Conversation Theory: A Constructivist, Dialogical Approach to Educational Technology

Bernard ScottLearning Environments and Technology UnitUniversity of the Highlands and Islands Millennium InstituteLews Castle CollegeStornowayIsle of LewisHS1 2SDUKEmail bernard.scott@lews.uhi.ac.uk

Scott, B. (2001). “Conversation theory: a dialogic, constructivist approach to educational technology”, Cybernetics and Human Knowing, 8, 4, pp. 25-46.

Abstract

This paper overviews conversation theory, as developed over three decades by Pask, Scott and others, with particular emphasis on its application to the field of educational technology. Topics covered include models for learning and teaching, individual differences in approaches to learning, CASTE (Course Assembly System and Tutorial Environment) and associated principles for course design and tutorial strategies, knowledge and task analysis and knowledge representation for course design. The paper begins with a brief biographical note on the life and work of Gordon Pask and ends with some examples of current applications and some thoughts about the role of conversation theory in future developments in educational technology.

Keywords

Conversation theory, Gordon Pask, holist, serialist, comprehension learning, operation learning, entailment structure, task structure, entailment mesh

Introduction

Conversation theory, as developed over three decades by Pask, Scott and others, can serve as a conceptual framework that brings order and sense to the evolving and complex area of application known as educational technology. Conversation theory provides clearly formulated pedagogic principles that help show how to deploy technologies to meet particular educational objectives. It also provides a set of concepts and principles that address more general issues concerning human systems and their organisation which, going beyond the strictly pedagogic, can help educational technologists and others appreciate and address the administrative, political and cultural implications of the new technologies.

It is not possible in a short paper to convey all that conversation theory has to offer. Rather, the paper is selective and summary, focussing on three main themes with, hopefully, enough coverage to inspire the interested to consult more substantial primary sources. The three themes are:

1. conversation theory as a theory of learning and teaching, particularly suited for understanding how to support effective student-centred learning when using learning technologies for the on-line delivery of resource based learning;

2. conversation theory as a source of principles of course design and the design of learning environments;

3. conversation theory, with its subtheory of conversational domains, as a source for methodologies of knowledge and task analysis, particularly suited for eliciting and representing the structure of course content in an on-line hypertext environment.

The paper is organised as follows. First there is a brief biographical note about the life and work of the late Gordon Pask. There is then a summary of conversation theory interpreted as a theory of learning and teaching. There is also a brief mention of some variants of conversation theory to be found in the educational technology and teaching and learning literature. Next, there is a description of CASTE (Course Assembly System and Tutorial Environment), developed by Pask and Scott to be, in Pask’s phrase, an embodiment of conversation theory. CASTE includes a formally specified set of tutorial heuristics that exemplify the design of tutorial strategies for ensuring that learners achieve mastery of a particular knowledge domain. Reference is made to work carried out on individual differences in styles and strategies of learning. CASTE is useful as an exemplifier of a principled approach to course design and for the design of student-centred learning environments. Reference is also made to the Thoughtsticker system, a more sophisticated course design and knowledge elicitation and representation tool developed subsequent to CASTE, which exemplifies methodologies for knowledge and task analysis and knowledge representation. There is a brief overview of the conversation theory subtheory of conversational domains and narrative structures from which the methodologies were derived. Finally, there is a discussion of the relevance of conversation theory for educational technology today with examples of current work where conversation theory is a major influence.

What I have tried to do here and in related papers is to provide non-technical accounts of conversation theory. Pask’s own writing are notoriously difficult, chiefly because he uses specialised notation schemes and technical vocabularies, both of which evolved over time, but also because he had a polymath’s eye for detail and possibly contentious issues whose relevance might not be obvious to a single discipline specialist. He was aware of leading edge developments in logic and theories of computation, in cognitive psychology and neuroscience and in work on artificial intelligence and robotics. In addition, he saw conversation theory as a useful framework for adumbrating a wide range of other work on human communication and human systems and on the design of man-machine systems and interactive environments.

Some of the material gathered together here in summary form can be found in more elaborated versions elsewhere (Scott, 1999, 2000, 2001a, 2001b, in press a).

About Gordon Pask

Gordon Pask (1928-1996) was a UK scientist and inventor, one of the founding fathers of cybernetics, the study of control and communication in complex natural and artificial systems. See Pask (1961) and Scott (1980, 1982, 1993, 2001c) for more on Pask’s approach to cybernetics. In his regular column in this journal, Ranulph Glanville has also frequently written about Gordon Pask. Pask’s many interests included work as an experimental psychologist studying styles and strategies of learning and as a pioneer educational technologist developing adaptive teaching machines. CASTE (Course Assembly System and Tutorial Environment) was developed in the early 1970s. CASTE built on many lessons learned from the design and development of adaptive systems for the teaching of perceptual-motor skills, applying them, with refinements, to the teaching of complex intellectual subject matter. Much of the pre-CASTE work is described in Pask (1975a).

Pask’s ideas about learning and teaching were articulated as conversation theory. In Pask’s phrase, CASTE was an embodiment of conversation theory. Conversation theory includes a subtheory of “conversational domains” and associated knowledge elicitation methodologies for carrying out knowledge and task analysis (Pask and Scott, 1973; Pask, Scott and Kallikourdis, 1973; Pask, Kallikourdis and Scott, 1975; Pask, 1975b, 1976).

