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47 Modeling the Costs and Economics of Distance Education Greville Rumble Independent Consultant

The costs of educational technology are of increasing interest to academics, government, international agencies, and development agencies. The relatively new discipline of the economics of education, initiated in the United Kingdom by Vaizey (1958) and in the United States by Schultz (1961), focused on attempts to quantify the economic benefits of, and the efficiency of public expenditure on, education. In parallel, the application of technology to education came to be seen as a way of lowering the costs of education (Jamison, Suppes, & Wells, 1974, p. 57). The use of technology would, it was argued, change the production function, offering what Wagner (1982, p. ix) later described as “a mass production alternative to the traditional craft approach.” The scene was therefore set for academic economists to take an interest the in possible impact of technology on educational costs.

COSTING DISTANCE EDUCATION Broadly one can identify four generations of distance education systems:

r correspondence systems (referred to below as Class I systems), r educational broadcasting systems (Class II systems), r multimedia distance education systems (Class III systems), and r online distance education systems (Class IV systems). These distinctions are not, of course, as clear-cut in practice as typologies of distance education make them appear. Nevertheless, they offer a useful framework within which to consider the costs of distance education in its various “ideal” forms. It was the development of capital-intensive, big-budget Class II and III systems that forced governments and aid agencies to ask how much these systems would cost, at the same time 703



as the providing institutions sought to derive methods that would help explain their costs to funding agencies. Ultimately three lines of inquiry emerged:

r From the mid-1970s until about 1982, a series of international conferences on the costing of educational technology took place (see UNESCO, 1977, 1980; Klees, Orivel, & Wells, 1977; Eicher, Hawkridge, McAnany, Mariet, & Orivel, 1982). Drawing on work undertaken progressively by Orivel (1975, 1977), Jamison, Klees, and Wells (1976, republished 1978), Jamison (1977), Klees and Wells (1977), and Eicher (1977, 1978a, 1978b), by the early 1980s the methodological issues had been agreed on, and it was left to Eicher et al. (1982) and Orivel (1987) to synthesize the work. However, although this work addressed the costs of a wide-range of technologies, in practical terms the cost functions developed applied most closely to the Class II systems, which were then the focus of international efforts. r Also from the mid-1970s, a series of studies sought to explain the operating costs of the distance teaching universities (Smith, 1975; Rumble, 1976, 1981, 1982; Wagner, 1977; Snowden & Daniel, 1980; Muta, 1985; Muta & Sakamoto, 1989; Muta & Saito, 1993, 1994; Pillai & Naidu, 1991, 1997). These studies generally aimed to show that the distance teaching university in question was (a) more cost-efficient than traditional universities in the same country, and/or (b) would achieve economies of scale if only it were allowed to expand. By and large this work failed to address the costs of the constituent technologies of the distance teaching universities, choosing rather to take the media mix as a given. As such they could be criticized for failing to seek more cost-efficient methods through the exploration of the costs of different technology strategies (c.f. Mace, 1978). r Finally, the costs of developing Class IV systems began to receive attention from the late 1980s (Rumble, 1989, 2001; Phelps, Wells, Ashworth, & Hahn, 1991; McGraw & McGraw, 1993; Arizona Learning Systems, 1998; Bacsich et al., 1999; BartolikZlomislic & Bates, 1999; Bartolik-Zlomislic & Brett, 1999; Inglis, 1999; Rumble, 1999; Whalen and Wright, 1999a, 1999b; Bakia, 2000). In the process a whole new generation of people is beginning to grapple with issues of cost methodology and the problem of applying costing techniques to online learning (see, for example, Bacsich et al., 1999).

MODELING THE COSTS OF DISTANCE EDUCATION The basic cost function for educational television systems developed by Jamison, Klees, and Wells (1978, pp. 93–98) suggested that the total costs (TC) of a system were made up of the costs (C) of a number of functions: TC = CC + CP + CT + CR

(Eq. 1)

where the subscripts C, P, T, and R refer to central, programming, transmission, and reception respectively (Equation 1). Each of these constituent components CC , CP , and so on are further broken down into separate cost functions that reflect the determining variables that drive costs. Among the main system variables identified (Jamison, Klees, & Wells, 1978, p. 94) are the number of students, the number of hours of programming each year, the area of the region to be served, the number of pages of printed material for each student, the number of students who share a receiver, the fraction of the reception sites located in nonelectrified areas, and the number of reception sites. Among the cost variables identified are the cost of project planning and start up, central administration, the production facility (land and buildings), and the production equipment; the annual cost of program production; the cost of the transmission




facility (land and buildings); the annual cost of power, maintenance, and operating personnel for a transmitter capable of covering the area served; the cost of one receiver; the cost of related reception equipment (e.g., antennae) for reception sites; the cost of building modifications for television reception; the cost per reception site for power generation equipment (required for television only in nonelectrified areas); the cost of electric power per reception site per hour (using power lines); the cost of electric power per reception site per hour using local power generation equipment or batteries; the cost per hour for maintenance at each reception site; and the cost of a book per page (Jamison, Klees, & Wells, 1978, pp. 94–95). All capital items were annualized for a given number of years (which varied depending on the nature of the capital item) using the standard annualization factor, a(r, n) and an appropriate social discount rate (r). The approach initially used by those modeling the costs of the distance teaching universities was much simpler. Basically just three variable, cost-inducing outputs were identified: the number of courses in development/production; the number of courses in presentation; and the number of students. Capital costs were ignored. Wagner’s (1977, pp. 370–371) cost function explaining the costs of the British Open University (Equation 2) is a good example of the approach taken: E = α + βn Cn + βp Cp + δS

(Eq. 2)

where E = the total recurrent expenditure α = the total fixed costs of the enterprise βn = the average variable cost of development/production per standard course equivalent per year Cn = the number of standard course equivalents in development/production in any year βp = the average variable cost of presentation per standard course equivalent per year Cp = the number of standard course equivalents in presentation in any year δ = the average variable cost per full-time equivalent student per year S = the number of full-time equivalent students

PROBLEMS WITH THE MODELS All these models basically assume that the total costs of a system are made up of a combination of fixed and variable costs. Fixed costs are those that do not vary with any change in the level of activity; variable costs do change. The total costs of a system (T) will thus be equivalent to the sum of the fixed costs (F) plus the variable cost per unit of activity (V) times the volume of activity (X): T = F + VX

(Eq. 3)

Such models are seriously weakened by the fact that they do not specify “the fundamental variables, which affect costs, in sufficient detail to be of practical value to people who are trying to prepare an operating budget for an institution” (Rumble, Neil, & Tout, 1981, p. 235). With each technology having its own cost structure, and with empirical studies showing wide variations in the actual cost of technologies (see below), it is clear that the models would need to be much more sophisticated to capture the actual factors driving costs. For example, the development, production, and delivery costs of a course vary depending on the mix of media,



and the models ought to reflect this. Similarly, in the case of student costs, while many of the costs of student support are driven by the number of individual students in the system, some of them are driven by the number of student course enrollments and others by the number of student groups. The idea that there is an average course with an average cost per course, or an average student with an average cost per student, is a fiction. At best the models provide us with a crude, aggregated, approximation of costs. More significantly, all the models treat overhead costs as a fixed cost that is then allocated to students in an attempt to derive an average student cost, such that the average cost per student (A) is equal to the variable cost per student (V) plus a “share” of the overhead costs (F): A = V + F/S

(Eq. 4)

Even Rumble’s models of the costs of the Universidad Estatal a Distancia in Costa Rica and the Universidad Nacional Abierta in Venezuela (Rumble, 1981, pp. 385–386; 1982, pp. 129–130), which attempted to correct some of these weaknesses by taking account of other factors such as the number of organization/managerial units managing academic programs, the number of local study centers, the number of broadcasts, and so on, do not capture the fundamental variables driving costs in sufficient detail to make them useful as tools for a real understanding of costs. A further problem with some of the models is that they do not take account of capital costs. The models developed by Jamison, Klees, and Wells did this, but those used to cost the distance teaching universities did not. Economists are generally agreed that the costs of capital tied up in projects need to be taken into account (Jamison, Klees, & Wells, 1978, p. 32; Perraton, 1982, p. 6; Wagner, 1982, p. 89; Levin, 1983, pp. 68–69). This is generally done using the annualization equation which “annualizes” capital costs by estimating an average of the combination of depreciation and interest on the undepreciated portion over the life of the facility (Equation 5): a(r,n) =

r(1 + r)n (1 + r)n − 1

(Eq. 5)

where a(r,n) is the annualization factor, n is the life of the capital equipment, and r is the prevailing rate of interest. It is worth noting, however, that quite small changes in the rate of interest and the lifetime assumptions made will have significant implications for the total cost of the project. However, the use of interest rates to assess the relative cost of public projects involving capital elements does not, in Eicher’s view, rest upon a sound theoretical basis for public finance decisions (because such decisions rarely in fact involve a choice between spend and investment for income growth) (Eicher, 1978b, p. 13). However, it is difficult to see how a true comparison of costs between a highly capital-intensive and a less capital-intensive option can be obtained without taking at least the annualized capital cost into account, and this the cost functions developed by Smith (1975) and those who followed him failed to do. Another problem affects those distance education projects that are embedded in dual-mode institutions (i.e., institutions that teach both by traditional and by distance means). The problem arises because some of the costs of the two approaches may be shared because of the existence of what are called joint products. A joint product is one of two or more products in which, initially, a single stream of inputs goes in until a “separation point” is reached, after which the products are acted on separately. For example, academics may develop a course and then teach one version by traditional class-based means and another version by distance means; or a series of lectures delivered in class may be videotaped and subsequently used in a distance program. Equally, some of the overhead costs of the institution will support the distance program, and some the off-campus program, and will therefore need to be apportioned across the products if only for pricing purposes. Rumble (1997, pp. 65–70) identified no less than six different




approaches used to attribute development costs to joint products in mixed-mode institutions and two approaches to attribute delivery costs. Such variations can radically affect the level of reported costs in systems and can be used both to manipulate data provided to funding bodies and to “justify” different pricing decisions.

