Granular interface design decomposing learning tasks and enhancing
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Granular interface design: decomposing learning tasks and enhancing tutoring interactionA. Patel a and Kinshuk ba CAL Research & Software Engineering Centre, Bosworth House, De Montfort University, Leicester LE1 9BH England Email: apatel@ Phone/Fax: +44 116 257 7193b GMD FIT, Schloss Birlinghoven, D-53754 Sankt Augustin, Germany Email: kinshuk@gmd.de Phone: +49 2241 14 2144 Fax: +49 2241 14 2065Advances in Human Factors/Ergonomics - 21B - Design of Computing Systems: Social and Ergonomic Considerations (Eds. M. J. Smith, G. Salvendy & R. J. Koubek), Elsevier Science B. V., Amsterdam,pp161-164 (ISSN 0921-2647, ISBN 0 444 82183 X)The concept of granularity has been applied in Intelligent Tutoring Systems (ITS) for the purpose of diagnosis, plan recognition, reasoning and belief revision. These implementations have been related with the knowledge management within the system. This paper considers granularity from an interface design viewpoint. The learning tasks are decomposed into smaller components at varying levels of granularity with the perspective shift enabled through the user interface. They remove the need for the system to engage in complex inferencing about the user knowledge as the system can provide a status feedback (e.g. correct/incorrect) at a coarser grain size and require the student to use a fine grained interface for more detailed interaction. The students also find it easier to focus on and grasp these smaller components.1. INTRODUCTIONThe process of education involves traversing the granularity of various disciplines to varying extents, from detailed to abstract and from intrinsically simple to complex representations of knowledge - the complexity arising from implicit knowledge, implied context and inferred semantic. The students also traverse the aggregation granularity in the process of an educational model progression along the part-whole dimension. At an introductory level, the students generally learn the details to be able to apply them at an advanced level. As they progress in a discipline, they learn to combine the parts into whole, within appropriate environmental constraints such as behavioural factors. The very nature of the educational process therefore favours a granular approach towards the design of tutoring systems where students can easily move between different grain sizes. As Hobbs (1985) observed, " We look at the world under various grain sizes and abstract from it only those things that serve our present interests".2. BACKGROUNDThis paper describes a Computer Integrated Learning Environments (CILE) approach employing Intelligent Tutoring Tools (ITTs) for the teaching and learningof numeric disciplines at an introductory level. It discusses the adoption of granularity in interface design to decompose the tutoring tasks with a view to reduce both the complexity of the system-student interaction and the cognitive load on a student at an introductory level. The ITTs are mixed-initiative systems with an overlay type of student model. The structural details of the ITTs are discussed in Patel & Kinshuk (1996a). They may be mixed and matched with other technologies (e.g. video) as well as human teachers, in various configurations to suit classroom based, open and distance learning (Kinshuk & Patel, 1996).3. GRANULARITY WITHIN AN ITTThe ITT provides fine-grained tutoring through a short and simple feedback regime. Instead of attempting to infer complex steps and build up complicated feedback messages, the ITT advises a student to move to an appropriate level of detail - either to an intermediate step within the current interface or through calling up a fine-grained interface (Patel & Kinshuk, 1996a, 1996b). The learning tasks within an ITT are decomposed into smaller components.This is illustrated with reference to the Capital Investment Appraisal ITT. An investment project may be viewed from various perspectives. Each of this perspective is an abstraction of a project’s details to enable comparison between different projects or evaluation against some policy norm.Figure 1‘Payback’ (figure 1) is an abstraction, along the dimension of time but implicitly along the dimension of uncertainty and risk as longer duration means greater uncertainty. Similarly ‘ARR’ or the Accounting Rate of Return (figure 2) is an abstraction in terms of an average percentage return on the average investment.Figure 2The ‘NPV’ (figure 3) or the Net Present Value is an abstract representation of a project’s surplus cash flow in absolute terms but discounted on the basis of time and a rate representing interest, cost of capital or opportunity cost.Figure 3The ITT represents these perspectives on different screens, keeping the project details on the same area of the screen, enabling a student to appreciate that the different techniques evaluate a given project from different perspectives. A student can move between these perspectives through the buttons on the control panel. Within these screens, each node represents an instance of a basic or a derived concept and the nodes are connected to each other through mutual relationships based on the underlying concepts. Dependencies are created by the sequence in which the nodes are instanciated, so that given a relationship