Logo-like and Inarticulate Intelligence

Mícheál Ó DúillBolling Special School

Annerley Street, Bradford BD4 7SY UKmikedoyle@cix.co.uk

AbstractLOGO is associated with an educational philosophy. The history of the last two centuries suggests that the scientific approach is more profitable. The paper reviews our current understanding of evolution, with specific reference to our species. The conclusion drawn is that human intellect is uniquely technological and inarticulate. Some consequences for our view of education are explored and tentative suggestions made for the future role of LOGO.

Keywords: LOGO, ICT, science, evolution, education, learning, language, technology.

1. Introduction

LOGO is a simple high-level programming language designed to be used by children as an educational aid [1]. It is also associated with an educational philosophy that reflects the work of Piaget and has a constructivist/constructionist child centred orientation [2]. Adult users of Logo, according to an Internet survey of the LogoForum and comp.lang.logo [3], peak in the 50 year-old age range and have as much interest in educational philosophy as Logo programming. Logo is presently little used in education, relative to the widespread use of both the language proper and turtle-graphics packages in the 1980s. As the resources currently directed towards establishing an ICT infrastructure and online communities begin to be released, that infrastructure and those communities will be well situated to consider more fully the role ICT will play in education, when information is processed industrially. This suggests that an in-depth re-evaluation of Logo may be timely.

The first step in any re-evaluation must be to clarify the educational rationale for using Logo. The objective of the originators of Logo, to help children learn mathematics, may no longer be a prime objective. Similarly, it may be time to recast the educational philosophy underling Logo in a more scientific mould, as happened to Greek natural philosophy. In particular, there is a need to move away from Ptolemaic ‘saving appearances’ congruent with verbal formulations of belief. For this there are two requirements. Firstly, a scientific framework within which to consider education; and secondly, agreement on the meaning of the (currently vernacular) terms employed in discourse about education and computers.

2. Evolution: the scientific context of education

We can understand ourselves and hence concepts of education, teaching and learning, only in the wider context of evolution. It is because of Darwinian processes and the replication of genes that this paper may be written [4]. It is because of the evolution of the physical universe that biological evolution is possible [5]. It is because as the universe evolved it passed through a number of distinct phases, related to energy and entropy, that complexity increased [6]. The evolution of our species evidences such a phase change [7].

2.1. Physical evolution

In the younger universe energy levels were so high that electrons could not be bound to protons and so atoms could not form. When expansion led to a critical lower energy level, electron-proton association became possible and hydrogen, helium and some other lighter elements formed. Planet formation, and the elements

193

required for constructing life forms, was dependent on the creation of heavier elements in larger stars and their distribution following a supernova implosion as interstellar dust. Biological forms exist in this context.

The ‘discontinuity’ shown by evolutionary phase change involves the addition of a novel entity to the pre-existing structure, and often degradation of free energy, both leading to an increase in entropy, i.e. complexity. In general terms, evolutionary phase change is resource dependent, in terms of both available energy and matter, and is determined by the entropy climate. We experience phase change every day when we boil a kettle to make a nice strong cup of tea, or when the children in our winter classroom look skyward in the hope of snow.

2.2. Biological evolution

Like planetary evolution, biology is resource dependent. No DNA based organism can exist on Earth unless there is an ozone layer: DNA is destroyed by the radiation ozone intercepts. As with physical evolution, biological phase change occurs only under specific conditions for most of evolutionary time Darwinian equilibrium exists. Prokaryotes (bacteria) were the only life form on earth for millions of years until the earth became covered in ice. This left only isolated prokaryote populations with barely sufficient energy to replicate. Under these conditions of prokaryotes associated symbiotically, leading to the incorporation of one as mitochondria, the ATP cycle powerhouse of the eukaryotic cell, which provided some energy independence and made possible more the complex structure of multicellular organisms. Climate change leading to the melting of the ice made a massive resource base available to the newly evolved eukaryotes, resulting in a radiation that activated their capacity to develop multi-celled organisms. Resource fluctuations are, similarly, associated with the various mass extinctions seen in the palaeological record. Vertebrate evolution appears to have been Darwinian except where these fluctuations occurred. In such cases the trend is for more complex species to benefit from the hiatus, apparently because they use resources more efficiently. It is worth noting that primates, a relatively unspecialised tree living tetrapod, coexisted with the dinosaurs and only radiated after the decline of the latter, alongside the evolution of modern angiospermous (flowering) plants and trees.