Others have developed variants of conversation theory, with applications to learning and teaching, learning to learn and the design of educational systems, e.g., Harri-Augstein and Thomas (1991), Laurillard (1993), Boyd (1993).

Gordon Pask is rightly considered as one of the fathers of both first order and second order cybernetics. The latter refers to the novel epistemological paradigm where the observer, rather than be a purely external observer to the systems he studies, is invited to acknowledge that he, too, is a system, an observing system. Conversation theory, with its accounts of observers in conversation and interaction, was a major contribution to the emergence of this paradigm. That grand old man of cybernetics, Heinz von Foerster (whom Pask considered his mentor), calls Pask “the cybernetician’s cybernetician” and talks of him, in some awe, as a genius (von Foerster, 2001). Overview of Conversation Theory

Although conversation theory can be elaborated as a general theory of human communication and social interaction, here, for the sake of brevity, it is interpreted as a theory of learning and teaching, in which one participant (the teacher) wishes to expound a body of knowledge to a second participant (the learner). Parts of the body of knowledge are referred to as “topics”; the term “concept” is reserved for the mental procedures that indicate understanding of a topic. Particular instantiations or models of topics are referred to as “relations”, which may be defined canonically with respect to a particular universe of discourse and associated modelling facility (see figure 1).

Underlying assumptions of conversation theory include the following. The brain/body system is a dynamic self-organising, “variety eating”, adaptive and habituating system, subject to boredom and fatigue. As Pask often put it, “Man is a system that needs to learn”, thus the problem of motivation is not “that we learn” it is rather what is learned and why. The basic mechanisms that support learning and adaptation are the various forms of conditioning that take place in associative networks (parallel distributed systems), with attentional systems subject to sensory-motor feedback (including proprioception and kinaesthesia) and algedonic (pain, pleasure) feedback. For humans, learning is also about the construction of symbolic representations, subject to constraints of logical coherence, acquired through the medium of dialogic, conversational interaction and the inner dialogic processes of strategic and tactical attention directing. In conversation, narrative forms are constructed and exchanged (Scott, 1999, Laurillard, Stratfold et al 1999, Bruner 1996). What is memorable is that which can be “taught back” (Pask and Scott, 1972).

Habitual forms of behaving and thinking evolve by proceduralisation. Proceduralisation may be guided and monitored by learning and teaching strategies. In conversation theory, remembering is understood as a dynamic process of reconstruction that is always contextualised and social (minimally, with no other person present, a psychological individual remembers with herself). If we do use the metaphor of memories being “stored” in our brains and bodies, we should recognise they are also “stored in our environments including the brains and bodies of other people”. This is a Pask aphorism that predates the more recently articulated concepts of “situated” and “distributed” cognition.

Pask argues that the distinctions required to characterise the cognition of an isolated psyche are the same as those made by the external observer of a conversation (cf. Ryle, 1971). In the “outer conversations” that constitute social institutions, the participants agree and disagree and negotiate shared descriptions, explanations and justifications. In her “inner conversation”, a person explains and justifies herself to herself.

In order to clearly conceptualise the idea that cognition is conversational in form, conversation theory distinguishes a type of organisation, the psychological (p-) individual, that is distinct from the biological and which is applicable both to persons and the social systems that they form. The participants in a conversation are p-individuals. The conversation is itself a p-individual.

A p-individual is formally defined as a stable, organisationally closed systemic whole, a “self-replicating system of memories and concepts”, where:

1. a concept is a procedure that “recognises, reproduces or maintains a relation”, e.g., in context, riding a bicycle, performing a calculation;

2. a memory is a metacognitive procedure that “recognises, reproduces or maintains concepts”, for example, in context, justifying a method or providing a chain of explanation showing how the understanding of particular topic is derived from or entails the prior understanding of other topics;

3. a description of a concept is a “task structure” that says “what may be done”; 4. a description of a memory is an “entailment mesh” that says “what may be known”

(see also below).

Pask refers to a biological individual in general terms as a mechanical (m-) individual. Note the power of the distinction between the two kinds of individual: m and p-individuals are not necessarily in one to one correspondence. One m may house several p; one p may be housed by several m’s.

Minimally in a conversation the participating p-individuals distinguish and learn about each other. Where there is a particular topic under discussion, participants construct models of each other’s models of that topic. The particular conceptions and misconceptions (kinds of understanding) that participants have of the topic in question can, following Aristotle and others, be broadly classified as “knowing why” and “knowing how”. In conversation participants may share both kinds of knowledge, participants exchange theories and present evidence in support of those theories, in this sense conversation theory is a theory of theory construction and elucidation.

The basic conversation theory model is shown in figure 1. Pask refers to this model as the “skeleton of a conversation”. It shows a “snapshot” view of two participants in conversation about a topic. The model distinguishes verbal, “provocative” interaction (questions and answers) from behavioural interaction via a shared modelling facility or “micro-world”.

The horizontal connections represent the verbal exchanges. Pask argues that all such exchanges have, as a minimum, two logical levels. In the figure these are shown as the two levels: “how” and “why”. The “how” level is concerned with descriptions of how to “do” a topic: how to recognise it, construct it, maintain it and so on; the “why” level is concerned with explaining or justifying what a topic means in terms of other topics. These exchanges are “provocative” in that they serve to provoke participants to construct understandings of each other’s conceptions and (possibly) misconceptions of topics and the relations between them. This is the essential aspect that makes conversation theory constructivist and dialogical in approach and clearly distinguishes it from other approaches that see teaching as the transmission of knowledge from teacher to learner.

