Learner

Introduction

This section focusses on the individual learner perspective that informs my practice. The learner in my practice has been central to improving the design of materials and courses, but not without understanding the social context. Dewey convincingly argues the importance of this in his declaration of pedagogical creed:

In sum, I believe that the individual who is to be educated is a social individual and that society is an organic union of individuals.

If we eliminate the social factor from the child we are left only with an abstraction; if we eliminate the individual factor from society, we are left only with an inert and lifeless mass.

Education, therefore, must begin with a psychological insight into the child's capacities, interests, and habits.

Dewey (1897, 77-80)

Making sense of the individual learner - their capacities, interests and habits - has helped me to develop a model of learning, Expressive Constructivism, which operates on the foundation of the concepts and theories expressed in the following sections.

Learners' Knowledge

The capacities to know, decide and act are represented by learners' knowledge. Knowledge is a term that is naturally confused in meaning, between the kind of knowledge which individuals have in their mind in order to think, make decisions and perform, and that which is shared in speaking, writing and other media and used by society to coordinate meaning and action. I prefer to consider the first, mind, as primary, and intimately connected with action, as proposed by Piaget in the term schemata - systems of knowledge in the mind arising from the interplay of experience and activity (Piaget 1953). The secondary meaning, that of externalised or articulated knowledge, is not normally functionally independent of human interpretation, but may be viewed as essentially information. Piaget's argument for biological stages in the development of learner's knowledge, linked to age, is developed by Vygotsky's more fluid perspective (Wertsch 1987), where learner's capacity may be greater when supported by another, named the zone of proximal development.

In both cases, learner's knowledge is argued to be modified by on-going experiences. The Expressive Constructivism analysis presented in this dissertation simply proposes that the experience that develops learners' knowledge is more specifically the expression and evaluation of learner's own knowledge by learners themselves, with varying levels of support.

This relates closely to Laurillard's concepts of intrinsic and extrinsic feedback  (Laurillard 2012, 55). Intrinsic feedback is that which is experienced through the consequences of our actions (close to the internal evaluation in my analysis [A1]). Extrinsic feedback that which is made from others' communications and observations (close to the natural evaluation in my analysis [A1]). Laurillard goes on to identify the importance of the learner's production of some representation of what they have learned (close to the natural and formal expression in my analysis [A1]) and continues with:

The nature of "learning through production" has not been thoroughly researched, despite the importance in formal education of insisting that learners produce something to show what they have learnt.

(Laurillard 2012, 57)

Laurillard does point out some attention has been paid to this in the context of collaborative learning, but her point reassures me that the analysis I have made [A1] is a useful contribution. Laurillard's Conversational Framework model of learning (Laurillard 2012, 92) is very close to the analysis I have proposed [A1], sharing many features, but its intention is to model the role of teachers, learners and peers, whereas I have focussed on the individual learner and greater simplicity to ease the designer's task.

The nature of Learners' knowledge is considered below by dividing it into facts, skills, mental models, strategies and attitudes.

Facts

In my view 'facts' are the simplest form of knowledge that enable the learner to respond to simple questions of definition. In logical terms, they represent connections between two or more atomic concepts, for example 7 times 8 is 56 connects 7, 8 and 56. Such facts are interconnected with others, such as 56 divided by 8 is 7 and thus can become metal models. They are important in that they empower higher order knowledge, but becoming less vital as we are increasingly supported by technology in the form of calculators, online dictionaries and searchable information. Performance is shown by recall or recognition of sounds, acts, definitions or simple relationships.

Skills

Skills are the standard, well-established procedures to be carried out by the learner when applicable situations are recognised. Performance is demonstrated by carrying out the procedure in front of others or by recording steps in the process.

Mental Models

Mental models are complex and dynamic relationships which can be employed to explain and predict more complex issues and may be based on networks of facts and skills. As such they are the most important form of knowledge to be improved through the expression and evaluation argued for in the Expressive Constructivism model of learning [A1] and thus are the subject of this discussion here.

My design practice has developed with the fundamental assumption that mental models (Craik 1943; Johnson-Laird 1983) are the basis of an individual's knowledge. Facts and skills could be argued to be the simplest mental models, but I prefer to identify them separately and as building blocks.

My belief in the importance of mental models to educational design is based on Donald Norman's view:

In interacting with the environment, with others and with the artefacts of technology, people form internal mental models of themselves and of the things with which they are interacting. These models provide predictive and explanatory power for understanding the interaction.