2.3. Primate evolution

A generalist homoeothermic tetrapod, the evolution of primates is intimately related to their complex three-dimensional, visually short-range, forest habitat; where visual identification of resources and coordination of complex motor movements to access them is combined with a requirement to communicate by sound. The Pongidae, particularly the chimpanzee, the most developed of primate species extant, exhibit social, communication and tool-using behaviour which parallels that of our own species: they make tools for specific purposes; have alliances and communication related to leadership and affect; and are capable of ‘de-chimpanzeeing’ closely related members of their own species [8]. When climate fluctuation some 5 million years ago favoured a reduction in forest and an increase in bush and grass, the precursors of chimpanzees stayed in the remaining forests, whilst newly evolving Homo developed a bipedalism that capitalised on the pre-adaptation of the hands for manipulation.

2.4. The genus Homo

Palaeobiological evidence shows the brain size of Homo species increasing from about 700cm 3 in H. habilis, to 1000cm3 in H. erectus, with a maximum of around 1450cm3 in H neanderthalensis. With brain size increase comes evidence of speech development, seen in the presence of Brocca’s and Wernicke’s areas, and an increasingly sophisticated tool assemblage [9]. A particularly important characteristic of Homo tool assemblages is that they are species specific. The beautifully crafted hand-axe made by H. erectus changed little in over a million years. The Neanderthal assemblage, comparable with that of anatomically modern H. sapiens with whom they co-existed in the Levant, elaborated only after contact with behaviourally modern humans. This suggests that these toolkits are best viewed as an extension of the phenotype [10], cf. birds’ nests. Homo species’ capability to manage fire may also be considered a complex inbuilt behaviour. The notion that the increased information processing capacity that came from a larger brain implies more

194

complex inbuilt behaviour is congruent with the tendency to specialist adaptation in evolution. Though surprising, the cumulative evidence, including the existence of a universal grammar [11] and the developmental process following the completion of neurogenesis some 12 months after birth [12], suggests that language, like the eye, is a classic biologically evolved information-processing adaptation.

2.5. Evolved intelligence

Intelligence is a function of evolved neurosystems, shaped by Darwinian processes. Hence, the intelligent functioning of a neurosystem is intimately related to the environment within which it evolved. Furthermore, because that environment must be relatively stable over long periods for Darwinian evolution to occur any neurosystem will be well adapted to it. Neurosystems, like the hoof of a horse, anticipate their environment. This has consequences for the way they function. If a sense input and effector action is likely then it is more efficient for a neurosystem to anticipate, to propose input and output, than continuously to compute all possibilities. Biological information processing systems, therefore, are likely to operate along the lines of adaptive resonance [13], i.e. a top-down propositional system that matches central anticipation against sensory input.

2.6. Human Intelligence

No animal can draw. There is no archaeological evidence that any species of Homo prior to behaviourally modern humans (H. sapiens sapiens) had any graphic capability. The earliest cave art extant originated some 30,000 years ago. The species that drew on cave walls, our species, also had a novel technological capability. The species-specific tool assemblage was replaced by generalised tool making including bone flutes, tools for music. Given that anatomically modern humans appear in the archaeological record around 200,000 years ago, it seems that some sort of evolutionary mindstorm occurred in the intervening period. We can be reasonably confident that language does not delimit the highest information processing level available to us as a species. Bone flutes, representational carvings and paintings, abstract signs, and a complex toolkit and household artefacts, evidence a transnatural lifestyle unlived by precursor species Homo who appear to have had speech.

Underlying the behavioural change must be neurosystem change. A working hypothesis, or model, is suggested by current psychological, neurological, and computational findings:

The much-enlarged frontal lobes are highly connected to older parts of the brain [14];

Whereas all people are competent language users, technological and artistic ability is distributed in the population unevenly; and

Highly developed musical, realistic drawing, and calculation skills are shown by ‘savants’, who otherwise have very severe mental handicaps.

This evidence is supportive of the hypothesis of a newly evolved capability. To develop this notion it is proposed to resurrect Maxwell’s information processing Demon.

The thought experiment proposed involves imagining a neurosystem, operating on the propositional principle, that has as its environment the previously evolved animal neurosystem. Such a neurosystem would have an indirect access to sensory input and motor output, i.e. to the way visual and aural signals are processed and motor actions sequenced. Given that language evolved biologically, the Demon will have access to the universal grammar.