between three nodes, if two nodes are already instanciated, a legal instance of the third node must satisfy the relationship. Such granular representation of the concepts allows the system ease in inferring missing conceptions and misconceptions in a student’s knowledge boundaries, andenables learning of the concepts at various level of understanding (Lelouche & Morin, 1996).On the ‘NPV’ screen, a student can choose to apply the discount factor as an abstract notion and let the system provide the values or choose to calculate it. If a student chooses to calculate it and enters an incorrect value, the system informs that it is incorrect and advises the student to use the fine-grained interface shown in figure 4.Figure 4The interface can be called up using the ‘Formula’ button on the control panel (not shown in Figure 3). If the student still has difficulty in understanding the concept of discount factor, the system provides a more descriptive explanation with an example as shown in Figure 5, accessible through the 'Explain' button.Figure 5The fine-grained interface is also laid-out along the dimension of aggregation granularity, to show that the discount factor is a reciprocal of the compound factor -a function of time duration and the rate per time duration.The tutoring on the finer details is necessary until the students internalise these concepts and are able to process them more efficiently in their minds. Beyond this point, it causes boredom if they are forced to use the fine-grained interface. An ITT, therefore, does not force the use of the fine-grained interface as a routine. It provides a status feedback (e.g. correct/incorrect) at a coarser grain size and advises the student to use the fine-grained interface for more detailed interaction. Similarly, the ITT does not force a rigid path to the solution and does not require that all the intermediate steps on the interface be complete, enabling a student to manipulate the finer details mentally and enter the aggregate outcome. It accommodates a serial approach of systematically working through the details as well as a holistic approach of rapidly assimilating details and focusing on the goal.4. GRANULARITY IN THE DOMAIN KNOWLEDGEThe domain knowledge can be viewed as consisting of three levels. At the introductory application level, a student learns how to use the basic tools of a subject discipline and the Basic ITT is designed to suit this level. At the advanced application level two types of integration are possible. A Ranking ITT provides a suitable interface for holding and comparing the results of multiple instances of an ITT while the Linking ITT combines other ITTs to extend the size and complexity of the problems covered. At the actual application approximation level, the students learn how to account for behavioural and environmental factors seen in the real world. This viewpoint enables creation of larger tutoring systems where a student can drop to a Basic ITT for more detailed interaction but otherwise operate at a relatively coarser grain size.5. CONCLUSIONMcCalla and Greer (1991) noted that students seem to reason at many grain sizes and appear to have both deep and shallow knowledge at the same time, especially in case of problem solving abilities. They observed that the relationship of partial knowledge to more complete knowledge is also a granularity relationship and as students refine their understanding, they are, in Hobbs’ (1985) terms, articulating their knowledge to finer grain size, apparently along at least three dimensions: aggregation, abstraction and goals. The students could also move in the opposite direction - from fine grained knowledge of particular situations to an understanding of inclusive, generic, coarse-grained knowledge.The granular interface design for tutoring systems, as described above, is therefore in harmony with a student’s state of knowledge and the ongoing process of knowledge acquisition at any given time.It enables easy shift in perspective and facilitates both bottom-up and top-down approaches to learning. While it provides all the necessary details without cluttering the screen, it also prevents fragmentation of concepts by adopting a granular interface design - a well-balanced interplay of the parts and the whole.REFERENCESHobbs J. (1985). Granularity. Proceedings of the 9th International Conference on Artificial Intelligence, pp432-435.Kinshuk & Patel A. (1996). Intelligent Tutoring Tools - Redesigning ITSs for adequate knowledge transfer emphasis. Proceedings of 1996 International Conference on Intelligent and Cognitive Systems (Ed. C. Lucas), pp221-226. Lelouche & Morin (1996).The Formula : A Relation? Yes, but a Concept too!! Lecture Notes in Computer Science, 1108, pp176-185.McCalla G.I. & Greer J.E. (1991) Granularity-Based Reasoning and Belief Revision in Student Models. Student Modelling:The Key to Individualized Knowledge-Based Instruction (Eds. Greer J.E & McCalla G.I.), Springer-Verlag, pp 39-62.Patel A. & Kinshuk (1996a). Applied Artificial Intelligence for Teaching Numeric Topics in Engineering Disciplines. Lecture Notes in Computer Science, 1108, pp132-140.Patel A. & Kinshuk (1996b). Knowledge Characteristics: Reconsidering the Design of Intelligent Tutoring Systems. Knowledge Transfer (Ed. A. Behrooz), pp190-197.。
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