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5: 5 Moyle, TCO

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Section 6: Chapter 5
Total Cost of Ownership & Total Value of Ownership

Kathryn Moyle(PhD)

Associate Professor

University of Canberra
School of Education and Community Studies
Director, Learning Communities Research Area
Director, Australian Information and Communications in Technology Education Committee (AICTEC) Secretariat
Canberra, Australian Capital Territory, 2600
Landline: + 61 2 62015649 Mobile: + 61 419030952 Fax: + 61  2 62015360


School leaders are regularly required to make decisions concerning the effective integration of ICT into their schools’ teaching and learning programs. School leaders however, face challenges about the processes to use to inform their decision-making. These challenges include knowing which data to draw upon; how to collect the data and how to analyze it so that meaningful decisions can emerge. As such, this chapter examines some recent activities aimed at using data to inform leadership and management strategies in schools as they pertain to teaching and learning with educational technologies, and focuses in particular on total cost of ownership and total value of ownership models for assisting school leaders to make judgements about teaching and learning with technologies. This chapter brings together for investigation the relationships between pedagogy, IT infrastructure, organizational development, asset management, and school financial models to explore approaches to data-driven decision-making in relation to technologies in schools.

Key Words

Data-driven decision-making
Total Cost of Ownership

Value of Investment


Intangible assets


Total cost of ownership (TCO):
all the costs associated with the use of computer hardware and software including the administrative costs, licence costs, deployment and configuration requirements, hardware and software updates, training and development, maintenance, technical support and any other costs associated with acquiring, deploying, operating, maintaining and upgrading computer systems in organizations

Value on investment (VOI): the systematic measurement of the alignment of a school’s human, information and organizational capital linked to a school’s strategy and performance in order to determine strategic readiness and the value of these intangible assets to the school.

Intangibles: An asset of value not directly quantifiable, such as teacher competencies and organizational learning, that sits within the context and strategic approaches of an organization.

1. Introduction

Schools, individuals, and governments in the 21st century are making considerable investments in information technologies (IT). Associated with these expenditures are widespread policy and societal expectations that school leaders must be able to build the capacity of their school communities where contemporary learning theories and practices include learning with technologies. At the same time, economic and educational purposes are articulated in school education policies in both developed and developing nations (cf. Association for Progressive Communications (APC) 1999-2007; Australian Ministerial Council on Education, Employment, Training and Youth Affairs (MCEETYA) 2005; Department for Education and Skills (DfES) (UK, 2005; United States Federal Department of Education, 2004). To meet the emerging educational and economic purposes articulated for schools and their students, these policy initiatives are leading to requirements for whole-school reform where there is a reshaping of the context and nature of school leadership and management. Furthermore there is a growing demand from communities, governments and other funding bodies for schools to be able to justify the costs of integrating technologies into educative processes not only for their intrinsic value, but also for instrumental, particularly economic purposes.

Many school leaders however are unsure of how data can be used to inform their work; what decisions concerning technologies they should make; or what types of decisions require their direct oversight (Moyle, 2006). Nor are they aware of what data they should collect and use to inform their decision-making processes, particularly in relation to the deployment and integration of technologies into teaching and learning within their schools. These dilemmas are among the driving demands for the development of school-based data-driven decision-making models (cf. Gipson, 2003; Hallinger & Snidvongs, 2005; Sayad, 2002; Whitty & Edwards, 1998). Approaches to determining the costs and benefits of technologies afforded through total cost of ownership and value of ownership models are being adapted from the business sector in the quest to assist school leaders meet the varying demands placed upon them.

It is against this complex, multi-dimensional backdrop then, that this chapter examines a range of recent activities aimed at using data to inform leadership and management strategies in schools as they pertain to teaching and learning with educational technologies. The ‘conceptual space’ for this paper sits at the intersections of teaching and learning with technologies, schools’ IT infrastructure requirements, their strategies for organizational and cultural development, and their finance and asset management strategies. The dual foci of ‘education’ and ‘technical’ issues are emphasized in this chapter, where ‘education’ refers to the structured personal and social pedagogical processes undertaken in schools to foster the development of children and young adults; and the ‘technical’ refers to the IT infrastructure and physical deployment requirements associated with integrating technologies into the teaching and learning in schools.

2. Policy contexts

Before exploring approaches to data-driven decision-making in relation to technologies in schools, it is worthwhile examining the global and nation-state policy contexts that are influencing the deployment of these technologies. School-based decisions are often made within broader social and cultural environments. Understanding these policy contexts can provide insights into the drivers for local decision-making and the approaches to be used.

School education is one of the major levers societies have for reproducing themselves. Policies are the mechanisms by which societies enact those levers. Many national school policies around the world are identifying both economic as well as educational purposes for school education. Indeed, there appears to be an increasing homogenisation of the policies that pertain to the deployment of technologies in schools emerging in the national policies of countries around the world, and in those from international agencies such as the Organisation for Economic Cooperation and Development (OECD, 2002).

