* The 12 schools surveyed in this category represent the entire population of public secondary schools (10) and private secondary schools (2) in the Seychelles
In Botswana and Seychelles, the distribution of computer rooms is driven by a policy that follows a phased approach to implementation, starting with secondary schools, and this is reflected in the sample. In Namibia, there is a more or less even balance in the distribution of computer rooms between Primary and Secondary schools that is the outcome of an 'organic' growth in the number of schools with computer facilities. This is partly because the Namibian government, though supportive of ICT in schools, had not yet made the strategic decision that all schools in a particular phase or grade range should provide learners with access to ICT. Under these circumstances school-based and NGO-based initiatives to set up, facilitate and operate computer facilities in schools in that country are important. Circumstantial evidence suggests that there is significantly greater NGO activity in the field of ICT education in Namibia than in Botswana and Seychelles. The existence of ICT education-based NGOs across the school spectrum in Namibia largely explains why the number of primary and secondary schools in the sample for that country is almost equal in number – there being no policy directing ICT provision in any particular phase. In that country, NGOs and school communities acquire ICT for schools in terms of their own programmes and objectives and the cumulative impact does not appear to favour primary or secondary schools.
Reliance on NGO initiatives appears to be a fairly typical situation across a number of sub-Saharan African countries where, according to Ottwanger (2003: 29) the 'most successful in the implementation of ICT in practice are a few, often donor-funded projects'. A concomitant characteristic across many countries is that, even where they have developed ICT curricula and materials and provided teacher training in some way, 'most of the countries lack national umbrella organisations watching over a co-ordinated implementation' (Ottwanger, 2003: 29-30).
In the sample the numbers of schools in each phase (Primary, Junior Secondary and Senior Secondary) were not consistent across each country. There were very low numbers of accessible primary schools with computer rooms in Botswana and Seychelles, where the policy emphasis was on equipping junior and senior secondary schools. Only 11 (<20%) of the sample of 62 were primary schools. It was therefore decided not to separately analyse ICT costs for each school phase. The main focus of the analysis was therefore on costs per school in each country rather than on differences in costs per school phase.
Firstly, the overall pattern of ICT expenditure is described and related to MoE policy on ICT access in the three countries. Then the allocation of funds within each country's expenditure envelope is discussed to show differing allocations of value to elements of the ICT package found between the school systems. This is followed by discussion of the main findings.
Table 2 provides a summary of ICT costs based on the TCO approach. The Botswana and Seychelles MoEs set out to systematically provide access and support ICT in their secondary schools and were primary funders of human resource, training and technical support costs. Unfortunately, expenditure data in these categories was not available from the Botswana MoE.
* Annualised over 20 years
School Level Technology Costs
The distribution of school level technology costs (Table 3) by category, calculated as a percentage, reveals a broadly similar pattern between countries, where in each case, hardware, software and peripherals constituted the single biggest expenditure followed by recurrent expenditure, the computer room and lastly consumables. Within this pattern, the higher levels of expenditure on hardware software and peripherals in the Seychelles were on account of higher costs of supply and installation on the relatively isolated island archipelago. The lower costs of the computer rooms in Namibia were attributable to lower labour-construction costs. The high share of costs allocated to consumables in Botswana (printer cartridges, paper and stiffy disks) was based on supply of these items to schools at the beginning of the computer room building and equipment programme, which may have been adjusted over time. The 'retro-fitting' of electricity and other installations to existing classrooms, which is less costly than special rooms purpose-built for computers, was quite common in Namibia.
* Percentages may not add up to 100 on account of rounding
Influences on Total Costs of Ownership
Total annual total cost of ownership (TCO) per school can only be analysed for Namibia and Seychelles because this data was not available for Botswana (Table 2). It is striking that in US$ terms, expenditure on ICT in Namibia was less than half that of the Seychelles. Moreover, Seychelles expenditure on computers was higher for both (a) school level technology costs and (b) support costs which were supplied centrally from the MoE. This indicates the extent to which the Seychelles MoE has committed itself to ICT as an important element in its national curriculum strategy.
In contrast, the greater overall share of costs is borne at the school level in Namibia, which suggests that in that country government depends on school communities and NGOs to sustain computer activity at schools. The shape of expenditure in Namibia is consistent with a country that is in the process of developing policy but where the MoE does not yet have the budget to underwrite the expansion of computers into schools on a large scale. The Namibia education system consists of a highly dispersed population of 1545 schools, while the Seychelles MoE is responsible for 50 schools with a more dense population, so putting in place computer rooms in the latter country will be a more financially-onerous undertaking. The first step on this path will be for the MoE in Namibia to formulate and implement policy regarding national aims for computer rooms in schools, including norms and standards for such learning environments, and where such a roll-out should start. Until this happens, the distribution of schools with computer rooms in Namibia will be driven by local initiative and will reveal a variety of approaches to the challenge of providing ICT access to learners.
It is important to observe that in the case of both Botswana and the Seychelles, where investment in computer infrastructure in schools was driven by government, the average expenditure per school was almost double the expenditure in Namibia, where support for such expenditure came from NGOs and the community (Table 3). A key question is whether a model of ICT provision in which MoE and NGOs share funding and roll-out – as appears to be the case in Namibia – could provide more efficient access to computer rooms of equivalent quality than facilities in countries that are (almost) entirely driven by government funds.
This question becomes more complex when we compare the proportionate allocation of funds to all cost categories within a TCO framework (See Table 5). The most important difference between the two countries is that the proportional allocation of expenses in categories 8-10, all of which refer to human resources and planning, were much higher in the Seychelles – with a combined percentage of 33.25% – than in Namibia, with a combined percentage of 14.2%. Support costs were mostly absorbed by the individual schools in the case of Namibia and funded by the MoE in Seychelles.
