Human thinking can skip over a great deal without losing the plot. In conversation the mind can leap over small misunderstandings, containing ‘ifs’ and ‘buts’ in untroubled corners of the mind without losing the thread of the main parts of the conversation. All of this is done as if on automatic. Despite all the attempts to envision the computer as a brain, it doesn’t have a foreground or background and all conversation must start from scratch and proceed serially. Yet what is fascinating is that, upon completion of a programming project, in the painstaking working out of the specification, line by code line, the programmer often begins to confront much of the hidden workings of her/his own thinking; discovering that thinking often lacks efficient algorithms for parallel processing, based on disjunctures between logic, rules, emotion, clarity and socialisation.
This paper attempts to dissect the components of teaching/learning via computer mediation in order to suggest that the differences of both the human agent and the technological artefact provide for a new, powerful and different teaching/learning process when combined.
The human brain is an enormous accumulator and parallel processor of information. The resulting knowledge is made up of many different informative inputs including thoughts, emotions, cultural backgrounding, peer dependence, etc. The computer, as technological artefact while, as yet, incapable of accumulating information as comprehensively as the brain, is able to serially process and archive that information more rapidly and efficiently with respect to the provision of outputs. The long and short-term adjustments of memory patterns in the brain are completely reconciled with computerised archived database search algorithms and techniques.
Learning is as much a function of its experiential context as the information upon which it is based. A major premise herein is that, given the differences between the brain and computer, their combination in any teaching/learning process has the potential to achieve a level of knowledge more comprehensive and uniquely powerful than we are able to, as yet, envisage. As well, there is the danger that the potency and efficacious wherewithal of either, or both, the brain and computer will be dissipated in wasteful utilisation.
We proceed with the social, interactive and constructivist theory of knowledge fused with the Marxist methodology of Lev Vygotsky. The aim is to examine the system of knowledge construction through the conjunction of the collaborative mental and physical labour of humans and the computer as artefact. We proceed in the following section to detail the input/output biases of humans as compared to computers wherein biology increasingly serves as the source of inspiration for computing research. This is followed by probing the human-artefact relationship in which human thinking is both constructing and being constructed by computerisation in the teaching/learning process. The core of the penultimate section is used to assay the place of the computer in the socially constructed process of teaching/learning where multilevel conversation and communication provides for interactive knowledge construction. A short discussion noting our rapidly changing, and uncertain, educational circumstances concludes in the spirit of Francis Bacon’s apothegm: “Nec manus, nisi intelectus, sibi permissus, multam valent: instrumentis et auxilibus res perficitur”1
Originally, speed was regarded as the main advantage of the computer. Using the multiplication time of a desk calculator as the basic unit of computing speed, it was estimated that differential analysers were 10 times faster, relay calculators 30 times faster, and electronic computers on the order of 105 times faster. The main mathematical significance of the electronic computer’s speed was that it brought into range problems that were only marginally practical or entirely impractical to calculate using earlier devices: large runs of ballistic trajectories, astronomical orbit calculations, and parabolic and hyperbolic differential equations of fluid dynamics are a few examples (Cohen, et al., 2000: 8 and 15). Given the exponential increase in the availability of searchable archived ‘memory’, the desktop computer now easily replaces the multi-volume encyclopaedias that stored the wealth of accumulated information in the recent past.
John von Neuman’s detailed description of the human nervous system as an information processor offered one of several comparisons of the computer and the human brain: speed, energy consumption, size, efficiency, and number of basic switching components. He calculated that the computer requires greater volume, consumes more energy, and is 10,000 times less efficient than the brain (in binary actions per unit either of energy or volume) but that the computer compensates by its considerable advantage (a factor of approximately 5,000) in speed and memory recall. Consequently, he argued that the brain and the computer are logically organised in rather different ways: the brain favours more but slower-switching components while the computer favours fewer but faster components. The computer is organised for serial operation and the brain for parallel operations, and there are problems that must be confronted in moving from one mode of operation to the other (finding efficient algorithms to make use of parallel facilities, for example, or adding extra storage for intermediate results in a serial operation) (Aspray, 1992: 208-209).