The vertical connections represent causal connections with feedback, a hierarchy of processes that control or produce other processes. At the lowest level in the control hierarchy there is a canonical world, a “universe of discourse” or “modelling facility” where the teacher (or computer-based surrogate, as in CASTE, below) may instantiate or exemplify the topic by providing non-verbal demonstrations. Typically, such demonstrations are accompanied by expository narrative about “how” and “why”, the provocative interactions of questions and answers referred to above. Note that the form of what constitutes a canonical “world” for construction and demonstration may itself be a topic for negotiation and agreement.

In turn, the learner uses the modelling facility to solve problems and carry out tasks set. He or she may also provide narrative commentary about “how” and “why”. In a computer-based environment these may be elicited using computer aided assessment tools with a variety of different question styles.

Receives or offersexplanation in termsof relations between

topics

Receives or offersexplanation in termsof relations between

topics

Offersdemonstrations orelicits models andproblem solutions

Why questionsand responses

Receivesdemonstrations,builds models orsolves problems

Modelling facility for performance of taskssuch as model building and problem solving

Why?Receives or offers

explanation in termsof relations between

topics

How?

Teacher Learner

How questionsand responses

Figure 1. The “skeleton of a conversation” (after Pask).

Pask refers to learning about “why” as comprehension learning and learning about “how” as operation learning and conceives them both as being complementary aspects of effective learning.

The distinction between “how” and “why” allows for a formal definition of what it means to understand a topic. In conversation theory, understanding a topic means that the learner can “teachback” the topic by providing both non-verbal demonstrations and verbal explanations of “how” and “why”.

Some variants of conversation theory

Laurillard (1993), drawing on conversation theory, provides an elaborated account of the exchanges that make up the skeleton of a conversation, interpreted for the kinds of learning conversation that take place in Higher Education. She distinguishes a domain of exchanges of descriptions, conceptions and misconceptions about both “how” and “why” from a general domain of “tasks”. “Tasks” are interpreted liberally as any learning activity the learner is asked to engage in which generates some product or outcome. In exchange for generating the product, the learner receives feedback about the quality of the product (formative assessment). In turn, the teacher adapts tasks to fit a learner’s current level of competence. Throughout, conversation continues with exchanges of conceptions and misconceptions, tasks completed and feedback received engendering topics for further discussion. The Laurillard “conversational framework” has become widely used in the UK, particularly for informing discussions about how best to deploy learning technologies and multimedia. In some learning contexts, there is a major emphasis on the higher level, generic processes of becoming an effective learner, “critical thinker” or “reflective practitioner” rather than on particular processes of learning in a subject domain (Harri-Augstein and Thomas, 1991; Schon, 1983; Novak and Gowin, 1984; Brockbank and McGill, 1998). Apropos of this, Pask notes that conversations may, by recursive “laddering”, have many logical levels above the basic “how” and “why” levels: levels at which conceptual justifications are themselves justified and where there is “commentary about commentary”. Indeed, reflexively the conversation may itself become a topic of conversation. Harri-Augstein and Thomas (op. cit.) make these notions central in their work on “self-organised learning”. Their emphasis is on helping students “learn-how-to-learn” rather than just on mastery of a particular subject domain.

In brief, Harri-Augstein and Thomas propose that a “full learning conversation” has three main components:

1. conversation about the “how” and “why” of a topic, as in the basic conversation theory model;

2. conversation about the “how” of learning (for example, discussing study skills and reflecting on experiences as a learner);

3. conversation about purposes, the “why” of learning, where the emphasis is on encouraging personal autonomy and accepting responsibility for one’s own learning.

The model in figure 2 shows the relationships between the components.

How and why of topicHow and why of topic

How of learning

Why of learning

Teacher Learner

Figure 2. A full “learning conversation” (after Harri-Augstein and Thomas)

In this brief summary, I have presented conversation theory as a theory of learning and teaching. It helps understand what is supportive of effective learning with respect to a given body of subject matter. It also helps understand what is supportive of empowering learners to become self-organised. Conversation theory is particularly useful for understanding how effectively to use learning technologies to support student-centred resource based learning. This latter theme is now taken forward by reviewing CASTE as an embodiment of conversation theory, where we shall see principles of course design and effective tutorial strategies exemplified.

Embodiment of conversation theory in CASTE

CASTE was developed by Pask and Scott in response to the need to provide learners with a description of a body of subject matter so that there could be “conversation” between a computer-based tutorial system and the learner about learning strategies. Whalley (1995), with approval, refers to CASTE as a system that “provided both a ‘virtual’ environment for the student and a system to facilitate learning conversations about it” and “clearly worked as an integrated whole”.

Pask and Scott (1972) had shown that in a “free learning” situation, with no imposed teaching strategy, students typically exhibited a preferred style or approach to their learning. Two main strategies for accessing and using learning materials were observed: a holist strategy, in which students accessed many “topics” (chunks of learning material) in order to build up an overview

of the subject matter before attending to specific detail, and a serialist strategy, where students worked in a one-step–at-once manner, learning the details of a particular topic before accessing further topics. In follow-up studies, using teaching materials embodying either a holist or serialist teaching strategy, it was shown that mismatch between a teaching strategy and a student’s preferred learning style could lead to little or no effective learning.

