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The Quest for the Digitisation of Education in Developing Countries: Are we Forgetting Teachers?

The development of every country partly depends on how strong and reliable its educational system is to produce the best minds to innovate and bring new solutions to that country’s challenges. Often, this task automatically falls on teachers who generally get the blame for all failures of the education system but rarely get the praise for its successes. In this modern the era of technology where students’ ICT literacy has come to the fore because of its potential for economic development, teachers are in the firing line of anyone who thinks the education system is not doing enough to prepare learners for the technology skills they need for surviving in today’s digital world.

With the SDGs showing that education will be the key to many of today’s world ills, it is probably high time we stopped and considered exactly what that education will look like in deprived contexts, should we continue to do business as usual. Yes, we want education for all and all that. We certainly want quality education and of course in the 21st century, quality education must respond to the needs of the next generation. In a world permeated with technology though, quality education is increasingly necessitating the use of technology for teaching and learning enhancement. As much as everyone wants children in developing countries to receive education that will enable them to harness the power of technology as the developing world tries to catch up with the rest, learning with technology remains a dream in many developing countries.

All we need is access to ICTs, right?

In developing countries, access to ICTs in schools is limited and costly in the rare instances where it is provided. The expectation is therefore that as soon as technologies—mainly computers—are available in schools, teachers would unreservedly make great use of them and just like a magic wand, improve students learning and overall ICT literacy, all of which are to contribute to the development of the concerned countries. Such has been the thinking behind many developing countries’ investments in the One Laptop Per Child project and similar projects. This technocentrism that has been decried for a while now [1] is yet to bear fruit despite many developing countries still constantly biting the same bait in hope of … well, a different outcome? After all, the $100 One Laptop per Child is no longer seen as the laptop that will save the world as the New York Times once claimed.

A key flaw of the technocentric view of ICT in education is that when the expected outcomes are not obtained, nothing else could be responsible but the teachers. Given the cost of these technologies to the otherwise deprived developing countries, the thought of teachers not making use of them is often intolerable. Why in the world would they not elect to use such equipment that cost so much to get? Are they not aware that some financial sacrifices were made to bring those devices to the schools? Have they forgotten that the country is betting its development on students’ skills to use those technologies creatively? So, teachers are always seen as potentially problematic in efforts to digitise the education sector. This negative image of teachers has not been helped by claims that teachers are a class of less technologically savvy digital immigrants who can hardly use ICTs to the liking of their supposedly technology-savvy, digital native students [2].

But do we really know the teachers?

If as it is now generally assumed, technology literacy skills of the next generation of learners are the responsibility of teachers [3], then understanding who we are entrusting that task with should be a priority. We are expecting great works from teachers in the building of our digital economies and that should mean we know better who they are and how they become the people we give such big responsibilities. Masterpieces don’t paint themselves, neither are they a product of a brush nonchalantly placed in the clumsy hands of an amateur. So, ICT in education ought not to be summed up with handing computers or laptops to teachers and schools before sitting back and awaiting miracles to happen. They surely won’t. The need for a generation of skilled men and women fully equipped with the ‘21st century skills’ of which ICT skills are central should be considered second to the understanding of the men and women who are going to make that ICT literate generation a reality: the technology-using teachers. There is a need to know what teachers are really willing and capable of doing with ICTs before counting unhatched chickens of economic transformations that we will get from ICTs, especially in the developing world.

Let’s take an example of Rwanda where I am currently doing a study on the development of identities as technology users of pre-service teachers. (You can read more about the study in this blog post). Rwanda is a country that has gained international acclaim for its efforts to digitise itself and its ICT-friendly policies. Without any other substantial resources, ICTs have been put at the centre of its economic ambition, and this privileged position has had them dubbed ‘the heart of the education sector’ [4]. As a result, investments in the acquisition of laptops for schools have been ongoing since 2007. Nevertheless, once in schools, these technologies have not been used as initially hoped. In fact, recently the Ministry of Education found itself left with no choice but to instruct school leaders to ensure their teachers are using the resources available or risk losing their jobs, after it was revealed that many of these devices had remained in their original boxes while others just disappeared. The easy question here is why are the teachers not using these resources in the first place? Why would they wait for their head-teachers to lose their jobs before they start using ICT resources given to their schools, for free? But an even better question would be ‘Who are those teachers, who are failing to use technologies given to them for free?’ How did they come to be who they are? What has made them to be the technophobes they are portrayed to be? These are some of the considerations that are the genesis of my ongoing PhD study.