One of the major differences between humans and computers can be found with respect to their different input/output biases (Pinker, 1997: 4, 21-28). Each human has around 100 billion neurons. Information flow is strictly dominated by input rather than output. From the moment we are born until we die, our sensory system is feeding our brain vast amounts of information. Our utterances and animation pale into insignificance in raw bit-rate terms compared to the visual, audio, and tactile input we constantly absorb. We can set each of our eyes to feed us bits from 127,000,000 rods and cones in the retina, via the visual cortex, at about 1 Gigabyte per second. This far outweighs the input from our hearing, tactile, smell and taste sensors, which total less than, but still relatively high, 20 megabytes per second. In contrast our typing rate is only around 40 bytes per second, speech is about 100 bytes per second, while gesticulation, and facial expression are even lower, but can be socially encoded to efficiently convey far more.
On the contrary, computers for the most part, only get a diet of low bit-rate alpha-numerics, relatively insignificant amounts of direct visual and audio feed, and certainly no tactile, taste or smell inputs. The impressive speed and robustness of biological visual processes is far beyond the capability of current mechanical image-recognition algorithms. Programming a computer to recognise objects requires many (time-consuming) iterative steps. So computers all seem to operate in the mode of creating more output than input. But, biology is increasingly serving as the source of inspiration for computing research. One of the more vibrant areas of research, given the massive increase of visual data in information systems, is in electronic image-recognition performance (Postma, et al., 1998: 25). Optimistic visionaries argue that only about one or two human generations will be needed to close the million-fold gap, of instructions per second, between the capability of the retina and the programmed computer. Moravic (1999: 93) predicts that given the advances in simulation modelling of physical, cultural and psychological factors, powerful reasoning programmes which already make medical diagnoses, schedule routes, analyse seismic data and so on, will completely transform our work, play and education. In May 1997, an IBM parallel supercomputer defeated World Chess Champion Garry Kasparov in six games. Even so, there is a lot of solemn talk about what computers can’t do. In fact, computers are doing a great deal today that they were “known” to be unable to do a while ago. As Nobel Laureate Herbert A. Simon sarcastically pointed out (2001: 34) there was even a book published by that name2 that has had to go through numerous revisions after the initial publication. It is already possible to programme computers to operate as if they were functioning under conditions of uncertainty, the dominant parameter of human decision-making. In particular, a branch of logic, called “fuzzy logic,” can be used to create programs that try to predict the probability of various outcomes when one or more pertinent conditions are not well known. Tellingly, the researchers themselves identify their work at the cutting edge of electronic fuzzy neural networks and spatial-temporal maps, anthropomorphically, as “adding consciousness to an intelligent information system...enabling machines to be aware of what they are in the current operating environment, how they relate to other objects, what they can do and who might do what they cannot attempt, etc” (Kasabov, 1998: 105-128). Theorems in mathematics and logic have been discovered and proved by computers independent of human guidance. One of the most impressive things about a computer is its capacity, by virtue of its power and the flexibility of the responses, to be its own instructional device.
At the level of code itself, computer logic cannot be fuzzy. Each programming statement must resolve into a set of logically determinant and executable machine instructions provided by a human agent. If something unexpected happens at the level of the computer instruction, it can only be resolved by human agency (Ullman, 1997: 22). However, the simple processing elements of biological systems can be, and are, used as part of the field of artificial neural networks (Pugmire, et al., 1998: 55-57). This neurally inspired approach to computation (Rosenblatt, 1962), although heavily criticised early on (Minsky and Papert, 1969), has more recently generated interest in developing a number of techniques which are related to instructional design, such as learning from hints (Suddarth and Holden, 1991). Given the importance for instructional designers in providing blueprints of methods to use for particular desired outcomes within teaching/learning environments, the relevance of computer-mediation becomes evident.
In any case, it must be emphasised that, as Ullman (1997) argues, the computer cannot be envisioned as a neutral instrument. There is something in the system itself, in the formal logic of programs and data, which recreates the world in its own image. Therefore, while the artefact is not comparable to human agency, it is a projection of a part of ourselves: that portion devoted to logic, order, rule, and clarity. We call the microprocessor the “brain”; we say the machine has “memory”. We create it, and its applications, in our own image. The computer, a machine originally designed to specifically process information and control systems – has become at the very least, a powerful ideograph of cybersystemic learning3 .
...slowly we incorporate the whole notion of systems: we’ll link registration data to surveillance, to contract compliance....Finally, we arrive at tautology: the data prove the need for more data! We think we are creating the system, but the system is also creating us. We build the system, we live in its midst, and we are changed (Ullman, 1997: 89-90).