FACTORS DRIVING COSTS IN EDUCATION In practice, the identification of drivers affecting costs in distance education systems has developed over the years to embrace a range of factors, many of which are interrelated. What is difficult is to put cost figures to these drivers. Technology Choice Technology choice has a significant effect on both total and average costs. A summary of the current evidence follows. (a) Face-to-Face Teaching. Face-to-face teaching in lectures (any audience size), seminars (small- to medium-sized group teaching), and tutorials or supervisions (one-to-one or oneto-two) involves relatively low fixed costs but, particularly in the case of small- and mediumsized group teaching, incurs a rapid increase in student variable costs because increases in student numbers and hence group numbers have to be matched with increases in staff numbers. The average student:staff ratio can vary enormously. In the UK higher education, for example, a feature of the past 30 years has been an increase of the average number of students per member of staff. This has had a major effect on the cost structure of campus-based higher education in the UK. As Scott (1997, p. 38) comments, the massification of British higher education is demonstrated [by] the sharp reduction in unit costs. Overall productivity gains of more than 25 per cent have been achieved since 1990. . . . This pattern, which exactly matches the expansion of student numbers, closely follows the cost curves in other countries where mass higher education systems developed earlier than in Britain. It supports the claim that mass systems have a quite different economy from that of e´ lite systems. (my italics)

Generally speaking, where few students are involved, face-to-face teaching may be the cheapest option. This is because student numbers are too small to warrant investment in learning materials. However, depending on the technology choice, distance education can be more costefficient than traditional approaches for large numbers of students, but any attempt to provide a significant amount of face-to-face contact will create a very expensive system. (b) Technology in Distance Education. Distance education systems are generally said to have high fixed costs but low variable costs per student. Each technology has its own cost structure. As evidence accumulated, so analysts attempted to generalize their findings in ways helpful to decision makers. In the mid-1990s, Bates (1995, p. 5) indicated that print, audiocassettes, and prerecorded Instructional Television were the only media that were relatively low cost for courses with populations of from under 250 students a year to over 1,000 student a year. In addition, radio was also likely to be low cost on courses with populations of 1,000 or more students. Other media, such as good quality broadcast television, preprogrammed computer-based learning, and multimedia are much more expensive. The problem with such generalizations is that technology costs are in practice susceptible to wide variations. The NBEET study found a range in the production costs of a 30-minute



videotape of from Australian $1,000 to A$39,000 (NBEET, 1994, pp. 36, 37). The range of costs in computer-based teaching was also very great (p. 37). Bates (1995, p. 197) gave a cost range of from Canadian S2,600 to S21,170 per student hour for the development of online teaching materials. Arizona Learning Systems (1998, pp. 13–14) suggested costs of from US$6,000 to $1,000,000 for a three-unit Internet course, depending on the approach used. The cheapest approach involved the presentation of simple course outlines and assignments; more expensive options included the provision of text ($12,000), text with reference materials ($18,000), images ($37,500), audio and video ($120,000), simulations ($250,000), and virtual reality ($1,000,000). The problem arises in part because a whole range of organizational and working practices impact on the actual cost of the technology as it is used in particular circumstances (see below). There may also be problems because costs in one system may not be directly comparable with those in another. For example, some systems have access to facilities (for example, study center space or transmission time) at preferential rates; in other cases costs that in one system fall on the institution’s budget may in another be passed onto the students, so that comparisons based on institutional budgets are misleading. It is very important, when one comes to compare the costs of one system with another, to be clear about the precise nature of the model being used and to understand how this can affect cost comparisons. (c) Conclusions. The main message to emerge from these studies is that there are a great many caveats that have to be made to any statement about the costs of technology within education. The problem is that the cost of a given technology is not just driven by the hardware and software costs of that technology but by other factors—of which the working practices underpinning the use of the technology is perhaps the most important. Using Existing Materials The costs of developing courses can be brought down by developing “wrap-around” materials to accompany existing textbooks and other materials, thus “transforming” them into a distance course by commodifying traditional lectures (by, for example, videotaping them) for later use and by buying-in material developed elsewhere by another supplier (Rumble, 1997, pp. 87– 91). Certainly the additional costs of videotaping lectures and reusing them for subsequent generations of students can be very low indeed (Fwu et al., 1992). Studies suggest, however, that the cost advantages of buying-in materials can be overestimated. Although this may be a cheaper option for low student numbers, payments to the providing institution mean that it can be cheaper to develop one’s own materials—with Curran (1993, p. 21) suggesting that the break-even point at which this is true can be as low as 123 students on a course. Working Practices Much of the information that we have on the costs of technologies is derived from particular case studies. Although analysts frequently counsel against assuming that the costs in one system will be similar to those in another, the urge to generate guidance for policymakers often leads to an assumption that the cost experience of one institution will transfer to another. Underlying this assumption is a belief that technology determines the social sphere (that is, the organizational structures, hierarchies, and work roles) within which it is used. Particular levels of costs are then thought to be a natural outcome of the sociotechnical conditions engendered by a given technology. While technological determinism has now been discredited (c.f. Grint & Woolgar, 1997, pp. 11–14 for a resum´e of the arguments), the perspective has a long history and was still being advocated in the 1960s and 1970s (Bell, 1960, 1973; Kerr, Dunlop, Harbinson, &




Myers, 1964; Blauner, 1964). Blauner (1964, p. 6), for example, maintained that “the most important single factor that gives an industry a distinctive character is its technology.” A technologically determinist approach to distance education would naturally be embedded in the literature, manifesting itself in assumptions that it is the technology itself that determines the structures of distance education systems. There is some evidence of this in recent literature. Thus Daniel (1996, p. 15) cites McGuinness’s (1995) claim that “technology, distance learning and global networks for scholars and students are transforming institutional practices in ways that may make current institutional structures and governmental policies obsolete” (my italics). Elsewhere Daniel (1996) suggests that what he calls the mega-universities (that is, universities that have distance teaching as their primary activity and in excess of 100,000 active enrollments [p. 29]) “operate differently from other universities in many ways, not least in the way they have redefined the tasks of the academic faculty and introduced a division of labor into the teaching function” (p. 30). He goes on to claim that “changes in technology transform the structures of industries” (p. 80, my italics). Further, this is a continuing process since “it is clear that new technologies, such as computer conferencing and the Internet, will change the format of university courses taught at a distance” (p. 130, my italics). In point of fact, of course, the relationship of technology to structure, work roles, skill levels, and so on is not simple, not constant across settings and firms, and not determined by the technology itself but by management. This does not negate the fact that technology can be used by management to reduce costs and that technology change may be accompanied by organizational change. This kind of technological determinism can blind managers to the very real variations in the way in which technologies are used in practice and to the wide range of costs that result. In fact, the way in which work is organized around a given technology, and the way in which human resources are engaged in the enterprise (including the use of casual as opposed to core labor), has a profound effect on costs (Rumble, 1997, pp. 83–87). (a) The Organization of Academic Labor. Many distance teaching systems have industrialized the organization of materials development, production, and delivery, thus breaking with the traditional craft approaches that characterize traditional education (Peters, 1967, 1973, 1983, 1989). The overall task of teaching can thus be divided into its constituent roles— curriculum design, instructional design, content preparation, materials development, and production (all tasks that themselves may require a number of specialisms), tutorial backup, continuous assessment, and examination script marking. These roles can be given to different people, in part reflecting the need to access specialist knowledge and skills, and in part because the very nature of the system would make it difficult for one person to undertake all of the tasks (e.g., to both develop all the materials and teach and assess all the students on a large-scale course). Most large-scale systems have a division of labor between those who develop the materials and those who support and assess the students. However, where student course numbers are low, individual academics may both develop the materials and teach the students (Rumble, 1986, pp. 127–129). Those who believe that the industrialized model reduces academic autonomy and control over the teaching process and thus degrades academic work (e.g., Campion & Renner, 1992; Raggatt, 1993) see this model as particularly attractive. It is one of the reasons why online teaching models are thought to be so attractive—though in fact any system that involves both the development of online materials and the support of online students is likely to begin to move toward a division of labor if course student numbers increase beyond the support capacity of a small group of academics. The organization of the course development process—and the way in which it is planned and controlled—also varies greatly. Course authors can work on their own, with an editor (the author-editor or transformer model), in small groups, or in large course teams (Rumble, 1997, pp. 83–86)—the latter being an expensive way of developing materials (Perry, 1976, p. 91).



What is feasible is in part determined by the way in which the curriculum has been divided and the content organized. Small modules or courses, and those based more heavily around existing texts and materials, allow much greater scope for individual academic control, while large modules and those involving a great deal of specially developed materials are likely to require a big team effort. In general the use of consultants can bring down costs significantly (Rumble, 1997, p. 87). (b) Contracting of Academic and Support Labor. The division of academic labor has been accompanied by another feature—the use of short-term and piece-work contracts. The nature of the employment contract is a crucial factor in determining costs. Course developers may be hired on permanent, full-time contracts of service to develop course materials. This is the most costly option, with a potential long-term commitment (to holiday, sick, and study leave) up to retirement age. Alternatively, they can be hired on short-term temporary contracts of service that limit the long-term liability of the employer; or as consultant authors and materials’ developers on contracts for service, essentially paid piece-rates for their output. This latter option is relatively cheap. As for the tutors that support the students, many of them are employed on piece-work rates, paid by the hour for their class tutoring or by the script in respect of assignment and examination scripts marked. In mixed-mode institutions course developers may teach on-campus students as well as develop materials. In some systems staff who have a full teaching load on campus are bought out to help develop distance teaching materials, either to develop a version of their own existing on-campus courses or a new course (Rumble, 1986, pp. 131–133; 1997, pp. 81–83). However, this does not always happen, with the result that staff may be reluctant to get involved in mixed-mode operations (Ellis, 2000). Nonacademic work—for example, editing, illustration, and the like—can also be given to consultants, while whole functions such as printing and the production and transmission of broadcasting may also be outsourced, either to a single supplier or to a number of suppliers. Whether outsourcing actually saves money will depend on circumstances including the relative transaction costs of in-house versus outsourced work and the extent to which outside providers can offer a price that is competitive. The Curriculum The number of courses on offer is also an important variable. The more courses that are offered, the greater the investment in developing, maintaining, and remaking the course materials will be. The number of courses offered depends in part on the number of awards or qualifications on offer, the range of subjects offered within those qualifications, and the extent to which students can choose elective courses as opposed to being restricted to mandatory courses. The number of years over which courses are presented, and the frequency with which materials have to be remade, will also affect costs. All content dates with the passage of time but in some subject areas (e.g., computing) knowledge dates extremely quickly, while in other subjects changes of legislation, societal change, and changes in academic interests and the impact of research on the subject all result in the need to update courses. The Number of Learners All commentators recognize that the number of students enrolled in a system is a crucial factor affecting both total system costs and average student costs. Media and technology choice will have a bearing here, given that some technologies lend themselves to economies of scale




