Compurers Educ. Vol. 15. No l-3. pp 137-113. 1990

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LEARNING SUPPORT ENVIRONMENTS: RATIONALE AND EVALUATION

LESLEY ALLINSON and NICK HAMMOND

Department of Psychology, University of York. York YOI SDD, England

Abstract-In this paper we argue that existing understanding of human cognition has much to offer the design of instructional systems and materials, and that new technologies, such as hypertext. in harness with traditional techniques. provide opportunities for extending the mapping of cognitive principles to instructional design. Following a review of types of know!edge, cognitive styles and strategies within a CAL framework. the advantages of learning support environments over other CAL approaches are discussed. Such environments are presented as extensions to hypertext which incorporate various generic features for learning applications. The need for evaluation of learning which is dynamic and internal to the learning tasks is stressed.

INTRODUCTION

Ausubel has aptly summarised the objectives of education as “. . . the long-term acquisition of valid and usable bodies of knowledge and intellectual skills and the development of an ability to think critically, systematically and independently”[l]. Where and how in the achievement of such laudable aims does CAL play its part? It is commonly assumed that CAL has a part to play, and that being so we must exploit the latest advances in computer hardware and software. This paper argues that CAL has become technology-driven, and needs to be constructed on the firmer foundations of our understanding of human information acquisition and processing strategies. The importance of evaluation will be stressed as an integral dynamic element of CAL systems.

Cognitive science has taught us, if it has taught us anything, that knowledge is composed of complicated interacting networks of information and skills. Only if we believe that knowledge is composed essentially of isolated facts to be committed to memory can support be given to Skinnerian learning and the use of passive rote-learning methods. The structure of knowledge is more complex-not only are there isolated facts but there are hierarchies, relational networks and combinational sets. Furthermore, knowledge can be viewed from a number of perspectives. For example, Shuell[2] discusses aspects of the nature of knowledge, including its locus and type. Locus refers to whether knowledge exists in an independent objective form or whether it exists primarily in the mental representations of like-minded individuals. For many disciplines there is controversy, conflicting explanations of the same experimental evidence (even conflicting evidence), historical perspectives, subjective opinions-as well as hard isolated facts. Even in the physical sciences, as Gilbert et a1.[3] have noted, there are different locations and hence representations of scientific knowledge-ranging from the scientist’s science to the children’s science. An explanation of a physical phenomenon in terms of Newtonian mechanics may be sufficient for one locus of knowledge, but for another only General Relativity Theory or even String Theory is adequate. CAL systems for learning will therefore need to support a variety of perspectives on a given knowledge domain. In the next sections we shall discuss aspects of knowledge representation and use in the context of CAL systems.

TYPES OF KNOWLEDGE AND LEARNING ACTIVITY

As well as supporting varied perspectives, general CAL systems need to handle a variety of learning activities-some active, some passive; some creative, some reactive; some directed, some exploratory. To limit the learner merely, say, to browsing an information base, or to a directed step-by-step tutorial, hardly matches the richness of everyday learning. Different learning activities optimally support the learning of different types of knowledge, and in turn a complex mosaic of knowledge types will be required to represent a specific domain. Understanding the mappings

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I38 LESLEV ALLISON and NICK HAMMOSD

between domain, knowledge types and learning activity requires thorough cognitive and epistemo- logical analysis, and we can only point to a few key distinctions here.

One distinction commonly made is between declarative (explicit or articulable) knowledge and procedural (implicit or action-based) knowledge (for example, Gagni[4]). In many domains, computers support forms of learning-by-doing not possible by other means, such as through direct interaction with simulations or by the use of graphical animation. These learning activities may aid in the acquisition of procedural representations, allowing the learner to bypass, or perhaps gain insight into, less direct declarative representations. Other learning activities, such as reading, creative writing. problem-soIving or self-assessment may all play their part in helping the learner to acquire a variety of forms of usabie knowledge, and all should be seen as potentiat activities within a CAL environment. What is important in the design of a CAL system is that acquisition of these differing knowledge forms and structures is encouraged. CAL material and activities must result in the formation of a coherent body of knowledge and at a level consistent with that required.