Consistent themes in these policies include

  1. the articulation of both intrinsic and instrumental purposes for school education and the role of technologies in achieving these dual purposes; and

  2. moves towards school reforms that are increasing the autonomy of individual schools within centrally-administered accountability frameworks, in the name of meeting the demands of the ‘knowledge economy’; and

  3. the promotion of better management of finances and other resources in schools.

These three themes are now briefly discussed to expose the fabric of the policy contexts, and to highlight the emerging importance of data decision-making models in schools that are informed by information about the costs and benefits of technologies in teaching and learning.

2.1 Intrinsic and Instrumental Purposes of School Education

The school education technologies policies in OECD countries share similarities and have several overlapping and interwoven themes (cf. OECD, 2002). One such theme evident in these policies links teaching and learning with technologies to both the intrinsic benefits for the individual and to economic benefits for society. The Australian national policy, the Adelaide Declaration on National Goals for Schooling in the Twenty-First Century for example states:

Schooling should develop fully the talents and capacities of all students. In particular, when students leave schools they should: . . .

1.5 have employment related skills and an understanding of the work environment, career options and pathways as a foundation for, and positive attitudes towards, vocational education and training, further education, employment and life-long learning; [and]

1.6 be confident, creative and productive users of new technologies, particularly information and communication technologies, and understand the impact of those technologies on society… (Ministerial Council for Education, Employment, Training and Youth Affairs (MCEETYA), 1999, p. 1).

Strategies promoted for meeting the individual and economic purposes of school education include using educational technologies to improve students’ learning outcomes; increasing the personalisation of teaching and learning; raising the standards of students’ outcomes from schooling; and supporting students to achieve to their full potential. These aims are particularly evident in the policies of the United States of America (USA), United Kingdom (UK) and Australia (cf. Australian MCEETYA, 2005; DfES) (UK), 2005; US Federal Department of Education, 2004). Although these aims are commonly articulated, there are however differing views about the impact of technologies on students’ educational outcomes, and whether these technologies provide an acceptable return on investment. Debates about the cost, value and impact of technologies in school education then, are generating demands for research.

2.2 School Reform

Another consistent policy initiative emerging in countries including Australia, US and UK, is the use of data by school leaders and teachers to inform their decision-making. In these countries decisions previously made centrally are being moved to the local level. Often missing from this policy work, however, have been considerations about the leadership and management responsibilities within schools concerning the inter-relationships that exist between the necessary infrastructure and financial models required to include technologies in teaching and learning. Indeed, a tension for school leaders is the drive for local decision-making about teaching and learning with technologies in the context of centralized IT policies about curriculum and IT infrastructure, where IT deployments within education systems occur beyond the individual school level. Understanding the costs and value of technologies in schools though, is important for school leaders so that they are able to lead strategic planning processes in their schools and to accommodate these policy contradictions. Data-focused models for planning and setting school priorities are therefore an emergent requirement for school leaders.

Furthermore, school leaders are seeking assistance in how to interpret centralized policy decisions in order that they can lead and manage school level decision-making about the selection and deployment of educational technologies in schools (cf. Gipson, 2003; Moyle, 2006; Bialobrzeska & Cohen ND). School reform literature tends to focus upon the changes required at the local level to implement local management within the context of broader policy and accountability requirements (cf. Caldwell, 1998; 2004; Hallinger & Kantamara, 2002; Sayad, 2002). One of the justifications for school reform which is aimed towards increased accountability at the local level has been the perceived widening gap between the past, traditional role of schools compared to the emerging requirements of the ‘new economy’ or the ‘knowledge economy’ (Hargreaves, 2004). Until recently however, in those countries where their national policies directly address local school management and school reform, these policies have tended to be silent in identifying financial and accountability expectations or approaches concerning technologies in learning at the local level. There are therefore gaps between policy rhetoric and the experiences of schools. These gaps are drivers for schools to examine and adopt business models for measuring the costs and benefits of the technologies deployed.

2.3 Financial Accountability

The inclusion of technologies into the daily lives of schools is resource intensive. These costs are placing new requirements on schools’ financial accountability methods as governments and individual schools account for and justify the expenditures. But at the local school level, little research has been undertaken to investigate models of financial management and accountability that take into account the life cycles of educational technologies. Similarly, in any given country it is difficult to determine with accuracy the extent of the investments made in educational technologies or to make international comparisons based upon either costs, impact or value of technologies in schools.

It is within this set of complex policy contexts then, that schools and school systems require data upon which informed decision-making about deployments of technologies can be based. Academics, governments and teacher professional associations are starting to turn their attention to the issues of what data, how to collect it, the relationships between different data sets, and how to interpret the data in order that improvements in student learning outcomes can be achieved. Business models that measure the costs and benefits of technologies (such as total cost of ownership and total value of ownership) are being examined for fit for purpose in the schools sector.