while others do not. Systems that provide considerable support to students generally deliver significantly less in the way of economies of scale than those providing little or no support. It is generally assumed that the more students a distance learning system has, the lower the average cost, and this is broadly so. This has led many distance education systems to seek to expand their student numbers year on year. There is, however, a problem with this. What distance educators seem to do in many ways parallels the wasteful practices of the post-World War II American automobile industry (see Johnson, 1992, pp. 44–46) by assuming that the high overhead costs inherent in distance education can be controlled by expanding student numbers to position oneself to sell places on courses at a lower rate than more traditional institutions. Thus, the emphasis is placed on driving expansion fast enough to cover overhead costs that are, to a considerable extent, caused by scale and complexity, and that are deemed to be fixed and hence beyond control. But most the economies of scale are reaped early on in expansion. The nature of the average cost curve is such that the more students there are in the system, the harder it becomes to achieve significant economies of scale. The pursuit of expansion in itself may cause costs to rise. Thought must also be given to the number of students at individual course level. For any given student population, the more courses on offer, the lower the average course population. However, students will rarely if ever be distributed equally across the courses. It is much more likely that the 80:20 rule will apply, with 80% of the students enrolled on something like 20% of the courses, so that one can expect a few courses have very high student populations and a large number to have relatively few students in them. Planners thus need to consider the likely student population on each course and bear this in mind in selecting the media to be used on each course. Organizational Structures Two things are worth noting about the cost studies undertaken to date. Firstly, most of them focus on the costs of single-mode systems, either large-scale educational broadcasting systems (Class II systems), or medium to large multimedia distance education systems (Class III systems). Second, where cost comparisons are made between the costs of traditional and distance education, the comparison is between the costs of these large-scale systems and the costs of traditional approaches to education. Relatively fewer studies have either looked at or compared the costs of distance-based provision within the context of mixed-mode institutions teaching both on campus by traditional means and off campus by distance education (exceptions include Wagner, 1975; Deakin University, 1989; Coopers & Lybrand, 1990; Taylor & White, 1991; Ansari, 1992; Makau, 1993; Cumming & Olaloku, 1993). Intriguingly, some of these studies suggest that mixed-mode institutions may achieve even lower costs per student than do distance teaching institutions—an issue that Rumble (1992; 1997, pp. 152–159) has examined. Using evidence derived from Taylor and White (1991), Rumble (1992) argues that, given the relatively low costs of producing videotaped versions of lectures and simple printed lecture notes around guided reading, it does not cost much to develop resource-based learning packages that can be used by on-campus students. Indeed, many campus-based institutions are doing this already to lower their teaching costs. Once they have done so, they can then use the same materials to teach off-campus students, often at a lower costs than that attained by many of the purpose-built distance teaching institutions. Hallak (1990, p. 200) and Renwick (1996, pp. 59–60) also suggest that lower costs may be possible; the Committee of Scottish University Principals (1992, pp. 34–9, 41) was unable to come to a conclusion; and Daniel (1996, pp. 32, 68) believes the competitive advantage lies with the mega-universities. This is an area for further research.



CONCLUSIONS As mentioned above, one outcome of the earlier work was general agreement on the methodology to be employed in costing educational technology and distance education projects. Unfortunately, these methods have their foundations in 20th-century management accounting systems. Such systems have difficulty in dealing with multiproduct systems because (a) they take little or no account of variations in the design of courses and the levels of service offered to different students (e.g., students on different courses or having different educational and support requirements), but instead assume a standard course model and standard student incurring average direct costs; (b) they often fail to identify the real drivers of costs; and (c) they allocate overheads to products by largely arbitrary means (see Johnson & Kaplan, 1987, for a critique of 20th-century management accounting systems). The failure to recognize the wide variation in costs of products and services to students, and the failure to identify the cost drivers actually pushing costs, means that most of the studies cited in this article, and all the models identified, are of limited use in helping decision makers understand the real behavior of costs in distance education systems. This largely invalidates the use of simple cost functions to project forward total system costs in situations where student numbers are increasing or being reduced, or where curriculum, organizational, technological, and process change is under way. These are very real drawbacks and limit the value of the studies in terms of the practical advice that can be gained from them to guide future decision making. On the other hand, these difficulties should not detract from the fact that in very many cases the average cost per student or graduate in Class I, II, and III distance learning systems is less than the average cost in classroom-based systems. So, while distance education is not necessarily a more cost-efficient option, it often is, and it is this that rightly makes it an attractive proposition for politicians, governments, educational leaders, and training providers. Two worries remain, however. First, it is not yet clear what the relative costs of Class IV (computer-based/virtual classroom) distance education systems will be. There are worrying indications that such systems require more input from teachers than Class I, II, and III systems, not least because they enable greater interactivity between teachers and students. Bates (2000, p. 127) suggests, first, that the cost of providing online student-teacher and student-student interaction tends to be lower than the cost of providing traditional face-to-face support, and that is because “a good deal of the students’ study time . . . is spent interacting with the pre-prepared multi-media material, so the teacher needs to spend less time per student overall moderating discussion forums compared with the total time spent in classroom teaching” (p. 128), and second, “the online costs still have to be added to the costs of prepared multimedia materials” (p. 128), and this pushes the total costs of online Class IV systems above those of correspondence (Class I) and multimedia (Class III) systems. One of the interesting features of electronic moderating could be the moderator’s experience of the time it takes to support students electronically. In face-to-face tuition there is a clear cost control mechanism in place—the timetable. The same is not true of online teaching, where the pressure is to respond to students’ queries rapidly and individually. Tolley (2000, p. 263) recounts her experience as a tutor on the “correspondence” and “online” versions of an Open University course. On the version of the course with scheduled tutorials, she estimated she spent 42 hours (10 hours preparation, 17 hours teaching, 15 hours sent preparing and sending tutorial related mailings), though this excluded her (unpaid) travel time to the tutorials (12 hours) and the unrelated time she spent marking assignments. On the online version, she spent 120 hours excluding assignment marking. In other words, her workload more than doubled. In addition, working online had a “dramatic effect” on her telephone bill. Crucially she was not paid for the additional time she spent. When tutors begin to demand to be paid for the increased workload, some chickens may come home to roost, either in demands for increased pay, or in a reluctance




to take the job. This suggests that the first of Bates’ claims is debatable—and that the costs of online teaching may be more expensive than costs for Class I, II, and III systems. Annand (1999) suggests that it is these costs that may in the end constrain the extent to which large-scale distance teaching universities can adopt online technologies. Arizona Learning Systems (1998, p. 20) reports that “All providers of Internet courses . . . have reported that this direct communication [between teachers and students] takes more time than preparation and delivery of a classroom lecture and the corresponding contact with students.” These faculty workload costs have pushed the typical direct cost per course enrollment of an Internet course (US$571) above that of traditional classroom instruction ($474), but they suggest that faculty workload will be reduced through improved support and processes. Arizona Learning Systems projects that measures such as the development of academic help desks could result in unit costs falling to $447 (1998, p. 7). In some cases colleges have restricted course enrollments in order to bring instructor time down (1998, p. 22). Arizona Learning Systems (1998, p. 24) suggests that the average cost per course enrollment should fall as enrollments rise. For a simple text course unit costs would fall from $782 per enrollment with 10 students to $453 with 500 enrollments, and for a multimedia course with images, the cost per enrollment would be $1,496 with 10 students, falling to $467 with 500 students (1998, p. 24). Second, it may be that flexible learning strategies within campus-based systems actually yield a more cost-efficient option than pure distance teaching systems—particularly those that use expensive media mixes (i.e., Class II and III systems). There is considerable scope here for further research, but as Rumble (2001) shows, there are very significant areas of cost that need to be taken into account, and the best strategy for decision-makers at this point in time is to treat any suggestions that on-line teaching will bring the costs of education down with considerable caution.

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48 Cost-Effectiveness of Online Education Insung Jung Ewha W omans University,Korea insung

COST-EFFECTIVENESS OF EARLY DISTANCE EDUCATION There is a relatively large body of literature discussing the costs and benefits of distance education across technologies and in a variety of contexts. In general, the literature has shown that “distance education can be more cost-effective than face-to-face education and that costs are predominantly dependent upon student enrollment and the fixed costs of course development and delivery” (Cukier, 1997, p. 138). Capper and Fletcher (1996) analyzed previous studies on cost-effectiveness of distance education and identified factors influencing costs in distance education. Those factors include number of courses offered (since the cost of developing a course is one of the major expenses in distance education, the most cost-efficient approach is to offer fewer courses for larger numbers of students), frequency of course revision, type of media used, type and amount of student support, and attrition rate. They concluded that even though cost-effectiveness of distance education is supported in most of the studies, costs vary substantially from one situation to another and are influenced by a number of factors. Generally cost-effectiveness of distance education increases as the number of students increase and the number of courses declines. A substantial number of studies analyzed in Capper and Fletcher’s (1996) report supported cost-effectiveness of distance education. A study that was conducted in Sri Lanka showed that distance education was by far the most cost-effective—4.5 to 6 times more cost-effective than residential training programs offered in colleges of education or in in-service teacher training programs. The main reason for this cost-effectiveness of distance education was that the teachers in the distance education programs continued with their full teaching loads, whereas the other groups did not. As appeared in this study, savings on salary costs and travel costs for program participants have been reported as one of the main sources of cost-effectiveness of distance education. There were cost-effectiveness studies that focused more on effectiveness of distance education than on the costs and analyzed general cost-effectiveness of distance education via 717



various technologies. Early cost-effectiveness studies on videoconferencing reported substantial cost-benefits (Showalter, 1983; Hosley & Randolph, 1993; Trevor-Deutsch & Baker, 1997). Even though its costs were higher than other classroom-based programs, interactive satellite-delivered training courses were found to be cost-effective due to increased enrollments, increased student access to quality programs and resources, and other benefits (Ludlow, 1994). Hall (1997) compared CD-ROM–based training to classroom-based training in a high-tech company and reported that over the 3-year pilot period, costs for the CD-ROM–based course were 47% less than those for classroom-based courses. Moreover, the improved instructional design, a variety of instructional models, and other strategies contributed to more effective learning and reduced training time. After analyzing a series of studies on cost-effectiveness of distance education, Moore and Thompson (1997) found that cost-effectiveness depended more on costs in relation to education value, rather than on costs alone. Moreover it is indicated that as technologies are rapidly evolving and costs related to these technologies also change drastically, difficulties arise in predicting costs of a certain technology. Thus, as Hezel (1992) has suggested, a question of “is the educational outcome worth the cost?” is more appropriate than the question of asking comparative costs between distance education and traditional face-to-face education. With these considerations in mind, Moore and Thompson (1997) reported several studies on cost-effectiveness of technologically mediated instruction using various technologies in a variety of contexts. As early examples of the studies, reports of Christopher (1982) and Showalter (1983) were analyzed. Christopher found that the Teleteach Expanded Delivery System was more cost-effective than resident instruction for providing training to Air Force students at remote sites. Showalter reported a 55% cost-benefit in delivering continuing education to professionals via an audioconferencing system. The cost-benefits of audioconferencing were also reported in some other studies in K-12 context (Schmidt, Sullivan, & Hardy, 1994). In addition, studies that specifically compare cost-effectiveness of a distance education course via videoconferencing to a traditional classroom-based course were reported. Those studies emphasized substantial savings through decreased travel costs by bringing training to the workplace (Moore & Thompson, 1997). While these studies are useful in providing a comparative look at identifying the costs and effectiveness of media-mediated courses, not much research has been conducted to assess costeffectiveness of online education. Even in the studies of cost-effectiveness of online education, “costs of development or costs born by students” are often excluded, and “these studies often use competing methodologies, making them difficult to compare” (Bakia, 2000). Because of the relatively small number of studies and methodological limitations of cost-effectiveness studies, findings from these studies need to be viewed as suggestive rather than definitive.