LEARNING STRATEGIES AND STYLES

Students come to the classroom or terminal room endowed with existing knowledge. Meaningful learning can only take place if the current learning task can be related, by the student, to his existing knowledge and meta-knowledge base. Meta-knowledge refers to knowledge of one’s own knowl- edge, of the techniques and strategies used for monitoring performance and for controlling the acquisition processes. It is perhaps a truism that a CAL system shouid promote optimal learning strategies and support styles of learning to which the learner can at least adapt, even if the style is not compatible with that preferred by the learner.

Whether or not students themselves should be given control over the sequencing and nature of learning activities-in short over the learning strategy-has been the topic of much research and debate. As Merritl[5] points out, there are two basic types of learner control: control of content (the learning material) and control of strategy (facilities for access, depth of presentation, practice questions). As Laurillard[6] comments “. . . There is no well established reason to suppose that a program designer, whether teacher, researcher or programmer, knows better than the student how they should learn. Therefore, when we are designing materials for a medium that is capable of providing an unusual degree of individualisation via student controt, it seems perverse not to take advantage of it.. .“. Research on the usefulness of extending the learner choice of actions has provided conflicting evidence, though it should be noted that many studies have been based on a limited range of learner control options in a specific knowledge domain. Fry[7] suggested that freedom led to inefficient learning, however Hartley[8] demonstrated that learner control could be more effective than program control Rubicam and Oliver[9] considered a number of studies- again with confusing conclusions. However, their findings did suggest that students who adopted a consistent strategy performed significantly better than those who were inconsistent. Another example of linear or selected branching of information screens by Gray [IO] suggests that students who experienced the branching option performed better in comprehension-based tests but no difference in retention-based tests. While one can extract some reasonable rules of thumb, such as that usually knowledgeable learners are in a better position to capitatise on freedom of choice than relative novices, the important point is that, as with many issues in educational technology, the optimal locus and nature of control is strongly dependent on contextual factors. A rigid allocation of control (whether by system or learner) is unlikely to be suitable across a range of domains, learner types and learning tasks.

Many authors have argued that not only should a CAL system support appropriate Iearning strategies, but that it should also be compatible with the student’s styfe of learning. Learners will bring with them wideIy differing cognitive styles which affect the guidance that should be given. A variety of cognitive styles has been suggested; for instance Messick et aZ.[l l] define 19 different dimensions. Though the independent nature of such a variety of cognitive styles has been criticised in that they may simply be differing aspects of general cognitive ability, they remain useful in exposing the different learning styles which can be adopted, styles which the design of CAL systems may need to take into account. It is beyond the scope of this paper to review the myriad of cognitive

Learning support environments 139

styles put forward or to rehearse the arguments for and against their use within CAL. While there is evidence that some people may consistently demonstrate one style of learning as opposed to another, many individuals will change their cognitive style to suit the current task[l2]. Entwistle[l3] is critical of much of this work stating that researchers have been “determined to pursue their own pet distinctions in cheerful disregard of one another”. What is perhaps important in the context of CAL is not whether these distinctions represent true differences in cognitive style but that they are observable and, in some situations, may contribute significantly to learner behaviour.

CAL systems that, by providing a restricted form of presentation, confine the student to a particular learning strategy. or perhaps to a particular learning style, are likely to fail a substantial proportion. Linear presentation will frustrate the student who, whether through inclination or current state of knowledge, wishes to learn by first gaining an overview, whereas a totally user-centred environment may overwhelm a student with the need for a more serial approach to learning. However, irrespective of cognitive style, the building of a knowledge base by assimilation of new material should always be encouraged and the flexibility for users to approach the material from a number of perspectives, and a distinct and visually rich environment will greatly aid meaningful learning.

STYLES OF CAL

The above discussion has highlighted a number of putative features which, in our view, a general-purpose CAL system should possess. These include: the ability to view information from a number of perspectives; the support of a range of learning activities; varied levels of control between learner and system sensitive to learner and task demands; support for a distinctive and rich learning environment. The extent to which existing styles of CAL support these features will now be discussed.

Programmed learning

The traditional drill and practice approach with linear or perhaps optional branching mech- anisms are still well represented within existing CAL packages. The shortcomings have already been mentioned, in that they are prescriptive and inflexible, and favour the acquisition of limited forms of knowledge. While they may have merit in some domains, we will not discuss them further here.