CASTE presented subject matter topics in a way that supported holist and serialist learning strategies. Using the conversational features of CASTE, system and learner agreed what was likely to be an effective learning strategy and established an associated “learning contract”. This latter typically included the agreement that progress was contingent on the student successfully “teaching back” what he or she had learned so far. Using these contractual constraints, effective learning to “mastery” level (Block, ed., 1971) was regularly achieved.

The main features of CASTE are shown in figure 3.

Figure 3. CASTE, main features• An entailment structure for the whole of a course – an hierarchical form of concept map showing

possible learning routes

• A modelling facility used for demonstrating topics and assessing understanding, in accordance with well-specified task structures

• BOSS (Belief and Opinion Sampling System) for sampling students’ uncertainties about topic choices and topic content.

• A communications console affording different transaction types, e.g., “state aim”, “select topic”, “elicit demonstration”, “submit explanation”.

Transactions were monitored and learners’ behaviours modelled by using a suite of tutorial heuristics, written as computer programs. In the original version of CASTE, these were accessed using a time-shared terminal (not in view). The main role of the tutorial heuristics was to specify permitted learning routes, taking into account what was known about a student’s current understanding of topics and his or her preferred learning style. When following a holist learning strategy, students were permitted to work on more than one topic at once. For serialist strategies, topics were worked on, to mastery, one at a time.

The basic rules of the system were that:1. the learner could only work on a topic, if she had demonstrated that she understood a

set of prerequisite, subordinate topics from which the topic in question could be derived (there may be several such sets if there are analogies depicted in the entailment structure);

2. having received one or more demonstrations of a topic the learner was constrained, at some stage, to produce a different demonstration to show that she understood the topic (all the transactions at the modelling facility were mechanically detectable and scorable).

A typical transaction sequence would be:1. The learner “explores” the subject domain by accessing brief descriptions of topic

content and examining the relations between topics shown on the entailment structure.2. The learner "aims for" a topic as one she wishes to come to understand. The topic must,

of course, be one that she does not already understand.3. The learner chooses topics to "work on". These may be one or several; they must be

subordinate to the aimed for topic or be the aimed for topic itself. Furthermore, all topics "worked on" must have sufficient subordinate topics already understood (as in rule 1, above).

4. The learner requests demonstrations of the topics worked on (this step is optional, the learner is free to omit demonstrations if she feels she can proceed without them): typically, the demonstrations take the form of written text and graphics showing how to set up a model on the modelling facility.

5. The learner "explains" the topics she has worked on by constructing models for the topics but (as in Rule 2) that are not just copies of the demonstrations she has received.

6. If the (non-verbal) explanation is correct, the topic in question is marked understood. If not, the learner is directed to request further demonstrations.

7. At some stage, by some route, the learner is led to demonstrate his understanding of the head topic(s) and the tutorial is over.

Overall understanding of “why” is ensured because the learner has accessed topics in a logically coherent sequence, supported by the expository narrative as revealed by the relations between topics shown in the entailment structure. In experimental studies of learning style, explanations of “why” were elicited by asking learners to justify by “teach back” why they chose a particular learning route. In fully automated tutorial mode, this requirement was approximated using multiple choice style questions, delivered on BOSS (Belief and Opinion

Sampling System), asking the learner to select from amongst a set of possible sequences that which they had followed and assign confidence levels to their answers.

Overall understanding of “how” is ensured because the knowledge and task analysis guarantees that in explaining a particular topic by modelling activities, the learner is also demonstrating understanding of “how” for all subordinate topics.

BOSS was also used to study learners’ uncertainties about topic content and about their strategic plans. Clear differences between learners following serialist and holist strategies were observed. A learner following a serialist strategy would typically be confident about the content of a topic selected to be worked on but would exhibit uncertainty about the topic sequence she intended to follow. In contrast, a learner following a holist strategy would not be so confident about the content of topics (usually more than one) selected to be worked on but would be quite confident about the topic sequence she intended to follow.

Having overviewed CASTE as a teaching system and as a learning environment in which students’ learning strategies can be observed, in the next section there is a brief description of how the principles used in the design of CASTE can serve as general principles for guiding effective course design. As we shall see, these principles are particularly applicable to the current generation of web based learning environments, although it is fair to say that none, as yet, has the full sophistication of CASTE. Principles of Course Design

Figure 4. A framework for course design

The chief components of a course are shown in figure 4. A main principle, perhaps the main principle, of course design is that there should be explicit relations between these parts, as indicated in the figure: all items of any of the four components should be mapped onto corresponding items of other components. Learning outcomes should have corresponding content; content should be served by effective tutorial strategies; assessment should map in a comprehensive and fair way back onto course content. Biggs (1999), in similar spirit refers to the need for there being a “constructive alignment” between the components of a course. In CASTE, the components conform to that principle.

CASTE exemplifies other principles of good course design, relevant for on-line, web based resource based learning.

More specifically:

Knowledge and task analysis leads to the specification of entailment and task structures. This ensures that for each topic, the “why” and “how” learning outcomes are clearly specified.

Entailment structures serve as “advance organisers” (Ausubel, 1968; Rowntree, 1990), providing the learner with descriptions of how the subject matter is structured.