COST-EFFECTIVENESS OF ONLINE EDUCATION Many educators or decision makers believe that the primary benefit of online education is that costs can be distributed over a large number of students, resulting in economies of scale for educational institutions (Kearsley, 2000; Inglis, 1999; Whalen & Wright, 1999). It is assumed that large student enrollment would increase revenue and lower the cost per student and operating expenses. While the possibility of reducing the costs appears to be one of the main factors that motivates decision makers to adopt online education, two other factors also seem to be important: improving the quality of students’ learning experience through various types of online interaction and increasing access (Inglis, 1999). From the student’s perspective, online education




means increased opportunities for interaction with other students and instructors and for wider access to a variety of multimedia resources and experts worldwide. As discussed in several articles (Relan & Gillani, 1997; McDonald & Gibson, 1998; Salmon, 1999), online technologies are known to be capable of providing an interactive learning environment that supports people in communicating with others in different places and time zones to fulfill their education or training needs. The Internet, as one of these online technologies, is viewed as an innovative distance education approach for delivering instruction to learners in different places and/or different times and for improving learner-learner, learner-instructor interaction. Related research and case studies show that a virtual education via the Internet provides an opportunity to develop new learning experiences for learners by managing self-directed learning and sharing information and ideas in a cooperative and collaborative manner (Hiltz, 1994; Daugherty & Funke, 1998; Jonassen, Prevish, Christy, & Stavrulaki, 1999). Cukier (1997) argued the importance of including educational values of online education such as increase in educational access and improvement in interaction among learners in analyzing cost-effectiveness. She summarized four of the cost-benefit methodologies examined in the previous studies and provided an integrated methodology for the cost-benefit analysis of network-based learning. Four approaches to cost-benefit analysis include a value-based approach, a mathematical modeling approach, a comparative approach, and a return on investment approach. A value-based approach considers the pedagogical needs and values of an educational institution in analyzing cost-benefits of online education. For instance, an educational institution that sees small-group interaction as important learner experience will be more likely to view interaction as a benefit to be analyzed whereas an institution whose goal in adopting online education is to reach as many students as possible will view expansive delivery and limited interaction as benefits in introducing online education. A mathematical modeling approach focuses on the costs and benefits that can be easily quantifiable. For example, a study that examines both the costs and benefits of videoconferencing used in two different ways will be interested in cost assessments for the teleconferencing, the costs savings resulting from remote delivery in two ways (where the instructor travels to the students and where the students travel to the instructor), and benefits of each method. In this study, cost-benefits of videoconferencing in two different delivery situations will be quantified for comparison. Cukier (1997) explains that a comparative approach can be used in a situation when the same course is delivered using different technologies, for example, comparing online education with traditional face-to-face instruction. A return on investment approach attributes an economic value to benefits and seeks to measure monetary gains of adopting a new medium as a delivery means. The proposed approach to cost-benefit analysis of online education, called an integrated approach, focuses on integrating major concepts in these four previous approaches. When this integrated approach is adopted, analyses of costs must address categories of capital and recurrent costs, production and delivery costs, and fixed and variable costs. And when estimating benefits of online education, performance-driven benefits such as learning outcomes, cost savings, students/teacher satisfaction, and opportunity costs; value-driven benefits such as flexibility, access, interaction, user-friendliness, and adaptability of materials; and value-added benefits such as reduction in capital investment, reduction in pollution, increased job creation, new business opportunities, reductions in social community costs, and creation of secondary markets must be analyzed. Cukier emphasized that the analysis of costs and benefits should be conducted separately and the approach should be multileveled. But costs and benefits will ultimately be evaluated subjectively. Based on Cukier’s (1997) frameworks of cost-benefit analysis, six case studies have been conducted by the NCE-Telelearning project team in Canada and two of them are available



online. Cost measures assessed in the two case studies (Bartolic-Zlomislic & Bates, 1999; Bartolic-Zlomislic & Brett, 1999) include 1) capital and recurrent costs, 2) production and delivery costs, and 3) fixed and variable costs. The cost structure of each technology is analyzed and the unit cost per learner is measured. The costs assessed in Bartolic-Zlomislic and Brett’s study did not include overhead costs as these were unknown. Benefit data include 1) performance-driven benefits, 2) value-driven benefits, and 3) societal or value-added benefits. Both quantitative and qualitative data were collected and included students, faculty and staff, and administrator perspectives. A case study by Bartolic-Zlomislic and Brett (1999) analyzed costs and benefits of an entirely online graduate course at the Ontario Institute for Studies in Education of The University of Toronto in changing the software from Parti, a UNIX-based mail and conferencing software, to WebCSILE, a Web-based software. The results of the study project that their online program will make a small notional profit of $1,962 (Canadian currency) per year during five years and 19 students will be needed to break even. It concludes that it is possible to develop highly cost-effective online courses within a niche market, at relatively moderate cost to learners. It also recognizes that despite the change in software from Parti to WebCSILE, the largest cost of the online course is tutoring and marking time spent by the instructors due to the nature of the course that emphasized active online discussions. These costs could be lowered if the format of the course was changed to less constructivistic environment. The instructors and students reported that additional skills to the contents of the course were learned, such as computer and writing skills. A case study from the University of British Columbia (Bartolic-Zlomislic & Bates, 1999) also reported similar results. The researchers found that the annual break-even enrollment based on the projected costs and revenues over 4 years was 44 students. The paper by Inglis (1999) is an attempt to examine the costs of shifting from a printbased course to an online course and to seek the rationales for moving to online delivery. Inglis showed that online delivery was less economical, when measured on a cost per student basis, than print-based deliver for four different intake levels (50/100/150/200 students). The distribution costs (such as ISP charges and individual support) for online courses represented a major component of overall costs. The author predicted that while there is an appreciable likelihood that the costs of mounting the subject online would be considerably higher than the estimates given in this paper, the likelihood of the costs being lower is small. The results of this study, in part, reflect the fact that in traditional print-based distance education most of the economies of scale that are obtainable in the design, development, and delivery stages have already been obtained. Several strategies to balance costs with benefits in online education are suggested. There are other empirical studies that specifically compare the cost-benefits of an Internetbased distance course to traditional face-to-face courses. A study conducted by the Rochester Institute of Technology compared the operational costs of asynchronous instruction using a variety of online technologies including e-mail, Internet, Web materials, and telephone conferencing in traditional classrooms and distance courses. Given the exclusions of planning and production costs and investments in technical infrastructure, the study reported costeffectiveness of asynchronous instruction used in distance courses. It also found that faculty used equal or more time in distance courses and reported using their time differently (Bakia, 2000). Another study conducted by Whalen and Wright (1999) reports that Web-based training has higher fixed costs than classroom-based training but these higher course development costs are offset by lower variable costs in course delivery. In general, Web-based training is more costeffective than classroom teaching mainly due to the reduction in course delivery time and the potential to deliver courses to a larger number of students in Web-based training. Asynchronous teaching on the Web showed cost-effective compared with synchronous teaching on the web




because of the cost of having a live instructor and the greater student salary costs due to the extra time required to deliver the course. Also, the online education platform costs affected cost per course due to the different license fees and upgrading costs across the platforms. The amount of multimedia content in the courses was a significant factor in costs. A report of cost-effectiveness of online courses in Korea National Open University (Jung & Leem, 2000) shows that the development and delivery costs for online education decrease over time (cost per online course was US$12,768 in 1998 and US$7,902 in 1999). And when compared with a traditional distance education course that used TV and textbook, an online course had higher completion rate (55.2% in the traditional course and 93.1% in the online course) and thus lower cost per completer. The students in two different courses show significant differences in learning achievement and technology literacy level. While the studies reviewed above provide some ideas about cost-effectiveness of online education, we still need “a firm understanding of the cost-drivers” (Bakia, 2000, p. 52) of online education programs and more rigorous effectiveness data in various learning contexts to make a firm conclusion on cost-effectiveness of online education. From the studies reviewed in this section, we understand that the scale, design, and production quality, an institution’s pedagogical value, and rapidly changing costs of hardware and software all influence costeffectiveness of online education. Some studies have focused on identifying more specific factors affecting cost-effectiveness of online education. The following section introduces their tentative findings. Factors Affecting Cost-Effectiveness of Online Education After analyzing previous studies on cost-effectiveness of ICT in higher education, Bakia (2000) concludes that “the most obvious obstacles (in implementing online education in developing countries) include prohibitive internet connection costs and inadequate technical infrastructures. Several factors suggest that the use of ICT in education, at least in the short-term, will be relatively more costly in developing countries, even if Internet access were readily available and affordable” (p. 52). Besides factors associated with technical infrastructure, several other factors that affect cost and/or effectiveness of online education are identified in previous studies.

r Number of students in a course (Capper & Fletcher, 1996) r Number of courses offered (Capper & Fletcher, 1996) r Amount of multimedia component in online courses (Whalen & Wright, 1999) r Amount of instructor-led interaction (Whalen & Wright, 1999; Inglis, 1999) r Type of online education platforms (Whalen & Wright, 1999; Inglis, 1999; BartolicZlomislic & Bates, 1999; Bartolic-Zlomislic & Brett, 1999)

r Choice of synchronous versus asynchronous online interaction (Whalen & Wright, 1999) r Completion rate (Jung & Leem, 2000) Moreover, some cost-saving strategies were identified in case studies. Online education systems often require a huge database system of online courses and materials. Since the cost of developing a database is high, most online education institutions have experienced financial difficulties in establishing a large database for their students. As a strategy to reduce the cost in operating online education, many institutions have “unbundled” educational functions— such as online course development, distribution, tutoring, assessment, general administrative affairs and learner supports (Farrell, 1999)—which are increasingly shared among specialized institutions. Unlike analog systems, digital databases can be linked through computer networks, shared globally, revised by users, and then transformed into meaningful knowledge. The Cyber Teacher