My research wants to understand who the teachers expected to use the increasing number of technologies in schools are, by approaching the problem from a socio-cultural point of view. Teachers’ ICT training and usage don’t happen in a vacuum. So, it is important to understand how the different contexts they grow up in (socially and professionally) shape who they are in relation to ICTs and therefore influence the likelihood of them using and negotiating the use of any technologies in any way with their students.

In this study I am following pre-service teachers during a year-long internship to understand how ICT policies and training programmes translate into actual technology using teacher identities. This means understanding the key influences that teachers-in-training have and the extent of these influences on the end product that ends up in schools. This understanding can already help predict what route will lead to teachers who are most likely to use the technologies that are available to them in the context.

Given the many factors that come into play regarding ICT use in education, I attempt to follow the training line to understand first, what ICT roles teachers are trained for and expected to play in policies and teacher education programmes respectively. Then I look at the support and the influence that come from those directly in charge of student-teachers development process (teacher educators and school-based mentors). How do they guide them in the use of ICTs? Does their practice encourage or discourage the development of ICT skills for these candidates who want to become teachers in a country that counts on technology to achieve its development goals? How does the existing ICT environment allow the teacher-trainees to cultivate and exercise their agency as professionals trying to achieve learning objectives with(out) the support of ICTs?

Answers to these questions will certainly not solve all the challenges related to ICTs for development especially as seen from an education perspective. However, they will give a picture of who the teachers are and what needs to be done to get them using already available technologies in Rwandan schools and also schools in similar contexts.

References
[1] S. Papert, ‘A Critique of Technocentrism in Thinking About the School of the Future’, in Children in the Information Age, Elsevier BV, 1988, pp. 3–18.
[2] M. Prensky, ‘Digital Natives, Digital Immigrants’, On the Horizon, vol. 9, no. 5. pp. 1–6, 2001.
[3] D. Epstein, E. C. Nisbet, and T. Gillespie, ‘Who’s Responsible for the Digital Divide? Public Perceptions and Policy Implications’, The Information Society, vol. 27, no. 2, pp. 92–104, Feb. 2011.
[4] Ministry of Education, ‘Education Sector Policy’, Kigali, 2003.

Why M-Pesa Outperforms Other Developing Country Mobile Money Schemes

24 November 2012 17 comments

Why has M-Pesa been so successful in Kenya, yet mobile money initiatives in other developing countries much less so?  Recent Centre for Development Informatics research[1] can help provide a systematic response.

M-money services have two core functionalities.  Registered customers can convert between e-cash and real cash (typically at the physical premises of an m-money agent), and can transfer e-cash from their account to that of another account holder via SMS. They might use this to send money to family members or friends, or to pay a provider – anyone from a taxi driver to a local school – for goods and services.

M-Pesa was launched in Kenya in 2007.  It has grown spectacularly: in mid-2012, there were 19.5 million m-money users in Kenya (83% of the adult population), transferring nearly US$8 billion per year (equivalent to 24% of GDP) – M-Pesa is responsible for more than 90% of these transfers.  Transfers are growing at nearly 40% per year.

It’s not that m-money initiatives in other developing countries have failed: there are an estimated 250m users of m-money services in emerging markets.  Just that they have not – yet – succeeded on anything like the scale of M-Pesa, with Kenya accounting for 30% of all emerging market m-money transactions in 2011.  For example, a recent survey in South Africa found only 16% of respondents with a mobile money account.  In Nigeria, only 3% of adults use mobile money.  And Africa is the lead continent: outside the Phillipines, m-money has been very slow to catch on in Asia. In India, for example, Nokia quit the m-money business in 2012 after two years of failing to build a critical mass.

How do we explain the differences?  University of Manchester research, based on six months of primary fieldwork conducted by Chris Foster, analysed the reasons M-Pesa has grown so fast in Kenya; reasons summarised in the model shown below:

Ongoing support from government – liberalisation of the mobile market; investment in infrastructure; light-touch regulation; facilitation of the initial pilot, etc – combined with strong consumer demand across all strata of society (itself partly fed by the instability and disruption following the disputed 2007 elections).  These drove a virtuous circle:

  • Competition between mobile sector firms pushed them to seek profits beyond the traditional middle-of-the-pyramid; answering the demand from the majority market of the country’s poor.
  • The service was delivered via atomised distribution networks that reached right down into poor urban and rural communities; a network of nearly 50,000 agents by 2012.
  • Those embedded intermediaries – essential in scaling any innovation to reach the base-of-the-pyramid – were given the flexibility to adapt business models, retailing patterns and service offerings so they met the specific and heterogeneous needs of their local customers.  Effective knowledge channels allowed these innovations to filter back up to the lead firms, which then scaled those they found most useful; fuelling yet further growth.