An instrument of labour is a thing, or a complex of things, which the worker interposes between himself and the object of labour and which serves as a conductor, directing activity onto that object. He makes use of the mechanical, physical and chemical properties of some substances in order to set them to work on other substances as instruments of his power, and in accordance with his purposes (Marx, 1868/1979: 285).
Within the institutional culture of education at present, and arguably for social formations in general, the most important aspect of computers is their use as a collaborative artefact for thinking and creativity (Bennahum, 1998: 34). Active agents of learning within Vygotsky’s “zone of proximal development” include people, with various degrees of expertise, but the zone also includes artefacts such as books, scientific equipment and a computer environment, intended to support intentional learning (Brown, et al., 1993: 191).
At a cognitive level, human thinking is both constructing and being constructed by computerisation in the teaching/learning process. Chronologically, with the release of the first mass-produced home computer, the Apple II in 1977, the graphical user interface (GUI) introduced by Macintosh in 1984, and the release of Mosaic, later known as Netscape in 1993, the terms of human-computer relations took a sudden and profound turn. On a massive global scale, humans began to cooperate and to live interactively with electronic, digitalising artefacts.
During this interaction over a generation, the human-artefact relationship has been transformed, involving both gains and losses. As computers become both more powerful and oriented to mass consumerism, it is harder to get beneath the glossy overlay of the GUI to the actual controls that make them work. Magic, beauty and metaphor are replacing the tactile sinews of logic gates and motherboards. Like most other household consumables, when it doesn’t work anymore we trade it in or throw it away. Where once teaching/learning in relation to the computer meant to comprehend and utilise a programming language (Charp, 1997), the new artefacts have spawned a changed curriculum centred around software in which knowledge and learning is constructed by a series of ‘clicks’ on ‘next’ buttons enabling us to ‘map’, and ‘navigate cyberspace’ with a ‘mouse’ (Bennahum, 1998: 210-211). The loss of knowledge of “how the engine works” has been replaced with an increased ability to both represent and navigate digital environments.
Neural systems exist in the brain to support two types knowledge construction: mental mapping (which is largely synchronic) and mental navigation (which is largely diachronic). Mental mapping allows us to encode our location with respect to places to be approached and avoided in a particular limited area, based primarily on current perception and working memory. ‘Location’ is used in a broad sense, which can refer to space, social status, academic discipline, etc. Mental navigation is concerned with making a transition from one location to another (Schmajuk and Blair, 1993). Mapping involves egocentric representations based on our current view of the world, given our perceptions of the socio-physical landscape that surrounds us. Mental navigation, on the other hand, is of necessity allocentric. It involves confronting, engaging, listening and responding to others whose egocentric perspectives may be very different from our own. Using the metaphor of a subway map, we have an intellectual and perceptual sense of where we are and where we want to go in a teaching/learning situation. Given our location within that cognitive map we must then navigate collaboratively and socially towards our goal. Spatial relations are identified, links identified, junctions and byways examined and a collaborative framework established between oneself, the artefacts used, and others engaged in cyberspace (Clancey, 1992; Guazzelli, et al., 1998:436-438). The major difference between that process now, and as it took place a generation ago, is the relatively recent placement of the computer as a significant determinant in both constructing our cognitive map, and in the development of our navigation process towards goal resolution.
Firstly, the cognitive mapping function (ability to survey and situate our thinking, reasoning, remembering and imagining) is assisted dramatically both by the synchronous and asynchronous communication potential of Internet relay chat, email, discussion forums, bulletin boards and real audio. Through one-to-one, one-to-a-group, or group-to-group communication channels, research findings, essay reproduction, group work or simple conversation, the individual is allowed to incorporate his/her individual cognitive position in comparison to, and interaction with, others. Harasim (1990:43) describes peer interaction amongst students as a critical variable in learning. In order to “come to know”, learners need to construct their knowledge by acting upon it, reformulating it, making their own personal interpretation of it, sharing it with others and building upon these ideas and concepts through the reactions and responses of their peers. Both anecdotal and formal research evidence identifies the social process of cognitive mapping for individuals of chat lines, bulletin boards, discussion software and research collaboration generated by computer-mediation. One practical example would be that in all of my courses, essay projects (both individual and group) are placed on the Internet for all students in the class to view. This allows them to situate their own work in comparison to others. A secondary benefit is that, as students indicate in surveys, and assessment evidence suggests, the quality of one’s effort is enhanced knowing that everyone in class will be able to read your work.