Tutorial strategies for sequencing learning experiences can be made explicit with reference to the entailment structure

There is a well-defined semantics for hypertext linking, that is, links are clearly identified as being of a particular kind serving a specific purpose, including, links between topics, links from topics to specific content, links from content to assessed activities and links from activities to formative feedback.

As shown in the typical transaction sequence (previous section), there is a well-defined, rich set of transactions types to support “conversational learning”.

The rules for using the system are clearly explained to learners (previous section), thus ensuring that “learning contracts” are explicit. Only by engaging in learning activities and demonstrating understanding can the learner acquire access to the contents of the conversational domain.

Formative and summative assessments leading to mastery learning are made explicit.

There are overlaps between conversation theory and other approaches to instructional design (e.g., Romiszowski, 1984; Gagné et al, 1992; Seels, 1995). Arguably, the CASTE principles provide a more coherent theoretical framework, a simpler taxonomy of learning processes and simpler terminology. This is in contrast to other well-known approaches, which, though useful for the professional instructional designer, are cumbersome and unwieldy for the working academic wishing to produce learning materials.

Gagné, Briggs and Wager (1992) distinguish motor skills, discriminations, intellectual skills, defined concepts, concrete concepts, cognitive strategies, attitudes, problem solving, verbal information (names or labels, facts, knowledge), rules and higher-order rules.

Romiszowski’s (1984) classification is even more complex. He distinguishes four main kinds of “knowledge” (facts, procedures, concepts, principles), and four main kinds of “skill” (cognitive, psychomotor, reactive, interactive), with further subdivisions.

The CASTE/conversation theory approach merely refers to a learner acquiring understanding of a topic if he knows the “how” and the “why”.

Theoretically, a consistent correspondence is maintained between, on the one hand, task structures and the “how” of operation learning and, on the other hand, between entailment

structures and the “why” of comprehension learning. Experience shows that this terminology and associated set of distinctions can be readily appreciated and put to use by academic teaching staff, following relatively brief staff development workshops.

The claim that a system like CASTE can lead to effective learning critically rests on the foundation of a prior detailed analysis of the subject matter to be talked about – the conversational domain. In the next section, there is a closer look at the conversation theory subtheory of conversational domains and the associated methodologies of knowledge and task analysis.

Conversational domains and expository narratives

The entailment structure in CASTE is a non-linear representation of the structure of a body of subject matter. As such it affords many different learning routes, each of which corresponds to a different expository narrative.

Narrative structures, stories and expositions can be described in a variety of ways using scripts, frames and other, possibly computer implemented, schemas. This is generally done by carrying out “after the event” analyses of discourse and written text. Such structures represent only a limited view of the knowledge content of a conversational domain. For non-linear hypertext domains what is needed, and what is provided by conversation theory, is a canonical way of describing the structure of knowledge content such that all possible narrative structures can be revealed and articulated as particular forms. This is particularly relevant for course design and communication in a hypertext environment, such as the World Wide Web.

In this section, we first look at how conversation theory models a conversational domain as a coherent, “globally cyclic entailment mesh”. Next, we retrace our steps in order to reveal the set of possible narrative forms that a particular conversational domain may engender. Some conversation theory terminology is introduced but formal notation schemes are eschewed in favour of diagrammatic representations of key ideas. Finally, there is a brief description of Thoughtsticker, a knowledge elicitation and course design tool that embodied conversation theory principles in its operations.

In conversation theory, the starting point for constructing models of knowledge (Pask’s preferred term was “knowables”) is the basic idea that a body of knowledge or subject matter consists of a set of topics related one to another, i.e., a conversational domain is not a collection of fragments, it is a coherent whole, a gestalt. This idea of the wholeness of a conversational domain is central to conversation theory. It relates directly to the concept of a p-individual as an organisationally closed, self-reproducing system of memories and concepts. The conversational domain is a static image of the processes of knowing and doing that constitute the conversation as p-individual. A domain that lacked coherence would not support the processes of p-individuation; it would not be memorable and reproducible.

Two basic forms of relations between topics are distinguished: entailment relations (hierarchical) and relations of analogy (heterarchical). A static representation of such relations is called an entailment structure. A simple example is shown in figure 5.

Figure 5. The diagram shows two simple entailment structures in distinct universes of discourse related by analogy. Universe I is concerned with “means of grasping”; Universe II is about “means of walking”; the joint analogical universe is about “limbs and their uses”. Topic A entails topics B and C; topic P entails topics Q and R. The analogy has the overall form “A is to B and C as P is to Q and R”, for example, “Hand is to finger and palm as foot is to toe and sole”. The key similarity is one of structure; the key difference is one of function.

Entailment structures reveal the “why” of knowledge, the conceptual structures that relate topics one to another. For a full semantics, the content of topics, their “how”, needs to be specified. This can be done operationally in the form of task structures, defined with respect to a canonical modelling facility. In Pask’s phrase, task structures show “what may be done”. They show the “procedural knowledge” or “performance competencies” that someone who understands a particular topic is deemed to have (figure 6).

Figure 6. For each topic in an entailment structure there should be an associated task structure, which gives operational meaning to the topic. For example, for the topic “hand”, the learner could be asked to draw a diagram of a “hand” or assemble a “hand” from component parts.

Task structures may be represented in a variety of ways, for example, as a precedence chart showing order relations between the goals and sub-goals of a task or as a flow chart showing a

sequence of operations, tests, branches and iterations. Figure 7 shows a precedence chart; figure 8 shows an example flow chart.