3. Data-driven Decision-making

Data-driven or data-focused decision-making models are premised on the thesis that the collection, analysis and synthesis of data available primarily within a school can inform school improvement processes including teaching and learning, strategic planning, organizational development, and procurement processes. That is, the data are collected and used to make decisions about administrative and pedagogical systems in order to improve and promote student achievement and school processes. Putting in place efficient processes that enable schools to harvest and interpret the data and to get alignments between the data collected and the outcomes sought however, have tended to be difficult and time-consuming for many schools.

A broad range of data can be used to inform decision-making processes and outcomes. The nature of the data collected however, is dependent upon what decisions are required. Data including student records of academic performance, attendance records, student demographics, school purchase and expenditure patterns, teacher capability assessments and so on can be used. Depending on the nature of the decisions to be made, appropriate data can be collected and analyzed in ways that administrators, teachers and parents can accurately assess the information in order to inform their decision-making. Challenges for implementing data driven decision-making models include identifying

  • What is it we want to know?

  • Why do we want to know it?

  • What data is required in order to address the questions we have identified?

  • How will the data be collected?

  • Over what timeframe will the data be collected?

  • How will the data be analyzed?

  • How will the findings be fed into the decision-making structures of the schools in ways that will be beneficial? and

  • How will we know the data has made a difference to the decision-making processes within the school?

While data-driven decision-making would appear a rationale course of action for informing school improvement processes, to determine workable models of data collection and analysis that pertain to the deployment of technologies in schools in teaching and learning is complex.

4. Measuring Data

Data-driven decision-making models are based upon the collection of data that can be measured. Data measurement processes presuppose that what is to be measured can be defined. Often indicators are used to illustrate what is intended by a particular concept or term, but the use of indicators in ways that are meaningful ‘requires a conceptual framework within which they can serve to assess a current state, to measure linkages between policy and outcome variables, and to assess policy options’ (Grootaert, 1998, p. 10). It follows therefore, that defining the indicators to be used in gathering data for measuring the cost, impact or value of technologies in schools, requires the establishment of a conceptual framework and the acceptance of a language that brings with it shared understandings about the meanings of key words and phrases. An overview of the frameworks that are being used to scaffold approaches to data collection for measuring the cost, impact or value of educational technologies in schools, will be outlined shortly, following a brief exploration of the importance of language in the measurement and interpretation of data.

4.1 Language

Discussing and implementing measurement strategies requires clarity about the meanings intended so that the inputs and the outcomes from the measurements undertaken have veracity and hold meaning. If the methodologies of measurement are inadequate, the results from them are bound to lack authenticity. If there is a lack of clarity about what is the meaning or definition of the factors being measured and what the central outcomes are supposed to be, then confusion rather than clarity will be at the heart of the findings. Indeed, even defining what is intended to be counted, or defined as ‘educational technologies’ in schools can be problematic. Questions arising include ‘do ‘educational technologies’ include bandwidth?’ ‘Are mobile phones to be included?’ ‘Are desktop and portable computers counted in the same ways?’ ‘Do peripherals such as printers and digital cameras get counted as educational technologies?’ Definitional issues such as where do the boundaries lie concerning ‘what are educational technologies?’ then, generate definitional and methodological issues requiring resolution.

4.2 What is Being Measured?

To meaningfully interrogate data about deployments of technologies in schools requires an examination of the relationships between teaching and learning with technologies; the infrastructure required for deploying the technologies; the costs of ownership; the value on the investments in educational technologies in schools; and the outcomes achieved by students as a result of including the technologies into their learning. Studies particularly in the US and UK (British Education and Communications Technology Agency (BECTA), 2004; Institute of Education Sciences, 2007) seek to link technologies directly to students’ achievements including on standardized tests. The findings from these studies however, vary and often produce inconclusive results. A recent study from the US Institute of Education Sciences (2007) found for example, that the ‘test scores were not significantly higher in classrooms using the reading and mathematics software products’ (Institute of Education Sciences, 2007, p. xiii) than those in control classrooms. But when reading these studies it is important to understand the types of data collected and the definitions used in order to understand the conclusions found. Similarly schools must understand the nature of the data required and the analytical approaches necessary to enable them to gain meaning from their financial and IT deployment data.

4.2.1 Tangible and intangible assets.

In the private sector, recent descriptions of IT deployments include references to both ‘tangible’ and ‘intangible’ assets. ‘Tangibles assets’ are those items that have traditionally been measured and are usually defined as physical assets owned by an organization or individual which can be seen or touched. IT tangibles include objects such as computer hardware, technology peripherals and telecommunications costs. Tangible assets are covered by the Framework of International Accounting Standards. The worth of such assets is usually presented in quantitative terms. Schools can determine the costs of the purchases of the IT tangibles from their financial records and can map these costs over time.