With respect to navigation, hyperlinking is a powerful tool for creating additional and alternative voices to those of the teacher/learners in the classroom. Not only are students able to explore the linkages between theory, applications, models and paradigms within the confines of the course itself, they are also encouraged to explore the interconnections to other courses in the discipline, and other disciplines, through further hyperlinks. A hypertext format thus allows a degree of learner control as to what information will be accessed and in what order. Relevant links to outside sources are built into the text itself, thus the lecturer’s voice is one of many possible voices in the exploration of a topic. Links are made at the point where relevant information interconnects with other information, rather than the traditional add on ’for further information’ section at the end of a textbook topic, lecture, or set of notes. It also enables students to set their own pace, either exploring issues about a topic of interest more deeply, or spending less time on concepts that are already understood. Students in this environment are no longer “passive learners attempting to mimic what they see and hear from the expert teacher” (Berge and Collins, 1995: 6), but more active participants in the creation of knowledge and meaning.
Further, the procedural power of the computer is delineated not by its ability to carry or store information, but by its embodiment of complex, contingent and heuristic behaviours. Whether the computer is flying an airplane, or simulating a money market, the procedural environment exhibits behaviours that are generated by human interaction. It is this interactivity that permits us to represent and navigate virtual space. Unlike linear media, such as books and films that portray space by verbal description or image, digital environments represent a space through which we can navigate. Since all of the other forms of media are becoming accessible in electronic form, and all of the world’s computers are potentially accessible to one another, the teacher/learner is provided with a global database of literature, news, music and libraries rematerialised in cyberspace (Murray, 1997: 71-84). Because the reality of cyberspace remains significantly fragmented and loosely organised, frustrating in its limitlessness, and often misleading, the role of the educator is limned. The job of teacher is predominantly that of a guide, efficiently locating and navigating information, posing questions, identifying alternative voices, collaboratively working through the implications and usefulness of that information based upon an identifiable world-view. Even moreso, the arguments of Rogoff (1994) and Ekeblad (1998) are pertinent as they see the role of the teacher as one of promoting and building communities of learners within educational systems. They both use Vygotsky’s seminal idea of the sociocultural situatedness of human learning, not just for the individual, but also for the entire community involved in learning and knowledge construction.
The first point to be made is that thinking is an active, collaborative process; the second is that during the last generation there has occurred the introduction of a new cultural instrument, the computer, which transforms this process. The computer is part of the special environment that humans now inhabit, and as is the case with all technology, is suffused with the thoughts, activities, and achievements of prior generations and presented to us in material form. Our present activity benefits from the mental labour that produced the particular material artefact (Cole and Wertsch, 1997).
In this view, the computer does not simply facilitate forms of thinking that would otherwise occur. Instead, to paraphrase Vygotsky, “by being included in the process of behaviour, the (electronic) mediator alters the entire flow and structure of mental functions. It does this by determining the structure of a new instrumental act, just as a technical tool alters the process of a natural adaptation by determining the form of labour operations” (Vygotsky, 1981: 137). In such a view artefacts clearly do not serve simply to facilitate mental processes that would otherwise exist. Instead, they fundamentally shape and transform them and in turn are changed by the process (Kapetlinin, 1996: 10). Again in Vygotsky’s view:
the inclusion of a tool in the process of behaviour (a) introduces several new functions connected with the use of the given tool and with its control; (b) abolishes and makes unnecessary several natural processes, whose work is accomplished by the tool; and alters the course and individual features (the intensity, duration, sequence, etc.) of all the mental processes that enter into the composition of the instrumental act, replacing some functions with others, i.e., it re-creates and reorganises the whole structure of behaviour just as a technical tool re-creates the whole structure of labour operations (Cited in Cole and Wertsch, 1997)
By taking this view, the very notion of agent comes to be redefined. Instead of assuming that individuals, acting alone, are the agents of actions, the appropriate designation is of a process in which the individual engages others via a means of mediation (Wertsch, et al., 1995: 64). Thus, the teaching/learning process is to be explored as a multi-level relationship between the cultural, historical and institutional settings in which thinking occurs, based on a set of conventions (values, symbols, rules) and combined with a electronic means of mediation that both shapes and is shaped by thinking in the pursuit of knowledges. It is a process of becoming for teacher, learner and artefact, rather than an expert-novice dyad of informational acquisition. Using Vygotskian theory, the Marxist construction of knowledge via labour and the means of production becomes the primary metaphor. It is through mental and physical human labour, mediated by technological artefacts, that knowledge is constructed (Confrey, 1995: 45).