Training Center in Korea, for example, is establishing a database of online teacher training programs in cooperation with other Korean teacher training institutions. Online training programs in this database can be used, revised, and implemented in different ways by different centers, and sharing allows each training center to reduce its costs for program development (Jung, 2000). Another example is the Instituto Tecnol´ogico y de Estudios Superiores de Monterey (ITESM) in Mexico. ITESM is a 27-campus university system with more than 78,000 students throughout Mexico and Latin America. Using the IBM Global Campus—an integrated system that provides Internet access, tools to design online courses, and other databases—ITESM draws on resources outside its system and offers more than 2,500 distance learning courses to students throughout the hemisphere (World Bank, 1998). With the Internet and a comprehensive intranet connecting its campuses and a distributed learning system called Lotus LearningSpace, educational resources developed by each instructor can be shared, students can interact collaboratively with other students and have direct access to instructors as well as library resources, and instructors can update their courses as needed. By sharing educational resources and providing additional classes and curricula without incurring the capital investment costs of building new campus facilities, immediate savings were reported. Partnerships reduce the burden to online education institutions by distributing costs across partners. An example of a sound partnership is the one between Boston College and the government of Ireland (Jung, 2000). In collaboration with other business partners, these two entities are developing technology resources for both K-12 and higher education in a project called Schools IT2000. This project aims to integrate ICTs into Ireland’s school system; Boston College provides much of the infrastructure and creates curricular materials, and Telecom Eireann provides Internet access (Oblinger, 1999). By forming appropriate partnerships with businesses, online education institutions diminish their investment risks. Collaborations with education institutions can also be mutually advantageous by permitting the exchange of technology and human resources and the sharing of courses. Each institution can develop online courses in its areas of specialization and exchange access to those courses with its partner institutions. Partnerships can also be formed with education institutions or companies in foreign countries. Cost-effectiveness of online education can be achieved either by reducing the costs or improving the effectiveness of online education. Recent studies in the cost-effectiveness of online instruction seem to focus more on identifying factors affecting learning process, satisfaction, and achievement in online instruction than on comparative cost-effectiveness of online courses over traditional courses. Instructional design, social, and students’ personal factors have been identified as three major factors contributing to success in online learning (Jung & Rha, 2000). Instructional design factors such as flexible course structure, quick and frequent feedback, visual layouts, and multiples zones of content knowledge influenced online interaction and learner satisfaction (Vrasidas & McIsaac, 1999; McLoughlin, 1999), and thus improved effectiveness of online education. For instance, McLoughlin (1999) attempted to present an approach to the design of a culturally responsive Web environment for Indigenous Australian students and to illustrate how cultural issues and decisions were incorporated into pedagogical design of an online course. He found that design strategies such as providing the multiples zones of content knowledge, adopting participatory course structure, and creating dynamic online learning communities were effective in improving students’ learning and satisfaction. Social factors also affect the effectiveness of online learning. Anderson and Harris (1997) identified factors predicting the use and perceived benefits of the Internet as an instructional tool. Interpersonal interaction among learners and social integration were among the most influential factors. This result is supported by another study conducted by McDonald and Gibson (1998). In addition, the study of Gunawardena and Zittle (1997) reveals that social




presence exhibited by participants contributed more than 60% of learner satisfaction with computer conferencing courses. Specifically, Gunawardena and Zittle examined how effective social presence—the degree to which a person is perceived as real in mediated communication environment—as a predictor of overall learner satisfaction in a computer-mediated conferencing system. Extensive analyses of previous studies on social presence theory were provided at the beginning of the paper. They also provided construct validity and internal consistency of the instrument that was used in the study and contained 61 items measuring social presence, active participation in the conference, attitude toward computer-mediated communication, barriers to participation, confidence, perception of having equal opportunity to participate in the conference, adequate training, technical skills and experience using conference, and overall satisfaction. The results of the study reveal that social presence contributes about 60% of learner satisfaction with computer-conferencing courses. It is suggested that design strategies that enhance social presence need to be integrated in computer-mediated learning environments in order to improve the effectiveness of online education. Students’ personal factors also play an important role in online learning. For example, students’ prior knowledge with technology or subject affected learning in online courses (Limbach, Weges, & Valcke, 1997; Wishart & Blease, 1999; Hill & Hannafin, 1997). Limbach, Weges, and Valcke (1997) conducted two studies in law content domain to explore a relationship between certain student characteristics and the preference for a specific study mode in print-based and in electronic learning environments. The results of the first exploratory research identify that about 75% of the students preferred a theory-based study mode and this preference seemed to be related to higher learning experience with this study mode. The second experimental research shows that even though the students indicated a more diverse preference for certain study modes in contrast with the first research, there was more preference for the theory-based study mode in a printed delivery learning environment mainly due to the greater experience and prior knowledge students had with this approach. This article clearly indicated that it is desirable to consider student variables in designing distance learning environments to provide students with the possibility to opt for a specific study mode and delivery mode and thus to improve effectiveness of distance education. In addition, Biner, Bink, Huffman, and Dean (1995) found several personality factors such as self-sufficiency, introversion, and relative lack of compulsiveness were related to achievement among the telecourse students. Learners being autonomous individuals constructing their own knowledge (Laffey, Tupper, Musser, & Wedman, 1998; Bullen, 1998; Naidu, 1997; Jonassen et al., 1999) and being actively involved in their learning (Shneiderman, Borkowski, Alavi, & Norman, 1998; Hillman, 1999) also tended to maximize their own learning. Given the fast development of information and communication technologies, we can expect that online technology will bring changes in forms of teaching-learning and educational institutions at all levels throughout the world. It is thus important for educators and policymakers to understand the factors affecting effectiveness so strategies can be appropriately explored to improve overall cost-effectiveness of online education.

FUTURE DIRECTIONS As indicated above, a relatively small number of studies have been conducted to investigate cost-effectiveness of online education. Moreover, there are methodological limitations of those cost-effectiveness studies so that the findings from these studies need to be viewed as suggestive rather than definitive. More valid and reliable empirical data are needed on issues of costs and learning improvement for definite conclusions on the cost-effectiveness of online education. Some specific



questions for future studies on cost-effectiveness include:

r Does standardization of the online program format reduce costs without diminishing the quality of education and/or decreasing online interactions?

r How much can online resource sharing improve the cost-effectiveness of virtual education? How do different design strategies of online courses affect cost-effectiveness?

r What are possible ways of improving cost-effectiveness, while maintaining high interactivity?

r How can economies of scale be achieved in specific contexts? r How often must online education courses be updated or revised to maximize costeffectiveness? The increased number of technology options have brought more opportunities than before for distance education. Online education programs offer possibilities that would not otherwise be available because of costs, time, or location constraints, especially to working adults. In addition, traditional institutions that have never provided distance education are now able to use online technologies to increase the flexibility and openness of their programs. Even though most agree that advanced technologies have made education and training more flexible and open, many learners still are unable to access the necessary technologies. There is a fear that the gap between the “haves” and the “have-nots” has widened and continues to do so. Issues of removing or lessening the disparity of access need to be addressed in cost-effectiveness studies of online education. Educators and researchers must also continue to explore more sophisticated means of improving quality and cost-effectiveness of online education. In this regard, future studies should address areas such as:

r Instructional strategies: What are the effective design strategies to help learners maintain and manage their learning goals and processes while browsing online resources?

r Strategies for active involvement: How can we assist learners to more actively process information and construct meaningful knowledge?

r Motivational strategies: How can virtual education motivate the learner? r Strategies for guidance and feedback: What are the most effective and efficient means of providing guidance and feedback to learners during their learning process?

r Testing strategies: What are the most effective testing strategies in virtual education to ensure that learners have integrated the designed knowledge and skills? Some of these questions have been answered. For example, in comparing two different instructional design strategies for Web-based training courses for corporate employees in Korea, Jung and Leem (1999) reported that a Web-based course that adopted design strategies to provide specific guidelines to self-directed learning appeared to be more effective than a course that provided a more open-paced problem-based learning environment. The Web-based course, which presented content in small chunks, provided specific guidelines to help learners manage their everyday learning schedule and provided opportunities for self-examination through various types of checklists. Its completion rate was 93.4% and the average grade was 85%. In another Web-based course, each learner was asked to solve authentic problems individually, using various online resources. Later, students collaborated with other learners to improve individual solutions. The completion rate for that course was 72% and the average grade was 62%. It was determined that a course that required active online discussion and individual research for Web resources without specific guidelines was somewhat inappropriate in a corporate training context in Korea.




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49 A Comparison of Online Delivery Costs with Some Alternative Distance Delivery Methods Alistair Inglis Victoria University ofTechnology

There is a great deal of interest, both within institutions and within the higher education sector generally, in how the costs of online delivery compare with the costs of well-established methods of delivery. This is apparent from the number of studies being carried out at the institutional level and within the sector (Bacsich et al., 1999; Bartolic-Zlomislic & Bates, 1999a, 1999b; Inglis, 1999; Jewett, 1998; Morgan, 2000). Understandably, institutions should be interested in the impact that the shift to online delivery is going to have on costs, given the pace at which that change is occurring. However, coming to an understanding of how the costs of the new methods of delivery compare to the costs of existing methods of delivery involves more than keeping account of actual costs. It involves gaining an understanding of the factors that have the capacity to have a major impact on cost relativities and understanding the extent of that impact. Through achieving such an understanding it is then possible to anticipate how particular changes in a delivery model are likely to impact overall costs of delivery and therefore the viability of programs. Without that understanding, detailed information on the actual costs of individual programs may simply lead to greater confusion.