This Wizard of Oz would also have access to evolved expectations and the behavioural characteristics of a primate species: such as the ability to work in groups, of leadership, and the capability to dehumanise others. It would also be ‘aware’ of the relationship between resource availability and reproductive survival in a way that no previous species could be. However, it could not be articulate because it has access only to neurologically processed information and not to the environment directly. This

195

freewheeling module would have access to information but not to things. But it also has access to the motor control system so could both design abstracted objects and direct the hands of a craftsman to create them. This, it is suggested, represents an evolutionary phase-change: from biology to technology.

3. Inarticulate Intelligence

Fundamental to the above model is that technology, not language, characterises our species. Language is but one neurosystem, albeit powerful and relatively recently evolved, to which our human intellect has access. Human intellect, more powerful than language, is expressed in manual operations including art, music and design. Much of the archaeological and historical record may be interpreted in terms of a steady increase in the exercise of control over the physical, ecological, and linguistic environment by this inarticulate intelligence. The following examples illustrate this.

3.1. Resource

All living organisms co-opt environmental resources so as to maximise the potential for genetic survival, and the closer the kinship the more similar the genetic makeup. Hence, early H. sapiens, and some modern, life-styles revolved around the extended family group. Where environmental conditions are favourable and open to management, as in post-glacial southwest Asia [15] (the biblical Garden of Eden), larger more differentiated communities can be resourced. The technological infrastructure developed in this context was thus available to exploit fertile, but otherwise resource poor, flood plains for food production.

The primate social structure became formalised through the naming capability of language, as deployed by the alpha-group. This was aided by the capacity to de-humanise, leading to the use of warfare and slavery to co-opt production and productive resource respectively. Language was also used to widen the genetic kin relationship to the larger community, by the use of the collective term “black-headed people” in the case of the Sumerians. Thus, the combined human and material resource of a large collective could be deployed. Thereby developed the city society of Sumeria, the written and archaeological record of which [16] demonstrates a cultural organisation that remained extant until post-Columbian Europe.

The period between the Sumerians and Columbus was characterised by slow technological progress. The major power source was manpower, with the addition of beasts of burden. Technology was employed in specific contexts: sails for ships and windmills, waterpower where available. Moreover, citizens of slave-owning societies, such as Greece and Rome, were not favourably disposed towards technology because economic production was the role of slaves; consequently, artisans had little higher status than slaves. Hence technology tended to develop in isolation from the wider society, in the hands of artisan families. Horsepower also was a late arrival: the stirrup and horse-collar are both post-Roman [17].

The discovery of America by Columbus five hundred years ago altered the resource input, and thereby processing requirement, of Western Europe radically. The new continent made available a huge increase in primary product, produced along traditional lines including the use of slave labour. The problem was in getting it back to European markets. The Atlantic created a bottleneck, which could only be circumvented by technological development. Hence, naval architecture and navigational technology became critical to increasing the capacity of the Atlantic conveyor and technology became respectable. A consequence of increasing input of raw materials was that the product-processing requirement in Europe, which initially increased work available to the population, finally exceeded manpower capabilities. Within two hundred years of Columbus’ first voyage practical heat engines were developed as primary power sources for increasingly mechanical production [18]. The introduction of integrated high-pressure steam engines and electricity at the start of the nineteenth century ended the primacy of manpower in production completing in part our technological genesis. Only information processing remained a wholly human craft activity.

The process of switching manpower to administration began at this time. Lancaster devised the ‘monitor system’ for mass-producing a literate and numerate human economic resource. From this, economically oriented elementary schools arose [19]; to displace elite ‘grammar’ schools two centuries later with the introduction of the English National Curriculum [20]. Technological alternatives to human brainpower for information processing were sought [21]. The process that began with the Jacquard culminated in the stored-

196

program digital computer a century and a half later]; Noyce and Moore did for information technology in 1968 what Trevithick had for power engineering in 1797: integrated and miniaturised.

3.2. Writing

Writing is a technology. All human cultures have highly developed systems of realistic and formal symbolic representation, but only complex societies require writing. For resource balanced hunter-gatherers, and simple environmental managing and agricultural societies, verbal communication supported by tokens and seals sufficed to document trade and administer the culture. In the far more complex resource unbalanced intensive agricultural city-based cultures of Sumeria, and Egypt, a technological solution was necessary: writing.