Vygotsky’s development theory and its complex implications stress the inherent social nature of all human activity. Data and computers are social constructs developed through the historical relationships of teacher, learner and learning context, and the technological means of data collection. According to Vygotsky, the ‘zone of proximal development’ refers to each person’s range of potential for learning. The environment in which it takes place, as well as the type of mediation of mental processes by the instruments of production and signs socially shapes that potential (Smagorinsky, 1995: 192-194). The construction of knowledges requires a relational and dialectic interpretation of mind, objects and contexts as part of a single bio-social-technological-cultural process of development (Cole and Wertsch, 1997). Activity and conscious thinking are dynamically interrelated, with learning a function of interaction with both artefacts and the social matrix of that activity (Jonassen and Rohrer-Murphy, 1999: 62).
As no instrument will be adequate to all tasks, and no material form of cultural mediation is of universal applicability, so there is a troubling difficulty with the computer. This difficulty, with respect to the teacher/learner relationship, is in its limited ability to engage the “meta-conversation” that is prominent in face-to-face teaching/learning. Here, the insight of de Saussure (1988) assists us by revealing that meanings lie with the representor and the representee and are in principle, private and inaccessible to others.
Teaching/learning is a socially constructed process in which individuals bring their own socio-historical context to the exchange. Part of the interaction is to continually match our socially constructed frames of reference. The meanings of interaction for each of the participants, while individually private, are constructed in the teaching/learning interaction based on the shared socio/historical context. This is what de Saussure meant when he insisted that there was no necessary connection between a word and its meaning. For meaning to exist what matters is that there is a common social world of reference existing behind the individual differences (Glanville, 1996: 442-446). In face-to-face teaching/learning, each of the participants are watching for meta-feedback signals in order to work out potential misunderstandings. A frown, a smile, a raised eyebrow, a nod of the head, a touch, a yawn, wandering eyes, are all signals used to identify the similarities or differences in understanding so as to adjust the representation of meaning. This meta-conversation, which is carried on to suggest, enhance, or reduce misunderstanding, is familiar to all of us as a different, but simultaneous, level of communication. We move interactively between the levels of specific representation and the meta-conversation to avoid, or remove, differences or misunderstandings (Glanville, 1996: 452). In other words, in any teaching/learning situation we are engaged in both a conversation proper, and a meta-conversation utilising visual, auditory or even tactile signals (Driscoll, 1994).
Computer-mediated-teaching/learning provides for a conversation that is more accessible in space and time. What the computer, and its user, have difficulty achieving is the socio-historical meta-conversation that is evident in face-to-face teaching/learning interaction. Written speech is a separate linguistic function, differing from oral speech in both structure and mode of functioning. Even its minimal development requires a high level of abstraction. It is the abstract quality of written language, addressed to an absent or imaginary person or to no one in particular that is a stumbling block. Written language demands conscious interpretive work. When deployed to its fullest extent, it is more complete, and likely more thoughtful, than oral speech, but must explain the situation fully in order to be intelligible. The change from maximally compact inner speech to maximally detailed written speech requires what might be called deliberate semantics - deliberate structuring of the web of meaning (Vygotsky, 1932: Chapter 1).
While the computer is not the origin of this problem wherein meta-conversation is lacking, which originates in writing versus speech as forms of communication, it is likely to provide a high quality resolution of the problem within the next generation. “Web-cam” already provides a relatively primitive video connection for those “meeting” over the Internet. Rapid advances are being made in streaming digital video and this has been identified as the “killer application of Internet2”4 within five to ten years. Innovations in computer speed, network speed and compression techniques will make full screen digital video enhancement of web-cam a reality very soon (Hanss, 2001).
But the crux of the challenge for us is the integration of computer-mediation into the teaching/learning process as an adjunct to, or replacement for, face-to-face communication. Given the immeasurable enhancement of efficiency in access and connectivity in space and time provided by the computer, how can we replace or re-generate what is qualitatively lost in forms of meta-conversation via computer-mediation, until we resolve the problem technologically via digital video. In one sense this is not an issue in that any extension of access and connectivity in distance education is beneficial. But given the increasing importance of distance education, both private and public it is essential to examine, and attempt to alleviate, the discomfort teacher/learners feel in computer-mediated-communication. In fact, there is a laundry list of tactics available to the teacher/learner (web cam, streaming video, .jpeg photos, homepages, bio-sketches, etc) to assist in generating elements of meta-conversation; an overall strategy is manifest in the works of Vygotsky (1962 and 1978). The difficulty with all of these is that while they may alleviate the problem they don’t remove it since they are all (except for the primitive web-cam) one-way transmission with little interactivity. Therefore, the lack of meta-conversation will remain at the core of complaints about computer-mediated learning in the short-run.