More recently, the onset of the ‘knowledge economy’ has facilitated the notion of ‘intangible assets’. In the business sector, these assets include ‘goodwill’, brand names and the social capabilities of employees and strategies such as organizational learning. Stock markets for example have highlighted that companies like Microsoft can be valued at a price many times the value of their tangible assets (Roos, Ross, Edvinssen & Dragonetti, 1997), and it is the intangible asset of the ‘Microsoft’ brand name, that in part makes Microsoft so valuable. Related to the concept of ‘intangible assets’ are employees’ competencies. In accounting terms, the notion of financial worth is applied to intangible assets such as goodwill in relation to the worth of an organization in much the same quantitative manner as that used for tangible assets.

Intangible assets are increasingly being recognised in the business sector as critical to the success or otherwise of an organization (Kaplan & Norton, 2004a). The value of intangible assets vary however, depending upon the worth put on the assets themselves in given contexts. Measuring the value of intangible assets therefore is important information to be able to take into account in the work of strategic planning and organizational improvement. Unlike physical assets, intangible assets while difficult to quantify, can be worth more to the value of the organization than the physical assets. But measuring the value of intangible assets is problematic. A challenge for managers is if they “could find a way to estimate the value of their intangible assets, they could measure and manage their company’s competitive position much more easily and accurately” (Kaplan & Norton, 2004a, p. 52). These authors combine intangible assets into three major groupings: human, information and organizational capital. They use the following definitions.

  • Human capital refers to the skills, knowledge, talent of the people in the organization’s workforce.

  • Information capital refers to the information held in databases, networks and in the technological infrastructure of an organization.

  • Organization capital refers to the organization’s culture, leadership, the ability for staff to share information, and how the human capital (i.e., the people) are aligned with the strategic goals of the organization (Kaplan & Norton, 2004a,; 2004b).

In light of the work by Kaplan and Norton (2004a; 2004b), questions for the schools sector then include: Is it possible to apply the question of value to schools? What is the value of educational technologies in schools? and Does the value of educational technologies in schools match the findings on student learning outcomes?

In 1964, Drucker made the following observation that may be worth transposing to schools:

Other resources, money or physical equipment, for instance, do not confer any distinction. What does make a business [school] distinct and what is its peculiar resource is its ability to use knowledge of all kinds – from scientific and technical knowledge to social, economic and managerial knowledge. It is only in respect to knowledge that a business [school] can be distinct, can therefore produce something that has a value in the market place [to students and the community] (Drucker 1993/1964: 23).

The concept of ‘intangibles’ has long been the purview of behavioral scientists. The advent of the Internet and more latterly the ‘knowledge economy’ has provided developed nations with the opportunity to explore the ways in which intangible assets can add value to organizations. Governments and organizations worldwide are experimenting with standards which might be applicable to establishing baselines for the management, valuation and use of the knowledge, social competencies and various intelligences as indicators of economic wealth. At this stage though, there are no international agreements for the measurement of baseline standards, nor are there models for measuring the intangibles evident in schools.

5. Cost, Value and Impact

Three concepts though that have underpinned measurement activities in relation to the inclusion of educational technologies in school education are ‘cost’, ‘impact’ and ‘value’. These three terms are now used in this chapter to structure the forthcoming discussion about what is being measured and what measurement strategies are best associated with investigating school-based models of IT deployment.

During the past decade, schools and departments of education, particularly within UK, US and Australia have been trialing and using different approaches to measuring IT in schools. These activities have involved examining the costs, impact and value of integrating IT in school education. The purposes for which schools in each of these three countries have been collecting ‘cost’, ‘impact’ and value’ data have included

  • To enable data-driven decision making by school leaders about the costs of deploying educational technologies in schools;

  • To inform school planning processes about the nature and extent of IT deployments over time;

  • To provide accountability statements to central or regional departments of school education; and

  • To inform approaches for strategic planning and improvements in school education that includes educational technologies.

An overview of some of the work being undertaken in these three countries addressing questions of measurement of the ‘cost’, ‘impact’ and ‘value’ of technologies in and on students’ learning is now presented and examined here.

5.1 Cost

The costs of IT in schools are probably the easiest measures to determine, although they are not without definitional and methodological issues. The most simple way a school can determine the costs of IT is to come to an agreed definition about what constitutes ‘IT costs’ and then count the expenditure according to those definitions, over pre-determined periods of time. Another method of counting the cost of IT deployments is to apply a Total Cost of Ownership (TCO) measurement approach.

5.1.1 Total cost of ownership.

Over the best part of the past decade TCO measurement tools (both hardcopy and online) have emerged in the US, Australia, and the UK to assist school leaders to make decisions concerning IT deployments in their schools. The phrase ‘total cost of ownership’ was originally developed by Gartner Incorporated. Gartner promotes itself as the world's largest private information technology research and advisory company (Gartner, 2006). Gartner created the phrase ‘total cost of ownership’ to refer to all the costs associated with the use of computer hardware and software including the administrative costs, licence costs, deployment and configuration requirements, hardware and software updates, training and development, maintenance, technical support and any other costs associated with acquiring, deploying, operating, maintaining, and upgrading computer systems in organizations (Kirwin 1987).