Language has one inbuilt constraint: words can only name things, attributes and actions. Thus, the word ‘frog’ names an animal; the word ‘green’ names its colour; and the word ‘jumps’ names its action. Speech is further constrained by its ephemeral character and the limitations of human memory. Writing overcomes this deficit. Early writing systems were initially pictographic but soon shifted to representing language [22]. Writing can encode language at different levels. The ‘word root’ level has a symbol for each morpheme, e.g. Chinese and Sumerian. This system can be independent of the sounds of spoken language but requires many separate symbols. Alternatively, writing can represent the spoken language by encoding meaningful acoustic information at syllable, consonant or alphabet level. The choice is motivated by best fit to the phonetic rules of a given spoken language. Whatever method is adopted, the graphic technology of writing disambiguates language. The grammatical and logical structure of speech is made more apparent. But coding choice is not neutral: Greek alphabetic script, for example, facilitated the development of logic.

Writing was not restricted to encoding language; number and shape representation preceded written language, transcending linguistic representation. As with text, representation shifted from concrete to formal. The Hindu-Arabic numeral system proved far more powerful than precursor systems designed to record abacus operations [23]. Geometrical representation proved extremely powerful for expressing mathematical ideas, including the development by Newton of the infinitesimal calculus and in producing accurate engineering drawings.

Graphic power can have less useful side effects. Some are to be seen the notions of revealed knowledge and textually authority within book-based religions and political philosophies, one of which constrained the intellectual climate of medieval Europe, an effect that persists.

The technology adopted for writing constrains how it may be conceptualised [24]. Chinese characters, though capable of being cast as moveable type are too numerous for easy classification. In Arabic script the letters change form to aid the flow of the penned line. Only an alphabet, e.g. the Roman, has a sufficiently small set of unique characters to make feasible a formal description. Pacioli described Roman letters geometrically, Guttenberg as manufactured objects, and Morse as electrical pulses. The latter technologies are applicable to any system of symbols in which sequence is more important than form. This enabled all such systems, including those of mathematics and logic, to be described in binary notation.

The final step in integrating biologically evolved language with inarticulate technology was the development of a logical machine capable of implementing algorithms [25]. Described first as a thought experiment by Turing, the first practical computer ran a program in 1948. This marked the point at which our species became able to represent processes actively. The biological ‘naming only’ constraint on language has been circumvented by technology.

3.3. Science

The earliest written descriptions of how the universe worked are highly anthropomorphic. In Sumer superhuman spirits called things into being by naming, which animated them by incorporating a spirit. Both man and pickaxe were in the hands of a spirit and all processes were spirit animated. Propitiation of spirits was the key to successful action; ritual, taboo and craft lore codified consistencies. Writing made possible a more reflective view, and the literate Hebrews reduced the pantheon to a single divine lawgiver; coupled with Greek [26] natural philosophy texts, this led to the Newtonian notion of discovering how a supreme

197

architect had ordered the universe. The impact of technology began to be felt in the Renaissance when illustrators of printed versions of Galen’s medical works ‘corrected’ the text with anatomically accurate drawings. Escalating technology caused by the Atlantic bottleneck catalysed more a utilitarian view [27]. Both Watt and Trevithick consulted scientists on steam engine efficiency: thermodynamics was born out of the price of coal.

The high status given to ancient learning in the Renaissance bequeathed us, in the literary rationalisations of idle rich kids who hung around the Agora, a distorted view of science. Science began about 1800 when man stopped being the measure of all things and chemistry ceased to be cookery. The electric dissociation of water marked the point where our species dropped reliance on biologically evolved senses and began to trust data from technology. This also marks a second triumph of technology over language.

4. Aside

The relationship described between language and technology has potential consequences for mathematics. Language can only name things, their actions and attributes. A Turing machine is needed to animate processes. Goedel’s undecidability proof relies on encoding language as number. If language is less powerful than technology, might it possibly be that a limitation on language has been demonstrated rather than a limitation on mathematical thought? If so, the angst and intellectual legerdemain undertaken by writers concerned with our capability to think the uncomputable [28] might possibly be seen in a different light.

5. Education

Passing attempts by workers such as Piaget apart, little attempt has been made to establish a scientific basis for education. Evolutionary approaches offer such a possibility, by making possible the separation of biology-based from technology-based processes. Specifically the evolutionary paradigm enables us to tease out the differences between the capability of any neurosystem to learn, learning associated with language, and technology oriented learning.

5.1. Learning

The stimulus-response type of learning described by Pavlov and Skinner is characteristic of a neurosystem, biological or computational. It does not exercise highly evolved capabilities.