Thinking originates in collaborative dialogues (Bahktin, 1981). Therefore, computer-mediated-communication requires open-forum, dialogic interaction. The most important responsibility, with respect to computer-mediation, to be carried out by the teacher/facilitator, is a dialogic open-forum, not the provision of information. To use the Internet as simply another way to ‘push’ information at passive learners is an anachronistic waste of this powerful tool (Crook, 1997). Given the capaciousness of the Internet, the learner is capable of gathering more information than most teachers will ever know or learn. In order to remain relevant, teachers must begin to reconstruct their pedagogical models, taking on the role of a facilitator and collaborator, encouraging active and interactive learning.
Numerous case studies provide evidence that students develop new, and powerful, habits of critical evaluation through discussion, irrespective of the medium (McCutcheon, 1981; Miller, 1999). As learners become aware of the multiple perspectives that are given ‘voice’ in a discussion forum, this awareness dramatises the need to consider conflicting possibilities, and in this context, learners learn the means of “choosing one’s orientation among them” (Bahktin, 1981; Bruner, 1986). The teachers must encourage responses, guide attention to key points in the discussion, scaffold strategies for questioning, monitoring and elaborating, all aimed at getting learners to think in increasingly complex ways about alternative interpretations. Based on a case study carried out by Miller (1999), students learned collaboratively to extend and question initial responses for themselves in ways that became socially valued in the class. For example, they connected text and personal experience; questioned the text and each other; evaluated possible interpretations; identified difficult passages and generated plausible explanations; moved back and forth from the landscape of actions to speculation about human intentions and consciousness; and created imagery, metaphor, dramatization to generate understanding. There is absolutely no way that this cannot be continually, and more efficiently, achieved via computer-mediation.
Electronic artefacts circumscribe and confront teachers and learners alike, making it necessary for us, as educators and students, to struggle for control over the social construction of the digital culture. Computers, in collaboration with human agents, have the capacity to catalyse a new culture, devolving power, breaking hierarchical controls, transforming bureaucratic institutions and expanding our abilities to create. Some of us, partly represented by those in the “Linux culture” for example, act as citizen-hackers capable of ripping open systems, understanding their functions and, collectively, building better ones. Simultaneously, it is very lucrative to evacuate the ideological field of battle and move into an arena consisting of information technicians, programmers, and system analysts who construct knowledges for transnational corporations that are removed from the computer user (surveillance, digital advertising, consumer data gathering and manipulation). The nuclear, biological, and chemical technologies used in 20th-century weapons of mass destruction were and are largely military, developed in government laboratories. In sharp contrast, the 21st-century technologies (genetics, nanotechnology and robotics) have clear commercial uses and are being developed almost exclusively by corporate enterprises. In this age of globalised commercialism, technology - with science education as its handmaiden - is delivering a series of magical inventions that are the most phenomenally lucrative ever seen (Joy, 2000). In this way, like technologies in the past, computer artefacts can just as quickly and easily be assimilated into an existing social system, centralising power, promoting hierarchy, embedding bureaucracy into our lives and limiting our ability to constructively create (Sosteric, 1999). In either case, humans and computers are capable of accomplishing things that neither of them can do alone (Rosenblith, 1962: 309).
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1. "Left to themselves, neither hand nor mind alone amounts to much; they are perfected by the instruments and aids that they employ" (Francis Bacon, Cited in Bruner, 1997: 65).
2. Dreyfus, H. 1993 What Computers (Still) Can't Do (2nd ed.)
3. "Everybody line up alphabetically according to your height." These words of Casey Stengel (1891-1975), United States baseball player and manager, sum up nicely the deep human need to arrange things in order, to sort, classify, and enumerate them, a need best satisfied, at more complex levels, by a computer.
4. Internet2 refers to a consortium of 185 universities joined by over 100 corporate, government, and not-for-profit organizations. Internet2 <http://www.internet2.edu/> is working to build and deploy advanced research and education applications on an enhanced networked infrastructure. Internet2 networks are like a time machine, anticipating what may be possible in the future when everyone will have access to high-speed broadband connections at home, school, and work.
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