* Computer costs are affected by the standards for provision set by a MoE (such as performance benchmarks set for computers, number of computers per computer room, etc.)
Could it be that the Seychelles approach – though more expensive – is more sustainable, given their emphasis on human resources costs to support the operations of the computer room?
These questions are raised deliberately because the cost data collected, though describing what current costs are, cannot assist in establishing which allocation patterns are more efficient or produces better quality of service than others. For example, the 9.8% allocation to school-based technology management in Namibia may seem to be cost efficient in comparison to the Seychelles value of 26.3%. But a lower allocation to technology management may buy less qualified user support, or user support that is not available on call, leading to extended down-time of the school computer network. This wasted time erodes the value of all investment inputs into the installation. More research needs to be done on hidden costs and opportunity costs of ICT investment in order to make the TCO model more sophisticated so that the impact of different expenditure patterns on efficiency and quality of ICT access can be understood.
Cost Indicators and International Comparisons
A simple and useful cost indicator is derived by calculating the cost of computers per learner or per computer. In Seychelles schools, the expenditure on computers was three times that of Namibian expenditure per learner and four times that of Namibian expenditure per computer (Table 6).
FURTHER DEVELOPMENT OF COSTING MODELS FOR AFRICAN COUNTRIES
The cost analysis applied in this study was deliberately restricted to a simple set of parameters that afforded relatively easy data access and which could be replicated. Consequently, there were relevant cost elements and influences on costs that were not incorporated, but which deserve noting in the interest of developing a more nuanced cost analysis.
In the first instance there is the matter of whether to use refurbished (or reconditioned) computers rather than new computers. There are conflicting views on whether refurbished PCs are a more viable technology option than buying new computers in terms of cost-effectiveness (See: Open Research, 2004; InfoDev, 2005: 3). For example, one argument is that opting for refurbished computers may reduce costs of acquisition of the hardware, but that the overall costs of maintaining older machines will outweigh the initial cost saving. A decision on this matter could appreciably affect ICT costs over a lengthy period of time.
Second, there is the option of 'cost recovery' in respect of ICT fees that could be levied at school level, in order to subvent MoE expenditure. But there are also equity, administrative, legal-regulatory and cultural aspects that must be satisfactorily addressed (InfoDev, 2005: 3).
Third, the dominant language of software and of the Internet is English. In linguistically diverse African countries, MoEs will come under pressure to support the development of content, materials and software that can add considerably to government's financial burden (See: Gyamfi, 2005; Dalvit et al., 2005).
Fourth, costs of hardware and software are dictated largely by technology cycles where each generation becomes progressively cheaper to purchase as it is superseded by new models/versions with higher performance. The MoE can control expenditure through defining the economic life-cycle – or the useful financial life – of an item, and also through timing of its purchases to maximise the efficiency of its systems.
All of the above aspects have the potential to impact significantly on ICT costs, but are difficult to introduce into a model that must be used for comparative purposes.
This article observes that in developing countries that have to deal with constrained budgets, financial allocations to ICT must properly take into account the full costs of sustainable ICT systems. However, there is a dearth of information about ICT costs that can assist MoE decision makers to apportion their budgets between competing demands between the four 'T's' - teachers, textbooks, time and technology.
This is because a body of work that upholds systematic study of ICT costs in African schools has not yet emerged. The task of generating a coherent understanding of ICT costs through research is complex because investment in ICT in African schools is mainly dispersed in resource centres or in small networks of pilot schools (Ottwanger, 2003: 29) which operate under different conditions with widely varying technology configurations. A shared approach to collecting data on ICT costs is essential to raise the comparability of research studies.
The analysis of cost data suggests that very few countries in sub-Saharan Africa will be able to contemplate the aggressive implementation of computer rooms in all high schools, as was achieved in Botswana and the Seychelles. Most will find themselves in a situation analogous to Namibia's. In that country, government depends on school communities and NGOs to sustain computer activity at schools. A key question is whether such a model of ICT provision in which MoE, NGOs and communities share the financial burden can be as sustainable, equitable and provide equivalent quality of access as facilities in other countries that are (almost) entirely driven by government funds.
Linda Chisholm was instrumental in securing the opportunity to do the project on which this paper is based. Within the broader project, this work benefited from discussions with Linda Chisholm and Rubby Dhunpath. Also, sincere thanks are due to the in-country researchers for their contribution and collegial support. Thanks to Anneke Jordaan for doing the SPSS data management. Responsibility for the opinions, interpretations and analysis in this paper remains my own.
1 The potential benefits of using of ICTs in educational administration at the school level (School Management Systems) and at the systemic level (Education Management Information Systems) are noted but not addressed in this paper.
2 There is a body of publications that deal specifically with the financing of distance education – in higher education and adult education – that will not be addressed here (eg: Butcher, 2003).
3 The following are examples of TCO-based tools giving the sponsoring organisation acronym and URL: IAET at AEL <http://126.96.36.199/tcov2/bkgnd.cfm> (Date accessed: 31 January 2006); BECTA at <http://schools.becta.org.uk/index.php?section=pr&catcode=ss_to_pr_su_03&rid=9650> (Date accessed: 31 January 2006); ISTE at <http://tsi.iste.org> (Date accessed: 31 January 2006); CoSN & Gartner at <http://classroomtco.cosn.org/gartner_intro.html> (Date accessed: 16 September 2003).
4 A detailed account of how these values were calculated is available in Paterson (2004).
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