THE DRIVE FOR INCREASED PRODUCTIVITY The importance that educational institutions are placing on developments in the areas of online learning, at least at the higher education level, is indicated by the degree of interest that has been shown in the National Learning Infrastructure Initiative (NLII) that has been sponsored and promoted by EDUCAUSE (formerly EDUCOM). The rationale for the NLII was initially set out in a white paper that explained the necessity for taking a more systemwide approach to use of new learning technologies in terms of the economic imperatives facing educational authorities (Twigg, 1994). 727



The case that Twigg presented for making the shift to new learning technologies was based on the need for future governments to achieve economies in the provision of postsecondary education. Twigg argued that the growth in population and the disappearance of old jobs and the creation of new jobs would lead to a substantial growth in lifelong learning. This increase in demand for postschool education would place so much economic pressure on governments that they would be forced to respond by looking for ways to cut the cost of education. Twigg’s analysis is supported by others who have studied the economics of higher education (see, for example, Arvan, 1997). The National Learning Infrastructure Initiative was put forward as the answer to achieving more with less. TWIGG’S MODEL Twigg argued that the ways to reduce costs are to reduce the need for direct faculty intervention and to make savings in buildings and plant. Both of these outcomes could be achieved, she said, by increasing students’ abilities to locate and use learning resources. Twigg argued that, when implemented, the National Learning Infrastructure would “increase access (via the network), improve quality (through the availability of individualised interactive learning materials) and contain costs (by reducing labor intensity in instruction)” (Twigg, 1994). The means by which Twigg saw savings being achieved was not, therefore, simply through a shift from face-to-face to online learning, but more particularly through a shift from classroombased to resource-based learning. This argument is spelt out in much greater detail in Twigg (1996). THE DISTINCTION BETWEEN CLASSROOM-BASED AND RESOURCE-BASED MODELS OF COURSE DELIVERY Inglis, Ling, and Joosten (1999) pointed out that distance education programs, including distance education programs delivered online, differ according to whether they adopt a classroombased or a resource-based model of delivery. Classroom-based learning is learning that takes place through dialogic interaction between student and tutor and student and student. Resourcebased learning is learning that takes place through interaction between the student and selfpaced instructional materials (see Table 49. 1). Most examples of distance education programs combine elements of both classroom-based and resource-based learning. However, it is the manner in which these types of learning are combined that determines the model of delivery that is being used. In the case of the classroom-based model, learning is centered around group activities. Such resource materials as are used serve the purpose of supporting and extending those group activities. In the case of the resource-based model, learning occurs through interaction between the learner and the learning materials. TABLE 49.1 Examples of Delivery Methods Employed in Classroom-Based and Resource-Based Models of On-Campus, Traditional Off-Campus, and Online Modes Classroom-Based On-campus Traditionaldelivery Onlinedelivery

Tutorials Seminars Audio teleconferences Videoconferences Asynchronous learning networks

Resource-Based Computer-assisted instruction Computer-managed learning Print-based self-instructional packages Broadcast television and radio Web-based delivery Multimedia packages Streaming video and streaming audio




The reason for drawing this distinction between classroom-based and resource-based models of delivery is that the differences between these two models are critical to the economics of distance education delivery. It helps in understanding the economics of online delivery if one first has a grasp of the way in which the economics of teaching at a distance has served to shape the earlier history of distance education. THE EXAMPLE SET BY THE UK OPEN UNIVERSITY The successful establishment of the UK Open University marked a watershed in the development of higher education. Prior to the establishment of the Open University, distance education programs had been characterized by high failure and dropout rates. The Open University was founded on a vision of offering mature-aged adults from lower socioeconomic groups who had been deprived of the opportunity of gaining a university education a second chance. The Open University used a mode of teaching that was heavily resource-based. The model that the university adopted combined the use of correspondence material, television and radio broadcasts, face-to-face tuition at local study centers, and residential schools. This model was chosen because the students for whom the university would be catering were expected to be working while they were studying and therefore would not be able to attend daytime classes. However, the use of broadcast television and high-quality print packages involved substantial development costs. Nevertheless, the university recognized that its resource-based delivery model also offered considerable potential for economies of scale and that these economies were capable of yielding considerable savings in recurrent costs for the university compared with conventional universities. (Wagner, 1972).

THE IMPORTANCE OF ECONOMIES OF SCALE The costs of any productive activity may be subdivided into costs that do not increase with the unit of output and costs that do. The former are termed fixed costs and the latter are termed variable costs. In education, examples of fixed costs include the costs of institutional infrastructure such as buildings and plant and the costs of courseware design and development, while examples of variable costs include the cost of labor associated with tutoring, student support and assessment. Economies of scale are obtained by spreading the fixed costs over a larger student intake. Ashenden (1987) pointed out that opportunities for obtaining economies of scale arise at two levels. At the course level, economies of scale can be obtained by spreading the fixed costs associated with the design and development of courseware across a larger course intake. At the institutional level, economies of scale are obtained by spreading the costs of the institutional infrastructure needed for delivery of programs across a larger distance education cohort. While Ashenden was referring to the type of print-based distance education practiced in Australia, the pattern he described is capable of being mapped onto cost structures for resource-based online delivery. As in the case of printed-based delivery, economies of scale can be obtained at the course level by spreading the fixed costs of design and development of the Web-delivered resource materials across a larger course intake. Meanwhile, economies of scale can be obtained at the institutional level by spreading the costs of information and communications technology infrastructure needed to support online delivery across a larger total online enrollment. The immediate success of the UK Open University led to many other countries around the world establishing national single-mode distance education universities based on the Open University model. However, two countries that stand out as not having followed this trend are Australia and the United States.



In Australia, a well-developed system of off-campus education was already in existence at the time that the Open University was established. Even so, a major government inquiry was launched to assess whether an open university should be established in Australia (Committee on Open University to the Universities Commission, 1974). This inquiry recommended against the establishment of an open university and recommended instead the establishment of a National Institute for Open Tertiary Education as a statutory body and the establishment a new regional university with special responsibility for open education. Due to the economic conditions that developed shortly afterward, neither was established. This left the way open for the newly established colleges of advanced education in Australia’s dual-sector higher education system to fill the gap in the market. By the early 1980s more than 40 institutions were operating in distance education and there was widespread duplication of courses. Most institutions were unable to capture a sufficient portion of the market to operate efficiently. Recognition by the statutory authority that was then responsible for overseeing the funding of the higher education sector, the Commonwealth Tertiary Education Commission, of the inefficiencies that such a dispersed system created led to a decade of research into the costs of distance education. At the end of this period, the Australian government legislated to rationalize distance education. However, it did not reduce the number of providers to one. The work by Ashenden’s (1987) investigation demonstrated that by adhering to the production values that were accepted in Australia institutions could operate cost-effectively with average course intakes of 50–150 and average total intakes of 3,000. The government therefore designated 8 Distance Education Centres, involving a total of 10 universities. The fact that the United States did not follow other counties in setting up a national university dedicated to distance education can be explained in cultural terms. The strong tradition that existed in the United States of school leavers moving away from home to begin higher education meant that there wasn’t the level of unmet demand for undergraduate places that fuel the growth of distance education elsewhere in the world. When educational institutions in the United States began to move into distance education, they did so not by adopting the resource-based learning model that had been adopted elsewhere in the world, but by extending the classroom-based model beyond the walls of the institutions using the power of the communications media (Twigg, 1996). They created a “remote classroom” model based on two-way videoconferencing and one-way audio, two-way video (Daniel, 1998). Because of the strong U.S. economy, institutions were not faced with such cost constraints as universities elsewhere. Institutions took advantage of economies of scale to recoup the substantial investment costs needed to implement distance education by this mode. However, most did not take the second step of trying to reap economies of scale through a shift to resource-based learning. Twigg’s argument has carried such weight in the U.S. context because of the difference described between characteristics of the U.S. higher education system and the higher education systems in other countries. Twigg was not the first person to campaign for the adoption of online delivery on economic grounds. A much earlier proponent of technology was Murray Turoff.

TUROFF’S MODEL Murray Turoff has been one of the pioneers of computer-mediated communication and is widely regarded as the “father of computer conferencing.” While working in the office of the president of the United States in the 1960s, he was responsible for the development of the first publicly used computer conferencing system. As professor of computer science at the New Jersey Institute of Technology, he went on to establish the Electronic Information Exchange




System (EIES), a system that was used in industry and education. He has continued to make a substantial contribution to the research into the use of computer-mediated communication. In 1982 Turoff proposed the establishment of a new institution that would teach online and developed a costing model to demonstrate the viability of the proposal; he has regularly updated that model since (Turoff, 1996). Given Turoff’s long-standing advocacy of the role of computer conferencing in business and education, it is only to be expected that the type of virtual classroom he would advocate is one based on the classroom-based model. Turoff argues that in this fast-paced world, by the time that subject matter is sufficiently well understood that it can be presented in the form of learning packages, it is too out of date to be relevant at university level (Turoff, 1997). Many distance educators would not agree with Turoff’s assessment of the limitations of learning packages. The pace at which disciplinary knowledge is expanding varies greatly from discipline to discipline. Even in those disciplines where progress is rapid, distance education providers have found ways of keeping up with development through the implemented justin-time methods of production. Twigg (1994) argued that what students need to learn is the means by which disciplinary knowledge is accessed. If that is accepted, then the pace at which what students are needing to learn is changing is by no means as rapid as Turoff has claimed, even in disciplines such as computing and law. Nevertheless, it is important to be aware of the philosophical assumptions Turoff makes in order to understand the basis upon which his costing model is derived. Given what has been said above about the way in which economies of scale are obtained, one would not expect substantial savings to be generated through the adoption of Turoff’s model, and indeed Turoff makes no such claims. In setting out his costing, Turoff was not trying to show that it was possible to make substantial savings through making the shift from campus-based to online delivery but that it was possible to deliver programs online at a cost commensurate with delivering the same programs face-to-face. Turoff believed that the purpose of using technology in the delivery of programs in higher education should not be to enable larger class sizes to be supported, nor to increase efficiency, but rather to improve the effectiveness of teaching. Turoff did not seek to achieve savings by such means as relegating teaching to low-paid instructors or by limiting the extent of instructor contact. To ensure that the quality of faculty would be on average higher than in existing institutions, he proposed that instructors be paid generously in comparison with those offered by traditional universities. However, the salaries to be paid to staff are not quite as generous as they appear at first glance, because instructors would be required to provide their own computers, scanners, and pay the costs of communication and well as the costs of development of learning materials. Where savings are achieved in Turoff’s model is in the institutional infrastructure. Computer and communications costs are kept low, principally by requiring students to accept most of these costs. Turoff argued that the $15–20 (U.S. dollars) cost of unlimited network access compared favorably with the cost of travel to a college over a significant distance or the cost of room and board to live on campus. There is already some evidence emerging that students’ patterns of attendance at universities are changing as they recognize that the need for attending on campus is diminishing. However, there is a danger in assuming that what applies to some students applies to all. Interestingly, Turoff’s decision to shift the use of funds from the physical plant to faculty also largely eliminates any opportunities that might otherwise have existed for obtaining economies of scale. Twigg, on the other hand, was predicating her case on the assumption that governments would need to reduce the costs of education. The creation of the National Learning Infrastructure was her solution to how this could be achieved. However, the way in which these savings were to be achieved was through economies of scale. Use of the new learning technologies is



the means by which the shift to resource-based learning is accomplished. The argument Twigg advanced for the National Learning Infrastructure was in essence no different from the argument made two decades earlier by champions of the Open University model. That being the case, it is worth asking whether even greater savings could be achieved by switching to print.