Figure 7. A precedence chart representation of a task structure showing the possible orders in which subgoals may be achieved to achieve goal D. As an example, the diagram shows that both of the subgoals A1 and A2 need to be achieved before subgoal B1 can be achieved but A1

and A2 can be achieved in any order,

Figure 8. A flow chart representation of a task structure, particularly useful for tasks where operations are ordered linearly and frequently repeated. The example shows a control process

for maintaining the value of output variable Z given information about input variable X and a target value of Y.

In order to encompass the idea of the coherence of a conversational domain, the idea of a body of knowledge being a learnable, memorable whole, it is necessary to extend and elaborate the entailment structure model as follows.

(i) Any topic at the lowest level of an hierarchical entailment structure may be analysed further in order to reveal sub-topics. The conversation theory term for such an analysis of a topic is unzipping. For example, in response to the question, “What is ‘a table’?”, the topic “table” could be unzipped to reveal subtopics concerned with “having legs” or “having a flat surface”.

(ii) An entailment structure may be embedded within a larger hierarchical form. For example, in response to the question, “What is ‘a table’?”, the response could be that it is an item of “furniture”; in turn, topics to do with “furniture” may be embedded within a larger structure of topics concerned with “human dwellings”.

(iii) Topics may be explained in terms of each other in different ways. If an entailment structure shows that topic A can be explained in terms of entailed topics B and C, then, in principle, topic B can be explained in terms of topics A and C and topic C can be explained in terms of topics A and B. For example, “table” may be cited in an explanation of “furniture” and vice versa. If these local cycles are added to an entailment structure, the resulting form is referred to in conversation theory as an entailment mesh.

These ideas are summarised in figure 9.

Figure 9. Extensions to an entailment structure by unzipping and embedding; local cycles have been added to form an entailment mesh

As modelled so far, the conversational domain, qua entailment mesh, is but a labyrinth of topics, albeit with extensible “edges”. It does not represent the intuitive idea that a body of knowledge is coherent globally, as a totality. Traditional approaches in the philosophy of knowledge have been wary of the idea of circularities in subject domains, fearing paradox and contradiction. It perhaps should be pointed out that it is only in recent years that mainstream philosophers have accepted the legitimacy of virtuous (i.e., not vicious) circularities in conceptual systems.

The celebrated and well-respected English philosopher, P.F Strawson, has recently signalled a more open approach to the issue of circularity.

Strawson (1992) expresses the general idea thus: “Let us imagine ... the model of an elaborate network, a system, of connected items, concepts, such that the function of ... each concept, could ... be properly understood only by grasping its connections with the others, its place in the system ... there will be no reason to worry if, in the process of tracing connections from one point to another of the network, we find ourselves returning to our starting point.... the general charge of circularity would lose its sting for we might have moved in a wide, revealing, and illuminating circle.”

As an example, within the domain “biology”, in explaining the topic “evolution” one may refer to the topic “cell”; conversely, in explaining the topic “cell”, one may refer to the topic “evolution”. (For other recent formal work on circularity in explanation, see Barwise and Moss, 1996).

In conversation theory, the global aspect of coherence is modelled as follows. Imagine that the edges of an entailment mesh extend until they meet. The meeting of opposing edges, top and bottom, left and right results in a closed form, a doughnut shape or torus. The resulting closed system of concepts is a globally cyclic entailment mesh.

As noted above, a conversational domain can also be extended by analogy. This entails mapping correspondences and defining similarities and differences for two or more entailment meshes. However, for analogy relations to be coherent, the bodies of knowledge they are relating together must themselves be coherent.

Having modelled a conversational domain as a coherent, globally cyclic entailment mesh, we can now retrace our steps in order to articulate the concept of a narrative form and to reveal the set of possible narrative forms that a particular conversational domain may engender.

For the moment, a composer of an expository narrative is taken to be someone whose conceptual repertoire includes a coherent, stable conceptual system for the subject matter in question. We thus avoid considering the case where, in the act of composition, new understandings evolve. This simplifies the task of modelling narrative forms but, of course, does not do full justice to the reality of composition. In Pask’s writings, it is axiomatic that conceptual systems evolve, that “man is a system that needs to learn”. As we shall see, below, part of the function of the Thoughtsticker system is to provoke the user into developing new insights and understandings.

As above, we may model a body of subject matter as an entailment mesh. The first step of composition then becomes that of “adopting a perspective”, assuming a point of view about that which is to be explained. For example, as a knowledgeable engineer, I could possibly subsume my knowledge under the headings “signal detection systems” or “signal filter design” or “process control systems”. In each case, I would be expounding a similar set of conceptual distinctions and associated procedural competencies but would be making different assumptions about desired aims and learning outcomes, pre-requisite knowledge and the domains of interpretation (modelling facilities) I would use.

Adopting a perspective on an entailment mesh has two complementary aspects, corresponding to the complementary processes of comprehension learning and operation learning referred to earlier.

(i) A head topic is distinguished with a supporting set of subordinate, entailed topics that are its conceptual support and justification from that perspective. In essence this corresponds to the act of isolating a hierarchical entailment structure from the entailment mesh in which it is embedded. In conversation theory, this operation is referred to as “pruning”. The only cyclic connections permitted in a pruned structure are those that relate two or more simple structures analogically.