Armed with this model, we can analyse the m-money weaknesses in other emerging markets.  For example:

  • Much lower levels of customer demand (put down to both culturo-institutional factors and more effective functioning of and access to existing financial services) combined with a more stringent regulatory regime are behind the slow growth rates in India.
  • A much smaller number of intermediaries (agents) and a lack of innovation (e.g. to address cash float problems) is restricting growth of m-money in Uganda and Tanzania.
  • Tighter regulation and the much small number of intermediaries has held back expansion of mobile money services in South Africa.

We are not the first to try to understand the different performance of M-Pesa vs. other countries (see e.g. Wolfgang Fengler, Amaka Okechukwu who both also note the value of Safaricom’s market domination).  However, we hope that our model provides a clear and transferable framework for comparison, that can be used alongside more in-depth evidence from other countries to help understand their relative success or failure in mobile money.

If you see ways in which you think the model should be modified – based either on experiences in Kenya or elsewhere; then let us know . . .


[1] Foster, C. & Heeks, R (2012) Analysing policy for inclusive innovation: the mobile sector and base-of-the-pyramid markets in Kenya, paper presented at Globelics 2012, Hangzhou, 9-11 Nov [copy available on request: innov4dev@gmail.com]

Steering e-Government Projects from Failure to Success

2 August 2012 3 comments

How do you turn a relatively unsuccessful e-government (or ICT4D) project into a relatively successful one?

There’s not a lot of guidance on this question.  Lists of success and failure factors are generic rather than specific to any one project, and need to be analysed before the project starts.  Evaluation methodologies focus more on impact than implementation, and generally apply only after the project has ended.

What is needed is a “mid-implementation toolkit”: something that will both analyse where you’ve got to in the project, and recommend an improvement action plan for the future.  Researchers working alongside an Ethiopian e-government project have recently published the results of testing just such a toolkit.

Using the “design-reality gap” framework, the researchers gathered data from four different stakeholder groups involved with the e-government project, which had introduced a land management information system into one of Ethiopia’s city administrations.  The system was only partly operational and was not yet fully integrated into city administration procedures: it could therefore be described as a partial failure.

The design-reality gap framework helps measure any differences that exist between the project’s initial design expectations and current implementation realities.  It does this along seven dimensions (see figure below).

Where large gaps are found, these highlight the key and specific problem areas for the project.  In this particular e-government initiative, significant design-reality gaps were identified in relation to:

  • Management systems and structures (a failure to set up an ICT department and to hire permanent IT staff).
  • Staffing and skills (hiring only five of the required nine IT staff, and undershooting the necessary qualifications and experience).
  • Project objectives and values (allowing some culture of corruption to remain among lower-level administrators).
  • Information systems (absence of one core system module and of digitised documents).

These gaps demonstrated that the e-government system had not yet institutionalised within the city government.  The gap analysis was therefore used as the basis for a discussion with senior managers.  From the analysis and discussion emerged two things.

First, identification of small gaps that had lain behind the partial success of the system – the commitment of project champions, process re-design being conducted prior to introduction of new technology, and stability in the information that was digitised onto the e-government system.

Second, identification of an action plan that would close the main extant gaps between design and reality: creating the proposed new ICT deparment, hiring additional IT staff, and setting up permanent positions with clearly defined salary scales and promotional criteria. These, in turn, would provide the basis for implementing the missing module, and scanning the missing legal documentation.

Not all the gaps can readily be closed: it will take a much longer process of cultural change before the last vestiges of corruption can be eliminated.  Nonetheless, design-reality gap analysis did prove itself to be a valuable mid-implementation tool.  It is helping steer this e-government project from partial failure to greater success.  And the authors recommend its use by e-government managers as they implement their projects: it has helped to focus management attention on key e-government project issues; it digs beyond just technical issues to address underlying human and organisational factors; and it offers a systematic and credible basis for project reporting and analysis.

Feel free to comment with your own experiences of design-reality gaps, or other mid-implementation techniques for e-government project analysis and improvement.

ICT and Economic Growth: Evidence from Kenya

26 June 2011 1 comment

Do ICTs contribute to economic growth in developing countries?

In the 1980s, Robert Solow triggered the idea of a productivity paradox, saying “You can see the computer age everywhere but in the productivity statistics.”  And for many years there was a similar developing country growth paradox: that you could increasingly see ICTs in developing countries except in the economic growth data.