THE EDUCATIONAL RATIONALES FOR ALTERNATIVE MODELS From what has been said above, it should now be evident that the more marked the move to resource-based delivery, the greater the potential for achieving economies of scale and the greater the scope for cost savings. Yet it is important also to consider this question: What effect does changing the mix have on the educational quality of the programs being delivered? The models proposed by Twigg and Turoff represent the opposite ends of a continuum that ranges across various combinations of classroom-based and resource-based components. In considering how these two complementary approaches to delivery might be best applied, it is necessary to take into account the educational rationales for choosing one or another approach or a combination of the two. Feenberg (1999) has characterized the difference between these two alternative models of online distance education as being between ‘automating and informating’ (Feenberg, 1999, p. 2). In describing the difference in this way, Feenberg tries to accentuate the difference between an emphasis on costs and an emphasis on quality. Feenberg acknowledges the opportunity that exists with what he terms an “automated” system to obtain economies of scale but argues that courses produced by a live teacher, which have the advantage of enabling learners to engage actively in dialogue, will be designed in relatively simple and flexible formats. However, portraying the resource-based learning model as an automated model is something of a shibboleth. Almost nowhere in the world has implementation of the resource-based model been seen to obviate the need for student interaction. In the UK Open University, local study groups is a key feature while in Australia, teletutorials, weekend residential schools, or residential schools are key components. Feenberg acknowledges that prepackaged computerbased materials will supplement the teacher. In acknowledging the legitimacy of the use of these materials, Feenberg accepts that there will be components of either delivery model in which the potential for economies of scale will exist and can be exploited. While some of the details of Feenberg’s argument might be questioned, the issue he raises is certainly a recurrent theme in the literature of online delivery (Harasim, Hiltz, Teles, & Turoff, 1995; Inglis et al., 1999; National Committee of Enquiry into Higher Education, 1997; Turoff, 1997). However, the conclusion this debate leads us to is not that collaborative learning is preferable to material-mediated self-instruction but that the optimum situation is represented by a combination of both approaches. The purpose to which research on comparative costs needs now to be directed is therefore to understanding how to obtain the most cost-effective combination of delivery strategies. This combination need not, of course, be limited to strategies mediated by the new learning technologies, but could also include the use of more traditional delivery media such as print, where this is appropriate.

WHY COMPARE COSTS? Research into the costs of delivery of education and training is seldom driven merely by curiosity. It is usually driven by the need of education and training managers for dependable data upon which to base management decisions. The type of research that is undertaken is therefore determined by the types of issues that are uppermost in the minds of managers. Sometimes there




may be a political element. However, more commonly the reasons are economic—education and training providers find themselves having to stretch their budgets further and are keen to learn the best way to do this. Rumble (1999) suggested that some of the reasons why educators worry about costs of online delivery include the need to understand and control costs, to identify costs in order to set prices, to demonstrate increased cost-efficiency and cost-effectiveness, and to justify projects in terms of their costs and benefits, and the fear that the overall cost will be too great for institutions or that the costs imposed on students will be too heavy. In the period immediately following the establishment of the World Wide Web, many education and training managers saw it as offering a way to achieve substantial reductions in the costs of delivery of programs. Initial interest in the costs of online learning was therefore focused on confirming that the types of savings that were being promised could in fact be obtained. However, this phase has passed. Most education and training managers now realize that the costs of online delivery are somewhat higher than at first believed. However, more importantly, they accept that online learning is here to stay and that, therefore, even if online delivery were to be shown to be not as economical as existing methods, it would still continue to evolve and would become less costly over time. Their primary responsibility as managers is therefore no longer to determine whether their organizations should be considering shifting to online delivery, but rather when and how. The management imperative in relation to online delivery is no longer to reduce costs, but rather to manage costs.

DIFFICULTIES IN MAKING COMPARISONS ON THE BASIS OF ACTUAL COSTS While it is usual to base comparisons of different modes of delivery on actual costs, this approach runs into a number of difficulties when addressing an international arena. Actual costs vary considerably from country to country but they don’t vary consistently. For example, labor costs are much higher in first world countries than in third world countries. However, the costs of technology and telecommunications are generally higher in third world countries than in first world countries. Furthermore, relative costs are not very stable, being subject to exchange rate variations that at times can be quite large. A variation in the exchange rate between two countries can therefore produce a change in a major cost component that is much larger than the total contributions of minor cost components. In coming to understand the way in which the costs of different delivery methods compare, it is therefore more important to appreciate the relative impact that different variables have on costs and the way in which they impact costs than to have a detailed knowledge of the actual costs of different cost components for a particular type of project in a particular context.

HOW SHOULD COSTS BE COMPARED? Recent studies of costs in distance education recognize the importance of adopting an activitybased costing approach (Cokins, 1996) rather than a costing approach based on line items (Rumble, 1986, 1997). However, the fact that institutions have not yet adopted activity-based accounting is one of the major factors that continues to bedevil attempts to investigate relative costs on a systemwide scale (Bacsich et al., 1999). The adoption of activity-based costing focuses attention on the differences that exist between different phases of a production cycle and numerous models have been proposed for subdividing the phases involved in the delivery of distance education programs by more traditional media (see, for example, Bates, 1995; Rumble, 1997). Bacsich and his colleagues have examined the



suitability of several models for costing online delivery and have proposed a new life cycle model comprising planning and development, production and delivery, and maintenance and evaluation phases, which they have attempted to validate by reference to panels of practitioners (Bacsich et al., 1999). In analyzing the costs of two alternative ways of going about a process it is generally of little value to compare the costs of inputs without comparing the value of the outputs. Comparing inputs without at the same time comparing outputs carries the tacit assumption that the outputs are the same or equivalent. In the case of the delivery of educational programs this is seldom, if ever, the case. Cost-effectiveness analysis compares costs with outcomes whether or not those outcomes can be measured in financial terms. Cost-benefit analysis compares costs with benefits in economic terms (Moonen, 1997). Cukier (1997) has pointed out that many published cost-benefit studies in distance education examine costs but not benefits. Studies that do not attempt to compare benefits as well as costs are of limited value in establishing the “big picture.” However, it is often the case that before and after conditions are often so different when courses are moved online that finding suitable outcome measures is often quite difficult. In the studies that Cukier reviewed, the types of benefits that were identified included cost savings, opportunity costs, or learning outcomes. Bartolic-Zlomislic and Bates (1999a), drawing on the work of Cukier, assessed the benefits flowing from a joint venture development involving the University of British Columbia and the Monterrey Institute of Technology using a range of outcome measures. These included measures of performance-driven benefits such as student/instructor satisfaction, learning outcomes, and return on investment; measures of value-driven benefits such as increased access, flexibility, and ease of use, and measures of value-added benefits including the potential for new markets. Bates (1995) argued that the basis on which costs are compared should take into account the purpose for which they are being compared. If this purpose is to decide whether to use a particular technology or if there is a fixed overall budget then it may be most appropriate to use the total cost over the whole of life of the project; if the purpose is to maximize the investment in the production of resource, then the marginal cost of increasing the amount of resource material may be the best measure; if the purpose is to recover the costs of delivery through student fees, then the marginal cost of adding an additional student may be the most appropriate measure; and if the purpose is to compare different technologies then the average cost per student hour is probably the best measure. An alternative approach to using actual costs for comparing the costs of alternative methods of delivery is to use break-even analysis (Markowitz, 1987) in which the comparison is made on the basis of the time taken to recover the initial investment. This approach is most appropriate for use in situations where all of the initial investment is being recovered through course fees and it is important to know whether investment in the initial development of the course is justified. As several authors point out, the number of studies that make any attempt to compare the costs of online delivery is small (Bacsich et al., 1999; NBEET, 1994; Rumble, 1999).

WHICH COSTS SHOULD BE COMPARED? The costs of delivery of distance education programs can be divided up in a variety of ways (Bates, 1995)—for example, into capital and recurrent costs, into fixed and variable costs, and into development and delivery costs. However, what is more important than being able to place costs into their appropriate categories is understanding how the different types of costs interrelate. For it is the ways in which costs interrelate that determines whether, in a particular set of circumstances, one mode of delivery will be less costly than another.




However, even understanding the relationship between different cost components is not, on its own, sufficient because the relationship between different types of costs depends on the measure that is used—whether costs are compared on the basis of overall costs, costs per student per workload hour, costs per student per contact hour, or some other measure. Each of these bases of comparison has the potential to produce a different result. Given this situation, the aim in comparing costs should not be to determine which is the best measure of costs, but to decide which is the best measure for the purpose at hand.

THE IMPORTANCE OF COMPARING LIKE WITH LIKE If costs are compared without also comparing benefits, a tacit assumption is made that the delivery methods being compared yield the same or similar benefits. However, the more two delivery methods differ, the less it is likely that the benefits will be equivalent. It is not hard to imagine how existing courses delivered in alternative modes might be directly translated into online versions. For most institutions that are currently heavily involved in distance education, direct translation of existing courses is the obvious and probably preferred first step to moving into this medium. However, simple translation of courses into the online medium is often deprecated because it makes no attempt to take advantage of the attributes of the media (Oliver, 1999). Moving into the online environment offers possibilities that are not available at acceptable cost via alternative media. These include animation, streaming audio, streaming video, and full color. Advantage can be taken of some of these options, such as color, without appreciably increasing cost. However, the costs of enhancement of the learning environment are typically much higher than the costs of direct translation. The development costs of interactive multimedia products are many times higher than the development costs of print materials designed to support attainment of the same learning outcomes (Bates, 1995). If more traditional methods of delivery are to be compared with augmented forms of online delivery, then any improvement in the effectiveness with which students learn should be treated as an additional benefit and some attempt should be made to measure the economic value of this improvement. The decision to shift to online delivery may provide the trigger for initiating a major course revision. However, the staff time involved in regular revision ought not to be regarded as an additional cost. Regular revision of courses is an accepted aspect of good practice in the resource-based learning model of distance education. If the timing of the redesign effort is altered by virtue of moving a course online, then only that portion of the development cost attributable to the cycle time of redevelopment ought to be considered an additional cost.

THE CONFOUNDING EFFECTS OF HIDDEN COSTS A difficulty that arises in trying to compare the costs of online delivery with the costs of other forms of delivery is that there are invariably some costs that remain unaccounted for. These “hidden” costs can distort the basis of comparison. The costs of long-established methods of delivery are usually well understood, whereas the costs of emerging methods of delivery are often not all known. Comparisons of this type therefore tend to understate the costs of newer methods of delivery while fully accounting for the costs of existing methods. The effect is to place new methods of delivery in a more favorable light. Paul Bacsich and his colleagues at Sheffield Hallam University in the United Kingdom have been trying to quantify the costs of online delivery (Bacsich et al., 1999). They have found that, in the institutions they have studied, many of the costs of online delivery are not being recorded.