The TCO model was developed by Gartner based upon assumptions applicable in private businesses in the corporate sector. Over the past few years, schools in the US, UK, and Australia have been investigating, independently of each other, how the Gartner TCO model or variations thereof, can be applied to their particular contexts at the local and regional levels. Since 1999, for example, the Consortium of School Networks (CoSN) in the USA has offered TCO support and advice to schools. A web-based tool has been available in the USA since 2003 and is being used by 1800 schools and school districts to calculate the TCO of educational technologies through the CoSN initiative: Taking TCO to the Classroom ( CoSN worked with Gartner to build this online TCO tool for schools. In the USA, schools have been applying the Gartner TCO tool to assist in the management and decision-making about the cost of deploying IT on their campuses.

Similarly, in Australia, the South Australian Department of Education and Children’s Services has piloted models of data collection consistent with the Gartner TCO approach. In 2003 a TCO analysis was conducted in one school to determine the comparative costs of deploying proprietary software compared to open source software (cf. Moyle, 2004). Subsequent TCOs have been used in South Australian government schools to inform the IT infrastructure requirements of schools. The TCO model in this jurisdiction is also heavily based upon the Gartner model of TCO.

In the UK, schools and agencies have also been investigating models and tools to assist in TCO calculations appropriate to their respective settings. BECTA has developed an online tool to assist in the gathering of cost-based data called the ‘ICT investment planner’ (BECTA, 2006). This BECTA tool is designed to allow schools to gain an in-depth view of technologies costs over a three-year period. The tool shares similarities with the Gartner TCO tool, but the BECTA tool includes both mechanisms for collecting financial data and also includes a questionnaire for staff to ascertain teachers’ perceptions of IT reliability, access to facilities and services, and an evaluation of the ease of use of the IT tools for teaching and learning purposes (BECTA, 2005b). Schools can use this planning tool to assist them to make decisions about the nature and extent of IT deployments.

It has become apparent through the TCO work in schools to date, that the focus has been upon accounting measures focused on structural inputs. The use of TCO tools in schools thus far then, has mainly contributed to understandings about tangible assets. While some would argue that the current TCO tools available go some way to measuring intangible costs such peer-to-peer tutoring and professional development, often these approaches are based upon business models where profit is the overall motive rather than teaching and learning. What TCO tools do not indicate for the schools sector though, are the intangible benefits associated with the tangible assets, nor do they identify the benefits associated with the deployment of technologies for teaching and learning purposes.

The respective TCO models used in the USA, UK and Australia then, have offered schools the capacity to quantify data previously left uncollected and analyzed. Little research however, has been conducted in the school sector, evaluating the TCO approach as it is applied to IT measurements in schools; nor about the appropriateness and value of the TCO model to schools or the school sectors. Now schools and school sectors in these countries are moving their investigations about measurement of deployment of technologies to analyze not only ‘cost’ but also questions about the ‘value’ of their investments. They are identifying the benefits the information a TCO brings but are also recognising the limitations of TCO analyses. As a result, a tri-nation project involving Australia, USA and UK has been established called Measuring the Value of Educational Technologies in Schools. The model underpinning this project has been developed based upon adaptations of the work by Kaplan and Norton (2002), which articulates measures of the value of IT in private sector companies. The purpose of the Measuring the Value of Educational Technologies in Schools project is to investigate in what ways tangible and intangible assets are linked with and contribute to the value of educational technologies in schools.

5.2 Value and Impact

There is little systematic, school-based research as yet investigating the question “What is the value of educational technologies in schools?” Some projects have purported to be measuring ‘value’ but have instead measured ‘impact’. The concept of ‘impact’ tends to be based upon a determinist assumption that, of themselves, technologies have an impact on the learning of students. The main aim of many impact studies is to determine the degree of impact technologies (of themselves) have made on students’ learning. The concept of ‘impact’ is grounded on the notion that because educational technologies are an ‘add-on’ to classroom practice, their impact can be individually measured. The question of degree of impact of educational technologies has on students’ learning is one that aims to assess end points only. In comparison, measurements of the value of technologies to students learning focuses both on questions of pedagogical processes as well as the outcomes achieved.

5.2.1 Value.

The concept of ‘value’ gives rise to questions such as

  • Value for whom?

  • What value?

  • How is the value expressed?

  • How do we know if the value has been achieved?