(Norman 1983a, 7)

I contend that mental models enable explanation, prediction and thus decision-making and action in a much wider sphere than Norman's focus on the interaction with technology. Nevertheless, it is in the practice of developing better user-interfaces in educational software that my journey as a practitioner started.  I found that by extending the concept of mental model to embrace a wide variety of modalities (sensory modes such as sound, vision, touch) and genre (expressive modes, such as narrative, diagram, play or poem), it could provide a basis for understanding learners' knowledge in all its guises.

I accept the constructivist view, that knowledge is created in the mind of the learner by their own mental activity in response to experience and information (Kolb 1984). In my view, at the heart of this is the establishment and improvement of mental models.

Mental models are not only faulty (as they continue to develop through refinement), but also unconscious in the sense that they may be unknown and even their nature unknowable to the person employing them. Nevertheless they may provide effective capability and thus form the basis of tacit knowledge (Polyani 1966).

Observing mental models

I do not believe that it is fruitful, especially for the design practitioner, to spend too long identifying mental models' structural properties nor attempting to use mental models as a basis for formal prediction or explanation. In my view, the biological representation and processing of mental models, in both the network and dynamics of the neural connections in the brain or the phenomenology of the mind, is simply too complex, diverse and subtle. To add further futility (or utility if this is seen instead as a teaching strategy), the act of discovering mental models, through dialogue with learners, can change the mental model itself (Rogers et al. 1992).

Further research in this area may be ultimately successful, but is a diversion in terms of my design practice. Clarity about the neural structure of the brain may indicate useful design issues, but often on a different level than that of thinking and learning. I suggest we can only objectively deduce the strengths and limitations of mental models by observing and analysing  human behaviours, verbal utterances and written or graphic articulations - expressions. This inability to more directly observe mental models does not lead me to reject mentalism, the study of mental perception and thought processes, as Skinner might (Hill 1984, 63-87).

Introspection and self-report

Instead, in my design practice I have favoured a more subjective lens for examining mental models through introspection (Kind 2005), the self examination of thoughts and imagination which can support our understanding. This kind of self-report is, I believe, no more or less useful than any other evidence we gain from human behaviour, and clearly needs to be handled with care. Nevertheless, table 5 lists some examples of mental models and distinguishes between mental models (learner's knowledge) and externalised conceptual models (information).

Table 5: Examples of Mental models

These examples are important to me in my practice because they differentiate between the idea of mental model and that of externalised conceptual model. The latter is a shared articulation of knowledge, often in oral, written or diagrammatic form (including a map) which tries to capture the essence of mental models so as to communicate knowledge.

Problem solving strategies

This form of knowledge is the basis of analysis and creativity and may involve the application of mental models. I argue that the key capabilities are those of recognition, open-mindedness, backtracking and re-formulation. My own work in this area resulted in a published paper to identify the steps that the learner would need to undertake to formulate computer models (Millwood and Stevens 1990), based on the experience gained in formulating the Modus project to design modelling software:

• identifying a purpose;
• having concern for presentation and communication;
• constructing an interactive simulation;
• picturing the end-product;
• identifying elements;
• characterising elements;
• identifying relationships and
• characterising relationships.

Attitudes to learning

Learning attitudes, or dispositions, are often the 'soft' and unrecognised aspects of knowledge in the learner, and not tested directly through summative assessment. I would argue that attitudes to be developed include determination, motivation, love of subject and a concern for quality and detail. Successful learners may also be patient, optimistic and persevering (Seligman 1998). A substantial development in my thinking in relation to attitudes to learning, inspired by collaboration with Stephen Heppell, was the concept of delight. The challenge to script and present a Teacher's TV programme, Happiest Days? (Millwood 2006), was the starting point for my own delight framework An Analysis of Delight (Millwood 2008a) based on John Heron's work (Heron 1992), which I designed to explain and justify design choices in technology-enhanced education. This framework is outlined in the Methodology section on Values driving the research.

Summary

This view of the learner and the range of types of their knowledge helps me as a designer to identify how technology enhanced educational innovations can support learning and offer a critical framework within which improvement to designs can be made based on a static understanding of what is to be learned  - the curriculum. But the dynamic processes of learning, as described by learning theorists in the next section - The Challenge of Learning Theory, provide such a rich and diverse picture, that to be pragmatic in my design practice I began to seek simpler models that provided sufficient detail to inspire and justify design decisions, hence the genesis of Expressive Constructivism [A1] as set out in the Claim section.