Language-oriented learning, which includes the natural development of mother tongue in infants, suffers from an intrinsic disconnection from reality. Language is a set of arbitrary sound sequences that defines a system of semi-independent learned semantic relationships, held together by a localisation of universal grammar. Pragmatic correlation with real events is imperfect, and the language module has the power to negate reality*. Vigotsky [29] and Piaget [30] explored its relationship to learning, perhaps assigning it unwarranted primacy.

Technology-based learning is entirely different. Uniquely human, and including music, art and mathematics, it is characteristically inarticulable: learning proceeds constructively by building up technological knowledge and skills in the making and using of tools. This is the set of knowledge and skills that artisans learn through apprenticeship. It is creativity, and is learned by a process that educators have yet to describe, which Papert calls bricolage [29].

5.2. School

For none of the above forms of learning is education necessary. Schools have existed since Sumerian times, when they provided vocational training for administrators. Literacy and numeracy then, as now, was the core of education [30]. This suggests a specific objective for education: teaching children the art of bringing language under graphical control, and of using graphics to describe the world. Education is about formal abstract graphic systems.

198

Knowledge is no more acquired through unguided bricolage than by operant conditioning or verbal formulas; a curriculum is required. The problem with any curriculum for universal education is that the latter is a mass-production system driven by economic requirements. For the past two centuries the demand has been for information processing, hence skill in literacy and numeracy, and now information and communication technology, are its core. For an industrial society, add science, the knowledge base, and design and technology, its practical aspect; provide an environmental and cultural context through geography and history, with art and music to contribute a cultural aesthetic; lastly, add a second language.

Writings of educational philosophers e.g. Dewey notwithstanding, the educational medium has always been the text, printed since the industrial revolution. The relationship between a text and the mind is straightforward: the text provides visual data, in words or drawings; the mind contributes the processing. This proved problematical for technology, which appears inaccessible to curriculum designers [31] and leads to a mere catalogue of craft activities.

Devised for text as the representational medium. Today’s educational problem is that the medium is ceasing to be text. ICT, unlike text, is bricolable: the medium itself can support learning because that which is constructed may be evaluated for aptness within the medium.

6. A tentative re-evaluation of Logo-like

The term ‘Logo-like’ is not well defined but includes notions of constructive learning and graphic representation. Logo, Boxer and ToolTalk are all generally considered Logo-like. The Logo Turtle, an object-to-think-with resonates with object lessons of the late nineteenth century [32]. The power of the graphic is illustrated by the wide perception that Logo and turtle graphics are one and the same. Both Comenius Logo and MicroWorlds have majored on graphic representation, whilst Lego-Logo has become fully graphic in Mindstorms. Boxer uses a graphic metaphor, though its originator has a language focus [33]. ToonTalk is entirely graphic. It is argued that graphic representation makes for ‘easier’ accessibility. This paper’s thesis is that graphic representation may be more (inarticulately) intelligible. The following eggsamples are illustrative:

6.1. Turtle Turing m/c

It is possible, using MicroWorlds Pro, to represent a Turing Machine on a computer screen. The tape is a series of cells and the Turtle can be made to move from cell to cell. At each move the Turtle tests the cell colour and changes state accordingly. Illustrated is ‘addition’; our interpretation as an observing Demon with access to both data and instructions.

6.2. Developing Tray

The frightening thing about pen and paper is making mistakes. ICT can check out spelling and make our writing neat. We could also use LogoWriter (and now MicroWorlds Pro) to explore the graphic composition of text. Illustrated is a Logo project that can hide or reveal letters and letter combinations. We, as Demons, can use programs to describe the processes.

199

6.3. Farmer

Complex verbal problems can be made more easily soluble if we combine the power, and universality, of both graphic and process representation. Illustrated is a Comenuis Logo implementation of software produced in 1981 by the UK Microelectronics Education Program. The, well known, problem is to get farmer, chicken and grain across the river safely given that: a) the farmer and only one item may cross at a time; and b) if left alone, the dog will eat the chicken, and the chicken will eat the grain. The author [34] found pictorial representation to significantly reduce the mean age at which the problem was solved.