Intelligent tutoring systems

Intelligent tutoring systems maintain a representation of aspects of the learner’s state of knowledge, and, through some computation on the difference between the current and the required states, influence the nature of the learner’s interaction with the materials. Intelligent tutoring systems have been successfully used in limited knowledge domains which are formal in their organisation and dependent on logical analysis-such as mathematics or some branches of the physical sciences. However, many less formal knowledge domains cannot be described in terms of such a logical calculus. We cannot discuss the variety of tutoring systems here, but certainly strictly model-driven systems may force users along a route as nearly as restrictive as the straightjacket of programmed rote learning. Fischler and Firchein[l4] discuss the limitations of expert systems in general. Though production-rule based systems have been one of the most active areas of applied artificial intelligence, their limited sphere of application is now generally accepted. It is useful to repeat some quotes from Megarry[lS] highlighted in a recent paper by Hammond[l6]-“A false trail has been laid by intelligent tutoring systems that try to create a model of the student. . . _ To treat the learner as a dumb patient and the computer system as an omniscient doctor is both perverse and arrogant”. It is, therefore, wise to caution the limited range of applicability of intelligent tutoring systems of this type and their restrictive role for the learner. It has been the principles employed by designers, such as Anderson et al. [17] (Advanced Computer Tutoring Project), that have left us with the most lasting ideas on how future CAL systems might be designed.

130 LESLEY ALLINKJS and NICK HAMMOND

Learner support enrironments

The intelligent tutoring approach at least has the advantage that the interaction is based on explicit models of the learner’s and expert’s knowledge, and, at least in some cases. on an explicit model of the processes of knowledge acquisition. Even if we reject the model-driven intelligent tutoring approach as unsuitable for non-formal domains, we still need to provide an alternative framework for linking the design and use of instruction to the requirements of teacher and learner. Merely providing a large information base for the learner to browse, such as in a hypertext-based electronic encyclopaedia[l8], will be no more likely to guarantee understanding or learning as the range of learning activities and the instructional guidance will be restricted and unmotivated.

In our research programme here at York, we have developed and evaluated the concept of the learning support environment (LSE) in order to meet this problem. The idea of the LSE is based on the rather mundane observation that more is known about providing optimal. or at least adequate, conditions for learning than is known about the detailed processes and representations involved in learning itself. Fortunately, we are able to learn to ride a bicycle or to speak a foreign language without us, or our teachers, having to become experts on the minutiae of knowledge representation. Good educational practice lies in a judicious mixture of pragmatic knowledge about successful practice and scientific knowledge about the underlying cognitive processes. An LSE is therefore intended to provide the learner with a set of tools to use within an appropriate context which, assuming a degree of rationality and meta-knowledge on the part of the student, will allow learning to progress in a flexible yet supportive fashion. If parts of the instruction can be appropriately guided by explicit representational or process models, so much the better. LSEs do not rule out the inclusion of intelligent tutoring techniques.

At York, we have developed an LSE which is not intended to replace conventional lectures or self study, such as literature searching and reading, but is integrated into a traditional educational environment. The students’ existing knowledge base will vary, as will their learning strategies and their goals-initial exploration, revision for examinations or a starting point for a piece of independent work. It is important to take into account the meta-knowledge students possess of their learning needs, capacities and strategies. We assume that, at least in tertiary education, learners can benefit from control over their learning-or at least over where control should reside when-so that they, not the program, can choose their own patterns of behaviour to ensure maximum facilitation of learning. It may, of course, be the case that the learner opts to be guided by the system. The earlier discussion of learner control concluded that a rigid allocation of control is unlikely to provide a generic solution. So how do we proceed? Our argument would be that, while we have not yet fully investigated the usefulness of giving the learner control, a pragmatic approach is to provide a range of activities and of forms of guidance which are tailored for the particular learning requirements, and to make the options and strategies available to learners as clear as possible. Our underlying principles for the design of our LSE are essentially of flexibility and freedom, and we have suggested the following generic system features[l9]:

l distinctive and multiple forms of representation provided by the use of graphical and dynamic presentation;

l rich access structure with many cross links for integration; l ability to juxtapose materials to assist integration; l dynamic simulations, interactive demonstrations and multiple-choice questions to stimulate

active learning; l learner control over what to learn and how to learn so as to best suit an individual student’s

goals and strategies; l multiple navigation methods; l simple user-interface by the use of metaphors to aid quick comprehension by the student

of system facilities.