The value propositions against which to measure the value of the ‘intangible assets’ of schools pertain to the ways in which students achieve their learning outcomes with educational technologies. Measuring ‘value’ means making judgements about the degree to which outcomes statements that include statements of value compare with the evidence that those value statements are being fulfilled, and to how well aligned a school is in being able to meet the values they espouse. Values are framed in discourse, and are ‘in the eye of the beholder’. Hence the value of educational technologies in schools rests in what is articulated as being ‘of value’ and the carriage of those value statements throughout the practices and organizational processes of the school. There are therefore, two big pieces to the ‘values puzzle’ for schools to be able to measure the value of educational technologies in school education: the value of ‘educational technologies’ and the role played by ‘intangible assets’ such as teacher competencies and organizational learning.

Kaplan and Norton (2004b) suggest that measuring intangible assets is problematic if attempts to measure the intangible assets occur on a ‘stand alone’ basis. They argue that the value of intangible assets sits within the context and strategic approaches of an organization. As such, measuring the value of intangible assets in schools would involve estimating how closely aligned their intangible assets are to the school’s strategic approaches to their work. That is, if the intangible assets and the school’s strategic operations are closely aligned then the intangible assets will create value for the school. To systematically measure the alignment of a school’s human, information and organizational capital and link these to a school’s strategy and performance in order to determine strategic readiness and the value of these intangible assets to the school forms the basis of the aforementioned Measuring the Value of Educational Technologies in Schools project.

The capacity of a school or school district to achieve their value statements can be tracked organizationally through policy statements and strategic planning documents, and synthesized with evidence collected through

  • Cost of the investments made (as can be seen through TCO reports);

  • Teacher self-assessment skills surveys;

  • Interviews; and

  • Analysis of teacher program plans and students’ work.

The achievement of value statements can be seen in evidence such as clear links between

  • What teachers set out to do (as demonstrated in their teaching program plans and assessment requirements); and

  • The final examples of students’ work.

In 2003, the Australian Ministerial company ltd commissioned research concerning measurement of ‘intangibles’ in knowledge asset measurement (Palmer, 2003). In 2006, CoSN launched a new project investigating ‘value on investment’ (VOI) for measuring the value of specific educational technologies projects (see CoSN sees this work as a critical companion effort to its TCO work, where the VOI work aims to help schools understand the ‘value’ of their investments in educational technologies and to develop metrics to measure progress. They have found that while schools articulate visions around the value they get from technologies, they tend to have no real metrics identified to calculate their progress.

There is a growing awareness in the school sector that social and human capital have critical links to structural inputs and policy outcomes. There are moves away from only measuring resource investments to including measurements of processes, ‘distance travelled’, outputs and outcomes. Schools are adopting ‘whole school approaches’ to strategic planning and to measurement. The theoretical and practical dimensions of the interactions between the tangible capital and assets, and the intangible social, cultural and behavioural actions relevant to school performance however, require better understanding. Questions of value and the measurement of ‘intangibles’ as part of those measurements are emerging as issues for schools as well as for governments.

5.2.2 Impact.

It was argued earlier that to attempt to measure the direct impact of technologies on students’ learning was to take a deterministic view of the role of educational technologies in teaching and learning. The question of ‘impact’ relates to questions of ‘value’ only in that ‘impact’ could be considered to be one indicator of an achievement of value. But attempts to isolate the impact of technologies from pedagogies or other influences on student learning such as the teacher, is a risky business. Nonetheless researchers have attempted to do so.

In 2002, reports emerged in the UK, USA and Australia about the impact of technologies on students’ learning (cf. Harrison et al., 2002; Johnson & Barker, 2002; Newhouse, 2002). Each of these projects included approaches to the measurement of the impact of technologies on students’ achievements. The UK report was funded by the Department for Education and Skills (DfES) and was managed by BECTA. It involved 60 schools and used sample data to undertake statistical analyses of students’ attainment linked to IT experience (Harrison et al., 2002). The Office of Educational Research and Improvement in the US Department of Education commissioned the USA report. This project identified seven outcomes from technology integration for teachers and students and investigated evaluation strategies within each of these seven fields (Johnston & Barker, 2002). In Western Australia, Newhouse (2002) developed a framework for schools to be able to articulate the impact of technologies on learning.

Several other research projects from BECTA over the past five years have also addressed the question of ‘impact’ of technologies on students learning. BECTA has indicated that ‘there is a growing body of evidence relating to the positive impact of technologies on learner attainment and other outcomes’ (BECTA, 2005a: 4). BECTA argues that one impact of technologies has been on students’ motivation for learning where that learning includes technologies. Reviewing research about technologies and students’ motivation, BECTA has reported that there is evidence to suggest that the technologies can have a positive effect on students’ enjoyment and interest in learning. The key benefits for students identified BECTA were:

  • increased commitment to the learning task,

  • increased independence and motivation for self-directed study, [and]

  • enhanced self esteem and improved behaviour (BECTA, 2003, p. 1).

