastern Cape)
is the province with the second-largest class size, with 60 per cent of the educators
indicating that they teach classes of about 46 learners. In contrast, a large percentage of
educators in the Northern Cape and Western Cape indicated that they teach classes of
fewer than 35 learners.
Class size by geographic location
The analysis of the data on class size was also done according to geographic location
to ascertain whether there are significant differences between the settlement types. It
emerged that 60 per cent of rural educators reported teaching classes with more than
46 learners. The figure for educators in urban informal settlements was almost the same
at 58.31 per cent.
The race factor
Given the history of apartheid education in which black people received the poorest
quality of education, it is important to investigate how the issue of race is being
addressed in the new dispensation. What progress is being made to narrow the huge
5
Key findings
Figure 3.3: Class sizes as reported by educators
Learners per class
0-35
36-45
46+
100
80
60
40
20
0
Percentage
educators
WC EC NC FS KZN NW GT MP LP
Province
Free download from www.hsrcpress.ac.za
racial disparities in education? The analysis contained in Figure 3.5 suggests that 58 per
cent of African educators are responsible for classes of about 46 learners. On the other
hand, a substantial number of white educators teach classes of about 21 learners. A
significant number of coloured educators (29 per cent) also teach large classes. The
majority of Asian educators (57.93 per cent) teach classes of about 36 to 45 learners.
Only 23.62 per cent of Asian educators teach classes of 46 learners or more.
Class size by district
Class size was further analysed according to districts in order to determine the degree of
variation between them. For the purpose of illustration, two districts (one urban and the
other rural) per province were selected. Table 3.2 illustrates the similarities and
differences.
Of all the provinces, the Western Cape had the least variation in terms of class size
among its districts. The City of Cape Town and Boland districts had a variation of less
6
Factors affecting teaching and learning
Figure 3.4: Class sizes by geographic location
Figure 3.5: Class sizes by race
Learners per class
0-35
36-45
46+
100
80
60
40
20
0
Percentage
educators
Urban formal Urban informal Non-urban
Area
Learners per class
0-35
36-45
46+
100
80
60
40
20
0
Percentage
educators
African White Coloured Asian
Race
Free download from www.hsrcpress.ac.za
7
Key findings
Table 3.2: Class sizes by district
District Class size
0–35 36–45 46+
Eastern Cape
Alfred Nzo DC44 (rural) 19.6 15.9 64.4
Chris Hani D13 (urban) 28.7 23 48
KwaZulu-Natal
Uthungulu DC28 (urban) 20.3 35.9 43.8
Umgungulu DC24 (rural) 26.4 34.3 39.3
Free State
Thabo Mafutsanyana DC19 (rural) 22.1 41.3 36.6
Lejwelepotswa DC18 (urban) 26.7 33.05 40.3
Limpopo
Sekhukhune CBDC3 (rural) 18.4 19.1 62.5
Capricorn DC35 (urban) 13.2 18.2 68.5
North West
Central Municipality DC38 (rural) 20.1 38.1 41.8
Kgaladi CBDC1 (urban) 29.8 35.2 35.1
Mpumalanga
East Vaal DC30 (urban) 21.5 25.9 52.7
Nkangala DC31 (rural) 16.2 29.1 54.7
Gauteng
Sedibeng DC42 (urban informal) 20 40.3 39.6
West Rand CBDC (urban formal) 23 43.1 33.9
Western Cape
City of Cape Town (urban formal) 31.3 42.9 25.9
Boland DC2 (urban informal) 29.1 43.6 27.3
Northern Cape
Namakwaland DC6 (urban formal) 48.2 37.8 13.9
Frances DC9 (urban informal) 28.7 54 17.3
Free download from www.hsrcpress.ac.za
than 2 per cent. In 2001 the City of Cape Town had 31.3, whereas Boland had 29.1.
Similarly, in 2002 the City of Cape Town had 42.9 compared with Boland at 43.6. The
Free State had a significant differential score between districts of about 4 per cent. Table
3.2 also indicates that the percentage gap between districts in the North West province is
significant. In 2001 the gap between Central Municipality (DC38) and Kgaladi (CBDC1)
was about 8 per cent, and in 2003 the difference was about 3.8 per cent.