These features are intended to capitalise on the psychological processes involved in learning, and to provide us with an environment for learning which attempts to meet many of our educational needs and goals. To implement these design principles, an LSE based on a hypertext environment was considered to be most appropriate.

Learning support environments 141

THE USE OF HYPERTEXT FOR LSEs

Hypertext is, in concept, quite simple: windows on a screen are associated with records in a knowledge base and links (usually activated by a mouse) are provided to these records. It permits the user to explore the knowledge base in a non-linear and highly interactive manner. It is these user-activated linkages which form the essential element of hypertext; not just textual items may be linked, but also graphical displays, sounds, video, indeed any media which can be controlled by a computer. For such a plethora of material, the term hypermedia is usually reserved. Hypertext has had a long history from the early seminal work of Bush[20] to Nelson’s Project Xanadu [21]- but it has been the recent appearance of hypertext systems for small common computers that has kindled the fire of the present enthusiasm. OWL International launched Guide in 1986 for Macintosh and in 1987 for IBM-PCs, and Apple released HyperCard late in 1987. It was this that fanned the fire into an inferno-its horizons were unlimited, its use uncritical. To quote from one proponent it will allow “the student’s mind to explore and to follow paths that are not dictated by evident outside sources which permits learning in a whole new fashion. By allowing students to use the material presented in their own way and at their own level, with more difficult or advanced material invisible until asked for, hypertext brings a freedom to the educational process for both teacher and student that was not present before”[22]. The claim is that its non-linear presentation of information should enable students to view “the phenomena of the world in interrelated relativistic terms rather than as isolated bits of information”[23]. It is interesting that the use of Intermedia (an advanced hypermedia system designed to support teaching and research in tertiary education, see[24]), produced more significant learning effects on the people involved in developing materials than on the students using the system. This is a point worth remembering. Hypertext, especially in its realisation on commonly available computers, has given many teachers the ability to produce learning materials by means of extensive authoring tools (unlike the intelligent tutoring approach, which may require the teacher to become an expert in AI). Nevertheless, the success or failure of the material still depends on the author. The possession of a computer paint package does not turn anyone and everyone into an artist! The case for hypertext is that it promotes the acquisition of concepts-not isolated facts-in a manner controlled by the learner. As such it would seem to offer a basis for meeting some of our educational objectives, although we should immediately note that our requirements for providing varied levels of control and for supporting a range of learning activities would almost certainly require extensions to the basic hypertext tools.

PROBLEMS WITH HYPERTEXT

But are there problems amongst the hype? The next section introduces some possible areas and the suggestions which have been made to overcome them. Hammond[l6] provides a summary of some of these problem areas; a slightly different grouping of user difficulties is presented here.

The disorientation problem

Getting lost in space[25] is likely if the learner is on unfamiliar ground in a possibly large knowledge database. The questions asked are “Where am I?” and “How do I get to some other place I know (or I think) exists. 7” Using a conventional text there is a similar problem, but the degrees of freedom are constrained to searching forwards or backwards through the pages. In hypertext the degrees of freedom can be vast-we really are in n-dimensional hyperspace! Two possible solutions have been suggested. The first is to restrict the degrees of freedom-to permit, for example, only a very limited number of links per information frame. Here, we are surely removing the very advantage of hypertext. It should be the inter-relationships between the knowledge nodes with determine the network, not the requirement to present a simple user interface. The second is to provide various facilities for guidance. These have ranged from simple backtracking facilities to visual maps which show relationship between various knowledge nodes. Though we possess a highly developed short-term memory for visuospatial patterns, there is no guaranteed mapping of the knowledge space (perhaps multi-dimensional) onto a two-dimensional

112 LESLEY ALLISON and NICK HAMMOND

screen. Attempts to produce spatial maps of the knowledge base automatically (especially global ones) have produced a tangled web of links of meaningless complexity[26]. Our approach in the Hitch-Hiker’s Guide has been to provide further guidance on local maps by highlighting the current node and areas already visited. Links to other maps are also provided, and any displayed node may be called from the map.