Bacsich and his colleagues subdivide hidden costs into three separate categories: institutional costs, costs to staff, and costs to students. Institutional costs are the costs borne by the institution. They include among these the costs of costing, the costs of collaboration, the costs of monitoring informal staff student contact, and the costs of copyright compliance. Staff costs are the costs borne by the staff even though in some cases they should, in principle, be borne by the institution. Among the staff costs, they include the costs of time spent out-of-hours in development of learning materials and the costs of use of privately purchased computers and consumables. Among the costs to students, they point to the costs of ink-jet cartridges needed to print out learning materials. Bacsich and his colleagues have concluded that there is a pressing need for institutions to start tracking the costs of online delivery. However, he acknowledges that there are a number of quite serious difficulties in trying to do this: academics, management, and administrators were reluctant to consider the use of any form of time sheet to track the extent of the investment of staff time in these activities; institutions were reluctant to acknowledge that staff work overtime; and the ways in which costs are internally accounted for within institutions were inconsistency and nongranularity of internal accounting (Bacsich et al., 1999). One finds differences of interpretation as to what should be regarded as hidden costs. For example, Morgan (2000) includes as hidden costs the costs of maintaining the central administrative services such as the central finance office and the president’s office, the costs of construction and maintenance of Web sites, and the costs of evaluation. Yet many of these are acknowledged and most can be readily quantified in some way. Whether one classifies these as hidden costs is therefore likely to depend on the individual context.

SHIFTING COSTS FROM THE PROVIDER TO THE LEARNER A special case of hidden costs occurs where costs are shifted from the distance education provider to the learner. Moonen (1994) argued that for costs to be reduced in the log run, some costs must be passed on to the student. As has already been pointed out, Turoff’s costing model assumed that students would accept the costs of communication. Rumble (1999) gives examples that suggest that institutions are moving in the direction of requiring students to assume responsibility for communications charges. Inglis (1999) found that in comparing the actual costs of offering a print-based course with the expected costs of offering the same course online, communication costs represented a significant proportion of the costs of online delivery. Furthermore, if the costs of online delivery of resource materials are going to be higher than the costs of print-based delivery, the students may prefer to be given the option of the medium in which they receive their materials.

OTHER COSTS THAT NEED TO BE CONSIDERED IN A TRAINING CONTEXT Most of the published research into the costs of delivery of distance education programs has been related to the delivery of programs in higher education. In higher education, costs are borne by the student or the state or both, in a training environment the cost is more commonly borne by the employer. When the focus is shifted to the training environment an additional set of cost factors has to be taken into account. For an employer, the costs of training include not only the costs of the training itself but also the costs of any travel and accommodation required to participate in the training and the cost of the loss of the trainee’s time (Moonen,




1997; Ravet & Layte, 1997). Whether such ancillary costs ought to be taken into account in making any cost comparison depends on the standpoint from which costs are being compared. If one is comparing cost from the viewpoint of the training provider, then the costs of travel, accommodation, and lost working time are not relevant. However, if one is comparing the costs from the perspective of the employer, then the magnitude of these ancillary costs tip the balance between online delivery and face-to-face delivery when choosing between training. It is therefore obviously important for the employer to take into account these costs.

COMPARISON OF THE COSTS OF DELIVERY IN A BUSINESS CONTEXT Online delivery is assuming growing importance in corporate training because of its synergistic relationship with e-business. In the business sector, investment decisions have traditionally been made on the basis of return on investment. Investment in training is treated no differently from other types of investment. In making decisions on investments, businesses are more interested in the benefits that will accrue than just the absolute cost of the investment. Return on investment (ROI) is the principal financial metric used to assess the value of a training investment (Cukier, 1997). ROI may be expressed as the annual profit from an investment after taking into account taxation, expressed as a percentage of the original investment, or as the number of months or years for the cash flow generated by the investment to recover the initial investment. While ROI can be calculated for an individual investment decision, it is more common for the ROI for different alternatives to be compared. For example, the ROI for online delivery of training may be compared with that of delivery via more traditional face-to-face training. The way that the expected return is measured is obviously of critical importance in such comparisons. Training managers are apt to measure return in terms of the attainment of training outcomes. However, as Cross (2001) has argued, that as business unit managers judge the worth of training in terms of the improvements in business outcomes attributable to the training investment, astute training managers will estimate returns in business terms. Both because the business returns to be expected from an investment in training are so dependent on the nature and state of the business and the relationship of the particular training to the activity of the business, and because the staff time and travel costs of training will depend on a business’s locations, it is difficult to generalize about the how alternative delivery methods compare. Because of the mounting interest in e-learning and the more hard-headed approach that business managers normally adopt toward investment decisions, one may be inclined to believe that e-learning must be proving cost-effective. However, what may be of greater interest in this connection is that when it comes to investments in e-learning, senior executives of e-business enterprises are relying more on intuition and on financial metrics because their focus is more on growth than on financial returns.

WHAT CONCLUSIONS CAN WE DRAW? Rumble (1999), in reviewing the findings of the small number of studies that have so far attempted to measure the actual costs of delivering courses online, has highlighted the great disparity in the findings. However, given what has been said here about the impact of economies of scale, the great differences in development costs for different types of delivery options, and the importance of the delivery model, this should not be surprising. However, from an understanding of the way these and other factors impinge on fixed and variable costs it is possible to predict how costs are likely to be impacted by changes in delivery methods and the results of such studies as have been undertaken do broadly correspond with these predictions.



Shifting from a remote classroom model of distance education delivery to a virtual classroom model is likely to result in some increase in overall costs. There is very limited scope for obtaining economies of scale at the course level by moving from on-campus to online delivery. However, because even classroom-based delivery uses institutional infrastructure there is still some scope for obtaining economies of scale at the institutional level. However, variable costs are likely to increase because of the additional time taken to communicate in the written rather than the spoken word while fixed costs are likely to increase because of the additional investment in infrastructure and support services Shifting from print-based delivery to an online RBL model is unlikely to result in appreciable savings and could also result in an increase in overall costs, once again because of the additional investment in infrastructure and support services that will be required. In both of the above cases, there is likely to be a significant additional cost to the student of moving online. Telecommunications charges make an important contribution to the overall costs of delivery and most distance education providers in the higher education sector are requiring these charges to be borne by the student. Requiring students to bear the communication costs doesn’t reduce the overall costs of online delivery but it may bring the costs to the institution down to a manageable level. In workplace training, it is not readily possible for telecommunications costs to be passed on to the trainee. However, in this context the costs of staff time, travel, and accommodation need to be taken into account in comparing online delivery with face-to-face training and there may therefore still be a net saving to gained in moving to online delivery. Shifting from the remote classroom model of distance education delivery to an RBL model of online delivery does offer considerable potential for achieving in savings in the costs of delivery. However, the savings will accrue, not from the change in the method of delivery but rather from the change in model of delivery. In all cases, there will be a substantial impact initially from the start-up costs associated with the establishment of new infrastructure, the development of new procedures, and the creation of new organizational structures for student and staff support. Nevertheless, the benefits of shifting to online delivery may justify the additional initial investment and any ongoing additional costs. This will particularly be the case where the shift to online delivery offers the opportunity to open up new markets that could not be accessed economically via existing delivery methods. Also, with time, the costs of online delivery are likely to fall as the capacity of networks grows, competition for customers increases, and technology improves. Meanwhile, the costs of existing methods of delivery are likely to remain stable or even increase.

WHAT MORE WOULD IT BE USEFUL TO KNOW ABOUT THE RELATIVE COSTS OF ONLINE LEARNING? The work by Bacsich and his colleagues aimed at identifying the hidden costs of networked learning may provide a more accurate accounting of the dispersal of funds for online delivery, but it remains to be seen whether the possession of such information will lead to more effective management of institutional resources, at least initially. In times of rapid change, the ways in which institutions conduct their operations and deploy staff and resources are generally more dependent on political rather than on economic factors. It is only once the new ways of operating have become more stable and predictable that costs begin to play a more decisive role in determining the choices that managers make. The phenomenon that Cross (2001) alludes to in relation to workplace training of managers relying more on intuition in making their decisions in relation to the implementation of online delivery




probably applies more generally, if for no other reason than that the paucity of dependable information leaves them with no other choice. Nevertheless, it is inevitable that education and training managers will continue to maintain a close interest in the costs of online delivery. Keeping costs within acceptable limits is what managers are expected to do. What this suggests is that the type of information that managers, and for that matter teachers, require is information that lets them make choices at the micro level. The question that managers now want answered is not “Which method of delivery is less costly?” but “How can the costs of delivery best be managed?” In other words, given that the decision has been taken to go online, how can the quality of courses and programs be maintained or increased without at the same time producing an escalation of costs. What has been argued here is that effective management of costs is more readily achieved if the delivery of courses and programs is conceived of in terms of delivery models rather than in terms of delivery system components and that when considered in these terms, maintenance of the quality of programs and courses is most easily achieved through a blending of resourcebased and classroom-based approaches. Because productivity and costs are likely to become increasingly important issues in education and training, providers in the United States are likely to move more and more toward resource-based learning in order to take greater advantage of the opportunity to obtain economies of scale. At the same time, distance education providers elsewhere in the world will take advantage of conferencing capabilities of networked learning in order to decrease the isolation of distance learners and improve the quality of their learning experience. The combined effects of these trends will be to bring the practice of distance education in the United States and practice of distance education elsewhere in the world into line. From a situation where it is readily possible to distinguish two quite different and often competing approaches of distance education delivery, we will move to a situation where two complementary approaches to distance education delivery are melded into a single hybrid approach. This phenomenon is likely to start to bring to the surface new costing issues such as: How can the melding of classroom-based and resource-based learning optimize the tradeoff between costs and quality? How can resources best be deployed to take advantage of economies of scale while not adversely affecting the quality of the student’s learning experience? What are the most effective ways of supporting student-student and student-instructor interaction without at the same time increasing costs? What possibilities exist for using the potential of information technologies to reduce the variable costs associated with student support? In what ways can improvements in the management of start-up of projects help to contain the initial investment in infrastructure, institutional reorganization, and staff development required to shift from existing methods of delivery into online delivery? How are course completion rates and student satisfaction measures impacted by the balance struck between the use of self-instructional courseware and virtual classroom group interaction? REFERENCES Arvan, L. (1997). The economics of ALN: Some issues. Journal of Asynchronous Learning Networks, 1(1), 17–27. Ashenden, D. (1987). Costs and cost structure in external studies: A discussion of issues and possibilities in Australian higher education. Canberra: Australian Government Publishing Service Evaluations and Investigations Program. Bacsich, P., Ash, C., Boniwell, K., Kaplan, L., Mardell, J., & Caven-Atach, A. (1999). The costs of networked learning. Sheffield: Sheffield Hallam University.



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