Learning support environments: Rationale and evaluation

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<ul><li><p>Compurers Educ. Vol. 15. No l-3. pp 137-113. 1990 </p><p>Pnnred in Great Bntain All rights reserved 0360-1315 90 53.00 + 0.00 </p><p>Copyright (; 1990 Pergamon Press plc </p><p>LEARNING SUPPORT ENVIRONMENTS: RATIONALE AND EVALUATION </p><p>LESLEY ALLINSON and NICK HAMMOND Department of Psychology, University of York. York YOI SDD, England </p><p>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. </p><p>INTRODUCTION </p><p>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. </p><p>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 scientists science to the childrens 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. </p><p>TYPES OF KNOWLEDGE AND LEARNING ACTIVITY </p><p>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 </p><p>137 </p></li><li><p>I38 LESLEV ALLISON and NICK HAMMOSD </p><p>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. </p><p>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. </p><p>LEARNING STRATEGIES AND STYLES </p><p>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 ones 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. </p><p>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. </p><p>Many authors have argued that not only should a CAL system support appropriate Iearning strategies, but that it should also be compatible with the students 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 </p></li><li><p>Learning support environments 139 </p><p>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. </p><p>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. </p><p>STYLES OF CAL </p><p>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. </p><p>Programmed learning </p><p>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. </p><p>Intelligent tutoring systems </p><p>Intelligent tutoring systems maintain a representation of aspects of the learners state of knowledge, and, through some computation on the difference between the current and the required states, influence the nature of the learners 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. </p></li><li><p>130 LESLEY ALLINKJS and NICK HAMMOND </p><p>Learner support enrironments </p><p>The intelligent tutoring approach at least has the advantage that the interaction is based on explicit models of the learners and experts 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. </p><p>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...</p></li></ul>

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