A 2004 report by BECTA indicates that the use of IT varies according to subject areas of study with more use of technologies being made in mathematics, science, computing studies and English, than there are in other subject areas. This report indicates that “there is substantial evidence from smaller focused studies of the contribution of specific uses of technologies to students’ learning. These include the use of simulations and modelling in science, ICT and mathematics, and the use of word processing in English” (BECTA, 2004, p. 5). Further, BECTA has reported that “many small studies have shown consistently positive results over the last twenty years, but this [the results] does not yet extend to all types of IT use, nor does it exclude the input of the teacher” (BECTA, 2005a, p. 4). BECTA reports however, that there is an emerging body of knowledge about the effects of specific types of technologies such as email and the World Wide Web on student achievements. They report though that the evidence of the effects of these is not yet seen to be consistent or extensive (BECTA, 2005a). BECTA also suggests that as a result of these different applications of technology within specific subjects, there is a greater number of digital resources available to these subject teachers; there is a greater body of knowledge in these subjects about pedagogical practices concerning teaching and learning that includes technologies, and there is also therefore a greater body of evidence of the effects of technologies in these subjects’ time (BECTA, 2004). BECTA reports that the positive impact on attainment is greatest where digital resources have been embedded in teachers’ practices over a period of time (BECTA, 2004).

But the connections that can be drawn between integrating technologies into teaching and learning, improving student learning outcomes and the costs involved in deploying technologies in schools still seem tenuous. At the same time, and in the context of scarce resources, school superintendents, school boards and policymakers increasingly want to know answers to questions such as ‘what is the value of technologies in students’ learning?’ and ‘what is the return being gained from investments in technologies?’ These questions allow an examination of the both the tangible and intangible costs and benefits, and represents spaces for new research.

6. Conclusion

This chapter has reflected upon the national policy directions driving the concurrent policy directions of school reform and deployments of technologies in schools. It has brought together for investigation the relationships between the following fields within school education: pedagogy, IT infrastructure, organizational development, asset management and school financial models. While TCO measurement tools are being used in the USA, UK, and Australia, it has been argued that although these TCO measurements enable the collection, sharing and analysis of costs of educational technologies, further work is required on the interpretation of that data and related data so that future directions in schools concerning the value of investing in technologies for teaching and learning can be determined. Such analyses will require symbiotic interpretations of the data by educators, so costs and value measurement work can interact iteratively.

While data-focused models for analyzing the costs of technology have been evolving to incorporate the concept of ‘value’ as a key metric, the complex issue of valuation of tangible and intangible IT assets in schools is only now being researched in the context of self-managing schools. Yet school leaders nonetheless are required to make financial, infrastructure, and pedagogical judgments and accountability statements about the value of educational technologies in school education. Measurements of assets are built into the financial, regulatory and reporting requirements pertinent to the school sector but the place of tangible and intangible IT assets remains problematic.

Intangible assets are increasingly being seen to be important for the success of schools and their students. However traditional financial accounting systems do not provide a suitable foundation for measuring the value created by enhancing these assets. Yet at both the micro and macro levels within school education intangible assets can drive long-term value creation.

A challenge for schools into the future is to focus and align their value propositions with their strategic approaches. As such, approaches to develop and trial the application of measurement systems that are focused on schools strategies in relation to integrating educational technologies into teaching and learning and with seeking evidence of the outcomes from that strategy, particularly those related to student learning, are beginning to gain traction in education communities. In order to measure the value of educational technologies in schools however, more research is required in order to shed light on the degree of alignment between a school’s value propositions and its actions.

7. References

Association for Progressive Communications (APC). (1999-2007). APC Africa ICT Policy Monitor, [accessed 25 February 2007]

Australian Ministerial Council on Education, Employment, Training and Youth Affairs (MCEETYA) (2005). Ministerial Council for Education, Employment, Training and Youth Affairs (MCEETYA). (2005). Joint Statement on Education and Training in the Information Economy. [accessed 2005, May 15]

Bialobrzeska, M. & Cohen, S. (ND). Managing ICTs in South African Schools. A Guide for School Principals, South African Institute for Distance Education (SAIDE), South Africa, [accessed 25 February 2007]

British Educational Communications and Technology Agency (BECTA) (2006). ICT investment planner, [accessed 2006, July 8]

British Educational Communications and Technology Agency (BECTA) (2005a). The BECTA Review 2005. Evidence on the progress of ICT in education, [accessed 2005, May 2]

British Educational Communications and Technology Agency (BECTA) (2005b). BECTA’s view: Strategic financial planning for ICT, [accessed 2006, May 31]

British Educational Communications and Technology Agency (BECTA). (2004). Educational research into ICT and whole school improvement – a selection of abstracts and further sources. research/school_improvement_bibliography.pdf [accessed 2005, April 6]

Caldwell, B. (2004). Re-imagining the self-managing school, Specialist Schools Trust, United Kingdom