That is still largely true of computers and to some extent the Internet, but much less true overall as mobiles have become the dominant form of ICTs in development.  In particular key studies such as those by Waverman et al (2005), Lee et al (2009), and Qiang (2009) have demonstrated a clear connection between mobiles and economic growth and/or between telecoms more generally and economic growth.  They all address the “endogeneity” problem: that a correlation between telecoms (indeed, all ICTs) and economic growth is readily demonstrable; but that you then have to tease out the direction of causality: economic growth of course causes increased levels of ICTs in a country (we buy more tech as we get richer); you need to try to control for that, and separate out the interesting bit: the extent to which the technology causes economic growth. 

The studies try to do this and show ICT investments cause economic growth, but they are all multi-country and provide no specific insights into the experiences of a particular developing nation.  If you know of such data, do please contribute.  Meanwhile, a recent edition of “Kenya Economic Update” provides an example.  Some overall points:

  • The ICT sector grew at an average of nearly 20% per year from 1999-2009 (by contrast, Kenya’s largest economic sector – agriculture – shrank by an annual average of nearly 2% per year).
  • The number of phone subscriptions has grown from the equivalent of one per 1,000 adults in 1999 to the equivalent of nearly one per adult in 2010; Internet usage rates for 2010 were around four per ten adults.
  • Person-to-person mobile money transactions at the end of 2010 were equivalent to around 20% of GDP with two of every three Kenyan adults being users.

But the report’s strongest claim is this: “ICT has been the main driver of Kenya’s economic growth over the last decade. … Since 2000, Kenya’s economy grew at an average of 3.7 percent. Without ICT, growth would have been a lackluster 2.8 percent—similar to the populaton growth rate—and income per capita would have stagnated”.  So ICTs were responsible for 0.9 of the 3.7% annual GDP growth, and for all of Kenya’s GDP per capita growth.  Put another way, ICTs were responsible for roughly one-quarter of Kenya’s GDP growth during the first decade of the 21st century.

Other nuggets from the report and from original World Bank data underlying the report:

  • The “ICT sector” is actually the “posts and telecommunications” sector.  Comparing figures from Research ICT Africa for mobile + fixed line + Internet/data services with those for the overall sector suggests that ICTs form by far the majority (likely greater than 90%) of that sector.  For the ICT part of the sector, latest figures for 08/09 show mobile takes a 54.8% share, fixed line takes 39.5%, with 1.8% for Internet services and 3.8% for data services (not 100% due to rounding).
  • The ICT sector in 2009 still represented only 5% of total Kenyan GDP (compared to 21% for agriculture/forestry), and growth has been volatile, at least as based on the recorded figures, ranging from 3.5% per year up to 66% per year during the first part of the decade, and from 7.9% to over 30% during the second part of the decade.  Only tourism (hotels/restaurants) was more volatile.  In six of the ten years of the 2000-2009 decade, though, ICT was Kenya’s fastest growing sector.
  • In the first half of the decade, annual investments in mobile were higher than annual revenues; but the ratio has subsequently slipped to investment averaging around half of revenue.  Investments in mobile during 2001/02 to 2009/10 are estimated at US$3.2bn (c.KSh250bn) and US$3bn in fixed phone services, with broadband, Internet and BPO investments adding perhaps another US$1bn.
  • The ICT sector provided a more than six-times-greater contribution to Kenyan GDP in 2009 compared to 1999.  Directly, the ICT sector contributed to 14% of the country’s GDP growth between 2000 and 2009 (at constant (i.e. not actual/current but accounting for inflation) prices, it grew from KSh13.7bn in 2000 to KSh71.8bn in 2009; GDP overall grew from KSh976bn to KSh1.382tn).  So the World Bank’s calculation that ICTs contributed a quarter of GDP growth during the decade also include a specific, quantified assumption about ICTs triggering growth in other sectors, in particular the financial sector.
  • Employment in the ICT sector is estimated to be around 100,000 in 2011 (c. 0.7% of the estimated 14m overall labour force).  But ICT punches above its weight in other ways: changes in mobile prices at the start of 2011 were credited with both causing the Kenyan inflation rate to drop and with potentially derailing government constitutional talks due to the substantial knock-on effects in causing tax revenues to drop since phone companies now contribute such a significant proportion of government income.

So, overall, what do we have here?  Some fairly solid evidence that ICT sector growth (predominantly due to mobiles) is making an important direct contribution to economic growth in this developing country.  And some less clear evidence that the indirect GDP growth effect of ICTs may nearly double this.  Thanks to mobile money, Kenya has seen a particularly strong take-up and economic role for ICTs, but it is fairly typical in terms of mobile investment, revenues, subscriber base, employment, etc.  In that case, it’s not too much of an extrapolation to expect that ICTs will have contributed something like one quarter of GDP growth in many developing countries during the first decade of the 21st century.  Evidence of ICT impact that development strategists and practitioners should be more aware of.

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