7. Conclusion

Universal elementary education was developed in the nineteenth century to provide human information processing capacity to complement industrial productive capacity. In the past half-century industrial information processing has arisen to provide this very complement. The economic imperative for education has changed; the new requirement is to complement both production and computation. This offers the potential to develop a genuinely humanist educational curriculum. The route to such a curriculum is not, however, to be found within the domain of philosophy. Scientific understanding of our evolved capacities is required. Environmental changes over the past 50,000 years suggest that our species represents an evolutionary phase-change: the transition from biology to technology. It is the thesis of this paper that the power of our intellect is inarticulately expressed as graphics and technology. Language, intrinsically affective, has been made more reasonable by technology, beginning with writing and culminating in the Turing machine. Logo-like educational software has a very specific contribution to make in bridging the technology-language chasm.

References

1. Oxford English Dictionary (Second Edition) CD-ROM, Oxford: Oxford University Press, 1999.2. Logo Philosophy and Implementation, Quebec: LCSI, 1999.3. West J, Unpublished Logo survey, Manchester Metropolitan University, UK, 2001.4. Dawkins R, The Selfish Gene, Oxford: Oxford University Press, 1976.5. Barrow JD, The Universe that Discovered Itself, Oxford University Press, 2000.6. Stonier T, Information and the Internal Structure of the Universe, London: Springer-Verlag, 1990.7. Tudge C, The Day Before Yesterday, London: Pimlico, 1996.8. Goodall J, The Chimpanzees of the Gombe, 9. Lewin R, Principles of Human Evolution, Malden, Mass.: Blackwell Scientific, 1998.10. Dawkins R, The Extended Phenotype, Oxford: Oxford University Press, 1999.11. Pinker S, The Language Instinct, London: Penguin, 1994.12. Deacon T, The Symbolic Species, London: Penguin, 1997.13. Carpenter GA & Grossberg S, Adaptive Resonance Theory (ART), IN Arbib MA (Ed), Handbook of Brain

Theory and Neural Networks, Cambridge, Mass: The MIT Press, 1995.14. Carter R, Mapping the Mind, London: Phoenix, 200015. Scientific American, Hunters, Farmers and Civilisations: Old World Archeology, San Fransisco: WH Freeman

and Company, 1979.16. Crawford H, Sumer and the Sumerians, Cambridge: Cambridge University Press, 1991.17. Asimov I, Asimov’s Chronology of Science and Discovery, London: Grafton Books, 199018. Trevithick F, The Life of Richard Trevithick Vols. 1&2, London: E&FN Spon, 1872.19. Gordon P & Lawton D, Curriculum Change in the Nineteenth and Twentieth Centuries, London: Hodder &

Stoughton, 1978.20. Graham D with Tytler D, A Lesson for Us All: The making of the national curriculum, London: Routlege,

1993.21. Augarten S, Bit by Bit: An Illustrated History of Computers, London: George Allen & Unwin, 1984.22. Sampson G, Writing Systems, London: Hutchinson, 1987.

200

23. Dilke OAW, Mathematics and Measurement, London: British Museum Press, 1987.24. Jackson D, The Story of Writing, Monmouth: The Calligraphy Centre, 1981.25. Berlinski D, The Advent of the Algorithm, New York: Harcourt, 2000.26. Lindberg DC, The Beginnings of Western Science: The European scientific tradition in philosophical, religious

and institutional context, 600BC to AD1450, Chicago: The University of Chicago Press, 1992.27. Hall RA, The revolution in science 1500 – 1750, London: Longman, 1983.28. Penrose R, The Emperor’s New Mind, London: Vintage, 1990.29. Vigotsky LS, Thought and Language, Cambridge Mass: MIT Press, 196230. Piaget J, Language and Thought of the Child 3rd Edition, London: Routlege & Kegan Paul, 1959.31. Papert S, The Children’s Machine, London: Harvester Wheatsheaf, 1994.32. Boyd W & King EJ, The History of Western Education (11th Edn.), London: Adam and Charles Black, 197533. Barber M (Ed), The National Curriculum: A Study in Policy, Keele: Keele University Press, 1996.34. Papert S, Mindstorms, Brighton: Harvester, 1980.35. diSessa A, Changing Minds: Computers, Learning, and Literacy, Cambridge: MIT Press, 2000.36. Doyle MP, The Computer as an Educational Medium, unpublished MPhil thesis, Manchester University,

England.

*“The whole nation slid into doublespeak. Words became divorced from reality, responsibility, and people’s real thoughts. Lies were told with ease because words had lost their meanings – and had ceased to be taken seriously by others.”

Jung Chang, Wild Swans, 1991

201