Caldwell, B. (1998). Self-managing schools and improved learning outcomes, Department of Employment, Education, Training and Youth Affairs (DETYA), Canberra, Australia

Consortium of School Networks (CoSN) (2006). Taking TCO to the classroom, CoSN, [accessed 2006, March 1]

Department for Education and Skills (DfES) (UK). (2005). Fulfilling the potential. UK, [accessed 2005, May 24]

Drucker, P. (1993/1964). Managing for Results, New York: Harper Collins

Gartner Incorporated, (2006). About Gartner, Gartner Incorporated, [accessed 2006, May 17]

Gipson, S. (2003). Issues of ICT, School Reform and Learning-Centred School Design, National College for School Leadership, UK, [accessed 25 February 2007]

Grootaert, C. (1998). Social capital. The missing link? The World Bank

Social Development Family Environmentally and Socially Sustainable Development Network, [accessed 2006, August 17]

Hallinger, P., & Kantamara, P. (2002). Educational change in Thailand. in A. D. Walker, Clive, (ed.), School leadership and administration. Adopting a cultural perspective, pp.123-140. New York & London: RoutledgeFalmer.

Hallinger, P. & Snidvongs, K. (2005). Adding value to school leadership and management. A review of trends in the development of managers in the education and business sectors. Nottingham, England: National College for School Leadership image2/randd-adding-value-hallinger.pdf [accessed 2005, May 26]

Hargreaves, D. (2004). Personalising learning. Next steps to working laterally, Specialist Schools Trust, United Kingdom

Harrison, C.; Comber, C.; Fisher, T.; Haw, K.; Lewin, C.; Lunzer, E.; McFarlane, A.; Mavers, D.; Scrimshaw, P.; Somekh, B.; & Watling, R. (2002). ImpaCT2. The impact of information and communication technologies on pupil learning and attainment, Department for Education and Skills, [accessed 2006, May 18]

Institute of Education Sciences (2007). Effectiveness of Reading and Mathematics Software Products: Findings from the First Student Cohort. Report to Congress, US Department of Education,, [accessed 2007, June 27]

Johnston, J. & Barker, L. (eds) (2002). Assessing the impact of technology in teaching and learning. A sourcebook for evaluators, Institute for Social Research, University of Michigan, USA, [accessed 2006, May 18]

Kaplan, R.S & Norton D.P. (2004a) Strategy Maps: Converting intangible
 assets into tangible outcomes, Harvard Business School Press

Kaplan, R.S. & Norton, D.P. (2004b). Measuring the strategic readiness of intangible assets, Harvard Business Review, February, Vol 82, (2) pp52-63

Kaplan, R.S. & Norton, D.P. (2002). Strategy Maps. Converting Intangible Assets Into Tangible Oucomes, Harvard Business School Press, USA

Kirwin, B. (1987). Gartner Total Cost of Ownership, Gartner, USA [accessed 25 February 2007]

Malhotra, Y. (2002). Measuring knowledge assets of a nation: knowledge systems for development, Invited keynote presentation at the United Nations Advisory Committee of the Department of Economic and Social Affairs, New York City, 4-5 September 2004, [accessed 2006, May 18]

Ministerial Council on Education, Employment, Training and Youth Affairs (MCEETYA) (1999). Adelaide Declaration on National Goals of Schooling in the 21st Century, Curriculum Corporation, [retrieved 30 May 2007]

Moyle, K. (2006) Leadership and Learning with ICT, Voices from the Profession: What Australian School Leaders Say, Ed-Media 2006--World Conference on Educational Multimedia, Hypermedia & Telecommunications, Association for the Advancement of Computing in Education, Orlando, June 26-30

Moyle, K. (2004). Total cost of ownership and open source software, Department of Education and Children’s Services (SA) [accessed 25 February 2007]

Newhouse, C.P. (2002). A Framework to articulate the impact of ICT on learning in schools, Specialist educational services, Perth, Australia, [accessed 2006, May 18]

Organisation for Economic Cooperation and Development (OECD), (2002). National Case Studies on ICT in Schools,2340,en_2649_34519_33932151_1_1_1_1,00.html [accessed 2005, May 20]

Palmer, C. (2003). A scoping study for a knowledge valuation project in limited (unpublished).

Roos, G., Ross, G., Edvinsson, L., Dragonetti, N.C. (1997), Intellectual Capital: Navigating in the New Business Landscape, Macmillan Business, Houndmill

Sayed Y. (2002). Democratising Education in a Decentralised System: South African policy and practice, Compare, Vol. 32, (1), pp 35-46

United States (US) Federal Department of Education. (2004). A Golden Age in American Education How the Internet, the Law and Today’s Students are Revolutionising Expectations. National Education Technology Plan,’s%20students%22 [accessed 2005, May 11]

Whitty, G. & Edwards, T. (1998). School Choice Policies in England and the United States: an exploration of their origins and significance, Comparative Education, Vol 34 (2), pp211 -227.

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