3.2.2 Formal contact hours by province
Educators were asked ‘how many formal contact teaching hours per week’ they taught
(question 4.8 on the educators’ questionnaire). Formal contact hours denote the amount
of time educators spend on educational activities, specifically teaching and learning in the
classroom. This is often referred to as ‘time on task’. The ideal number of formal contact
hours remains at 25 per week but, as will be noted below, some educators in this study
reported having 35 formal contact teaching hours per week. The province that shows the
highest formal contact hours between learners and educators within the category of
25–35 hours is Limpopo with 76 per cent, followed by the Eastern Cape with 71.8 per
cent. Mpumalanga is the province with the third highest number of contact hours within
the category 25–35. The Western Cape, Northern Cape and KwaZulu-Natal have a lower
percentage of formal contact hours in the category of 25–35 hours a week. It is surprising
to find KwaZulu-Natal with low formal contact hours, an indication of adequate educator
supply, as in most cases it falls within the category of poor provinces, such as the Eastern
Cape and Limpopo, with a shortage of educators. This observation will be investigated
in a study planned for 2006/7. Looking at provinces with high a percentage of formal
contact hours, in the category of 36 and more, we find Gauteng (about 11 per cent), the
Eastern Cape (about 10 per cent) and Free State (about 10 per cent).
It is important to note that some educators reported formal contact of less than 25 hours,
which means that in relative terms they are doing little at school. This feature was notable
in KwaZulu-Natal. The Eastern Cape had fewer educators in this category.
An analysis of formal contact hours by geographic location indicates that a significant
number of educators in urban areas (about 13 per cent) fall within the 15–24 formal
Figure 3.6: Formal contact hours by province
8
Factors affecting teaching and learning
0-14
15-24
25-35
> 36
100
80
60
40
20
0
Percentage
WC EC NC FS KZN NW GT MP LP
Province
Free download from www.hsrcpress.ac.za
contact hours category. Most of the educators in urban informal and non-urban
settlements (about 70 per cent) have more formal contact time within the category of
25–35 hours. This indicates that educators in urban informal and non-urban areas have
more contact hours with learners than those in urban formal areas.
Figure 3.7 indicates differences in terms of educators who have formal contact of 36
hours and more. Instead of the general trend, in which urban formal areas have low
formal contact hours compared to urban informal and non-urban areas, the former now
have higher percentages than the latter. It should be noted, however, that in relative
terms urban informal and non-urban areas have a total average of more formal contact
hours than urban formal areas.
Analysing formal contact hours according to racial groups shows that about 4 per cent
of African educators have more than 36 formal contact hours a week. A significant
percentage of Indian/Asian educators (about 4 per cent) have less than 15 hours. On the
other hand, most African, white and coloured educators have formal contact hours within
the category 25–35, which falls within the national norm.
Figure 3.8: Formal contact hours by race
Figure 3.7: Formal contact hours by location of institution
9
Key findings
100
80
60
40
20
0
Percentage
Urban formal Urban informal Non-urban or rural
0-14
15-24
25-35
> 36
100
80
60
40
20
0
Percentage
educators
African White Coloured Indian/Asian
Race
0-14
15-24
25-35
> 36
Free download from www.hsrcpress.ac.za
One of the measures of school quality is the achievement scores of learners at a
particular exit point. Currently, the matric results provide an indication about the
performance of the education system at the secondary-school level (Umalusi 2004). There
are attempts to come up with national testing at Grade 3 and Grade 9 (DoE 2001, 2002).
This study investigated performance in matric for the three years, 2001–2003.
Figure 4.1 shows that the Northern Cape is one of the provinces that consistently has
been achieving higher percentage passes in the matric examination (about 91 per cent
throughout the three-year period). The second province that continued to get higher pass
rates was the Western Cape, with about 86 per cent during the three-year period. The
province reflecting the lowest pass rates over the three-year period was the Eastern Cape,
with a pass rate of around 55 per cent. Mpumalanga and the North West also obtained
low percentages. Thus, unsurprisingly, provinces with lesser financial resources are the
weakest performers in the matric examination.
The analysis of matric performance was also conducted in terms of the total number of
exemptions achieved in the provinces. Again, it is evident that the Northern Cape and the
Western Cape continue to obtain a significantly higher percentage of matric exemptions.
Table 4.1 indicates matric performance by districts. Two districts per province were
selected to demonstrate differences and similarities within and between the districts.
The selected districts are arranged in the order of largest to least difference.
The first two districts in Table 4.1, located in the Eastern Cape, demonstrate huge
differences, with a gap of around 30 per cent. It is worth noting that the differences have
been consistent through the three-year period, with a pass rate of 30.7 per cent in 2001,
41.8 per cent in 2002 and 41.6 per cent in 2003 for DC44, which is a rural district, and, in
contrast, the urban D13 district achieving pass rates of 70 per cent in 2001, 69 per cent in
2002 and 71 per cent in 2003. Of particular importance is that the two districts differ in
their geographic location, one being urban and the other rural.
10
4. School performance
Figure 4.1: Matric results by province
100
80
60
40
20
0
Matric
percentage
passes
WC EC NC FS KZN NW GT MP LP
Province
2001
2002
2003
Free download from www.hsrcpress.ac.za
Không có nhận xét nào:
Đăng nhận xét