(ii) In principle and, at least, potentially, the adoption of a perspective calls forth one or more particular universes of discourse or domains of interpretation, which can be conceived of as multidimensional “spaces” in which possible relations may be recognised, constructed and maintained. As earlier, the conversation theory generic term for a canonical version of such a universe or domain is “modelling facility”.

Where the topic hierarchy is a simple entailment structure with one head topic and where the context does indeed serve as a universe of discourse that supports the permitted operations on relations, then the topic hierarchy may, by a suitable semantic, be placed in one to one correspondence with the task structures that operationally specify the content of those topics.

That is, in general, where concepts are executable as a set of operations, there is an isomorphism between the declarative, coherence aspects of conceptualisation concerned with saying why something is what it is, and the procedural aspects of conceptualisation concerned with saying how operations are carried out, how the concepts may be modelled or instantiated as relations in a particular context (modelling facility or universe).

Particular narratives composed may have different emphases, depending on the narrator’s aims. Where there is an emphasis on teaching the “how” of operational content, e.g., mastery of a set of skills, a clear mapping between topic descriptions (minimally, topic names) and particular operations is of signal importance. In military operations, such as dismantling an automatic weapon, there is a requirement to be able to “name parts” but the main emphasis is on being able to carry out operations.

Where the emphasis is on conceptual descriptions and “why” type explanations, task structures may be less well defined. They may even be tacit assumption that the student is already familiar with the basic operational topology of the universe that is being talked about, that he or she has the required set of operators to model and instantiate topics if so required. In this latter case it is not usual to insist on making explicit the isomorphism between entailment structure explanations and task structure operations. An example of this would be a lesson about the social consequences of genetic engineering, where a basic understanding of genetics is assumed.

Analogies may be thought of as topics which say how one set of topics maybe transformed into another set of topics or, equivalently, as meta-narratives that say how one narrative may be transformed into or derived from another.

When expounding analogies, different narrative sequences may be adopted depending on the students’ needs and the demands of the subject matter being expounded. For example, one may set up a framework of correspondences of description saying that topics in one universe are indeed similar to another and, at a later stage, demonstrate this by matching operations and clarifying and highlighting by demonstration just what are the similarities and the differences between the topics in the two universes of discourse. Alternatively, one may provide operational experience and conceptual description in the two distinct contexts and establish the

analogy relations at a later date. Indeed, part of a teaching strategy might be that one gives the learner the opportunity to recognise and establish those relations himself, by a process of eduction (Spearman, 1923; Mason, 19924).

Subsequent to CASTE, Pask and associates developed Thoughtsticker, a sophisticated suite of programs that support knowledge elicitation and course design processes. (See Pangaro, 2001, for a very accessible account of Thoughtsticker functions).

The key operation of Thoughtsticker is that of recommending novel perspectives and associated expository narratives. This is achieved by, first of all, eliciting from the user a “knowledge fragment”, a particular perspective and narrative form, and representing it as an entailment structure. Then, as a purely syntactic operation, Thoughtsticker adds links intended to make the fragment locally and globally cyclic.

Novel perspectives are then generated by the “pruning” operation described above. New perspectives are presented to the user as entailment structures that show putative alternative ways that she might choose to expound the subject matter. The novel perspectives may provoke new insights and understandings. It is up to the user to accept, reject or modify the proposals.

To sum up: the conversation theory subtheory of conversational domains provides methodologies for knowledge and task analysis and representation. Uniquely, the conversation theory approach addresses the idea that conversational domains are heterarchical, locally and globally cyclic gestalts. As such, they image the idea that a p-individual is an organisationally closed system of concepts and memories. The methodology supports the construction of entailment meshes from entailment structure fragments, as when knowledge is elicited from a subject matter expert and then fleshed out. It also supports the reverse process, revealing particular expository narratives imminent in a conversational domain. This latter is relevant for adapting a set of learning materials to a learner’s particular interests and learning strategy preferences. Thoughtsticker is a course design tool that helps implement the methodologies.

Concluding comments on conversation theory and the future of educational technology

In this relatively short paper, I have tried to give a reasonably substantial account of conversation theory, its genesis and some of its applications in educational technology. Pask’s influence on educational technology and related fields has been enormous and it is well beyond the scope of this paper to attempt to catalogue all the current work that is proceeding under that influence. Good recent sources are two special double issues of the journal Kybernetes, in which Pask’s life and work is remembered and celebrated (Scott and Glanville, eds., 2000(a), 2000(b)). In the papers which bear directly on educational technology, topics covered include the design of virtual and real interactive learning environments, adaptive intelligent tutoring systems, principles of course design, self-organising knowledge domains and advisory systems, self-organised learning, approaches to learning, styles and strategies of learning. Other papers address more general topics in psychology, artificial intelligence, sociology and architecture. Here, I will briefly highlight some particular areas of development with selected references.

In their studies of learning and teaching, Entwistle, Biggs and colleagues embed Pask and Scott’s (1972) work on styles and strategies of learning within their broader “approaches to learning” framework. Three main approaches are distinguished, surface, deep and strategic. As the names suggest, the surface learning approach emphasises rote memorisation and is contrasted with the deep learning approach, which emphasises learning for understanding. The strategic approach is a mix of surface and deep learning intended to maximise achievements

with respect to particular goals, such as passing exams or other forms of summative assessment. Deep learning is characterised as the versatile, effective combination of comprehension learning and operation learning deploying both holist and serialist strategies, use of a preferred style of learning being constrained by the demands of a particular knowledge domain and the forms of exposition being made available within a particular learning context. Entwistle (2001) provides a useful summary of the approaches to learning framework. Also of interest is the Enhancing Teaching and Learning Project which is deploying the framework to study learning in a variety of traditional and technology enhanced environments across a wide range of subject areas (Entwistle, Hounsell et al, 2001).