Such tools as these help in answering “Where am I. 7”, but we need others tools to assist in “Where is such-and-such and how do I get there ?” Certainly the facilities found in conventional linear texts can be realised-content pages and indexes. The disorientation problem also means that some users fail to gain an overview of the knowledge base. They are unaware of significant portions or fail to understand how component modules relate to one another. We conducted an experiment on our own hypertext system using a group of students who had to explore a relatively small knowledge base in a fixed time[27]. Those subjects who were provided with only basic hypertext links and no other navigational tools saw a smaller proportion of the materials compared with subjects who were provided with additional navigational tools. Interestingly, these pure hypertext subjects thought they had seen most of the material.

The ejiciency problem

Students may wander about the knowledge base uncertain of their immediate goals, or perhaps they have explicit goals but are uncertain how to achieve them. Hypertext encourages investigation

’ through a large number of links, but to someone who is lost, the presence of a multiplicity of paths is a distinct disadvantage. For the novice user this will certainly be the situation[28]. Once again the number of links per information screen could be limited as a simple means of reducing choice. There is perhaps a maximum limit on the number of items for selection due to the problem of the cognitive-processing overhead. It can be necessary to concentrate on several tasks or trails through the knowledge base at one time. We need at times to encourage serendipity, and at other times to discourage it.

Novice users, or even expert users in an unknown part of the knowledge base, will need extra guidance-to be led rather than to lead. Tours are such a facility. In our own Hitch-Hiker’s Guide, such tours are initiated when the user selects part of the screen which briefly details the contents of the tour-the mouse pointer changes to the shape of small coach (as a constant reminder that the user is being guided). A tour will present a sequence of the main elements of a particular portion of the knowledge base (as prescribed by the author). The user will be returned to the starting point on completion of the tour, or is free to divert from it at any point to follow some side path-and return to the tour on request.

Thus there is the need for a wide range of navigational tools not only to help overcome the disorientation problem but also to alleviate the inefficiency problem.

EVALUATION

We have presented some problems and shortcomings of several approaches to CAL: the intention was not to give the impression that we should throw away our computers and return to chalk and talk, as we believe that LSEr or some derivative, are our best opportunity to unleash the creative power of the computer. Cognitive psychology suggests that the learner is not an empty vessel into which information has to be poured, but that knowledge is stored as a network of concepts or constructs. Learning involves the making of connections between the learner’s existing network of knowledge and new information to be learned. Instruction should facilitate these connections, and the process of education might be defined as “the construction of knowledge by the learner”[29]. Enhanced hypertext systems-or more specifically LSEs-by their essential structure are an analogy of the internal inter-relationships in a knowledge base. There are still many uncertainties and unknowns about the effectiveness of this approach in specific situations. We need to apply the fundamental engineering approach of design-build-test, and iterate around this loop a number of times until our system becomes optimised with respect to criteria based on its effectiveness in increasing or structuring the learner’s knowledge base. There is no need to attempt to correlate educational achievement-however that may be defined-with some, possibly imagined, cognitive

style. The evaluation can be reference to external factors.

Learning support environments

internal to the task of knowledge

CONCLUSIONS

143

acquisition and need not seek

Hypertext-based systems possess enormous potential in the future implementation of CAL systems. The reasons are that they allow the author to externalise his knowledge in a natural way-namely, a network of inter-related concepts. Learners possess different cognitive strategies, and perhaps cognitive styles, but certainly their strategies will change in accordance with their needs and motivations. To assist in these, we need to provide a variety of enquiry and navigational methods. Our proposal of the LSE is that an approach based on general cognitive principles associated with learning can provide a vehicle for active learning. It is not an approach based on the formal rule-based modelling of the user or the knowledge domain. Evaluation, as we have stressed, is essential if we are to appreciate the success or otherwise of our continuing efforts. No doubt, CAL design will remain an eclectic craft drawing from other disciplines. Such an approach is both a strength and a weakness. Perhaps, we can discern a symbiosis between the structures of knowledge representation in the CAL system and the means of measuring the student’s learning. Such a binding without the need for formalism or reference to external psychological factors, presents us with a vast opportunity to exploit in future CAL systems.

I. 2. 3.

4. 5.

6. 7.

8.

9.

IO. II. 12. 13. 14.

15. 16.

17. 18. 19.

20.

21. 22. 23. 24. 25. 26.

27.

28.

29.

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