Related work on individual differences, also drawing from Pask and Scott’s studies, is that of Nigel Ford and associates who have for a number of years been carrying out in depth studies of students’ strategies for retrieving information from electronic archives. The research has revealed individual differences analogous to those of holist and serialist. On-line cognitive style assessment instruments have been developed. Ford (2001) provides a recent description of the research programme and a summary of its findings. For other work on individual differences, much of which draws for Pask and Scott, see Schmeck (1988). This volume includes a summary chapter from Pask himself (Pask, 1988).

In the UK especially, the learning technology community has adopted Laurillard’s (op. cit.) elaboration of the conversation theory model of learner and teacher interaction as the dominant theoretical paradigm (see, for example, Britain and Liber, 1999).

Pask was a visiting Professor at the UK’s Open University and was a major contributor to a large-scale project applying conversation theory concepts and procedures to the design of distance learning courses (Lewis, Pask et al, 1976). As a direct outcome of this work, the Science Faculty have been applying conversation theory type methodologies for course design for a considerable number of years. In particular, procedures for constructing “relational glossaries” have been developed. A relational glossary is a glossary that exhibits the cyclicity or “closure” (the OU’s preferred term) of a conversation theory entailment mesh. This is a pedagogically powerful way of ensuring that a body of learning materials for a particular knowledge domain does indeed work together as a coherent whole and for ensuring there is a close mapping between learning outcomes, knowledge content and assessment strategies. See Zimmer (2001) for an account of this work. Evaluation studies show that both faculty and students regard the relational glossary as being a powerful learning aid.

In the larger domain of the internet and hypertext knowledge archives, work inspired by conversation theory is being carried out on self-organising “learning webs” where “Learning algorithms … adapt the link strengths, based on the frequency with which links are selected by hypertext users.. to make the World-Wide Web more intelligent, allowing it to self-organize and support inferences” (Heylighen, 2001) and on “recommendation systems” for “An extended process of information retrieval in distributed information systems” where “The knowledge stored in distributed information resources adapts to the evolving semantic expectations of their users as these select the information they desire in conversation with the information resources” (Rocha, 2001). A recommendation system is a generalisation of the Thoughtsticker course design tool. The system models the behaviour of the user of a set of distributed information resources, makes inferences about the predications she is using to give meaning to the information resources and makes recommendations to

the user based on those inferences. The user then may or may not validate those inferences by her acceptance or not of the recommendations. Thus a “hermeneutic circle” is set up, where user and system may converge towards a mutually shared set of predications.

Pask has had a considerable influence in the field of architecture where conversation theory provides a conceptual framework for informing the design of interactive buildings and artefacts, as in museums and other learning spaces. See for example Bradburne (2001), Frazer (2001) and Silver, Dodd et al (2001).

Alongside the longstanding generic influence of Pask’s work on adaptive teaching systems that permeates the world of computer aided learning, there are other developments that draw inspiration from conversation theory and CASTE. Arshad and Kelleher (1993) describe an on-line CASTE type student advisory system. Patel and Kinshuk’s Byzantium system for teaching the concepts and skills and of accounting resembles CASTE in its clear distinction between an interactive microworld in which procedural skills may be practised and mastered with various degrees of intelligent tutoring and adaptive support and the domain of conceptual knowledge which is operationally grounded in the microworld. See Patel, Scott and Kinshuk (2001) for a description of how Byzantium functions and protocols map onto conversation theory. As yet, there are no contemporary web-based systems that match the power and sophistication of the later versions of CASTE and Thoughtsticker (implemented at Concordia University in the 1980s under the direction of David Mitchell, using four Apple II computers working in parallel; see Pangaro, 2001) but moves are being made in that direction. Freeman and Ryan’s (1997) Webmapper system is a Thoughtsticker type tool to aid course design for web-based learning environments. Patel and colleagues (op. cit.) are developing web-based intelligent tutoring tools along CASTE principals. Examples of other work on web-based adaptive tutoring systems, though not directly drawing on conversation theory, can be found in Brusilovsky, Stock et al (eds.) (2000).

Conversation theory is explicitly a cybernetic theory and it is heartening to see other workers in educational technology drawing on cybernetics for inspiration (Liber and Britain, 2001; Boyd, 2001). As I have argued elsewhere, cybernetics is very much about being holistic and transdisciplinary, about abstracting general principles and articulating similarities and differences in order to bring order and unity (Scott, in press b). The power of conversation theory lies in its subject domain and technology independent generality and its recursive applicability to different levels within social systems. By making clear the constructivist, dialogical form of human communication, it can help us characterise what is effective learning and pathology free communication within social institutions. (See Glanville, 2001, Zueew, 2001, and Scott, in press c, for essays in this direction). I believe conversation theory has a continuing role to play in educational technology, not just as a source of useful models and methodologies but also as an inspiring and unifying metatheoretical framework for all of us in the field who aspire to be reflective practitioners and members of true learning communities.

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