CHAPTER S
Multiple Portrayal Networkl
5. 1 Introduction
Cognitive trait model (CTM) is a student model that profiles the learner's cognitive traits. Working memory capacity, inductive reasoning ability and divergent associative learning are cognitive traits investigated in this study. By using the profiles created by CTM, adaptive computer-supported learning environments can adapt the content to suit individual learner's needs based on his/her cognitive traits (Lin, Kinshuk & Patel, 2003).
With the aim to accurately profile learners' cognitive traits, extensive amount of literature studies on cognitive traits were carried out. Taking working memory as an example, cognitive scientists developed different theories and perspectives for working memory capacity: Baddeley (1 992) decomposed working memory into its components and studied it structurally; Salthouse and Babcock (1 992) and Daneman and Carpenter (1980) viewed working memory as a process; and Atkinson and Shiffi'in ( 1 968) defined working memory functionally as the gateway allowing information to be transferred to the long-term memory. Some researchers developed models about working memory capacity whereas others studied it experimentally. An empirical study by Huai (2000) showed that students with holistic learning style (prefer the big picture view) have significantly smaller working memory than students with serial learning style (highly capable to follow and remember sequentially fixed information). All these different theories/experiment-results provide different views to the same entity: working memory capacity. The different views on working memory capacity are analogous to different portrayals of the same physical object. When viewing a physical object from different angles, it results in different portrayals (images).
In discussing different intelligence theories, Sternberg ( 1 990) contended that different "metaphors of mind" guide different theories about human intelligence. In Sternberg (1 990), geographic, computational, biological, epistemological, anthropological, sociological, and systems metaphors are listed. Different metaphors provide different views to intelligence and lead to different research programmes, different questions, and different answers. Baer (1 993, p95) asserted that "the theories deriving from different metaphors provide different glimpses of the 'true' nature of intelligence". Sternberg (1 990) therefore argued that theories based on different metaphors can
I The discussion of this chapter include concepts called manifestation of traits (MOTs) which are fully
discussed in chapters 6, 7, and 8. For some readers, it might be benefit if they read chapter 6 before commence reading this chapter. Simulation and validation of the work introduced in this chapter is presented in chapter 9. Interested readers can go to chapter 9 to find out more of it.
Multiple Portrayal Network
provide one another important insights, and theories arising from diverse approaches might be profitably combined to build more inclusive theories.
Although, differences, even we are told to value them in a democracy, become problem statements for those who try to understand the cognitive traits. In this study, we propose a mechanism, called multiple portrayal network (MPN) that is capable of representing an entity that the relationships of its portrayals are difficult to know. MPN is also able to evolve itself based on the behaviours of individual learners in order to provide more accurate profile of the learner.
In order to present the works about MPN, the overall structure of CTM is briefly revisited in next section to serve as the background information regarding where MPN fits into current study. Detailed description of MPN is then presented. Theoretically, MPN is also one type of machine learning; its relation to other machine learning techniques, especially to artificial neural network, is also briefly mentioned. Finally, we conclude this chapter by a discussion of the benefits and limitations of MPN.
5. 2 Cognitive Trait Model and MPN
Figure 5-1 shows the overall structure of cognitive trait model (CTM). The Individualised Trait Networks Component in Figure 5-1 contains one to many Individualised Trait Networks (lTNs). Each ITN represents one cognitive trait. After the MOT Detector Component finishes detection of MaTs, it sends the result to the Individualised Trait Networks Component.
Trait Model
Trait Model Gateway
Action Individualised Trait Networks
@
@
Component@
ITN 1 TTN 2 ITN n
Learner Interface
MOT Detector Component
�
. .. .. .�
Interface Listener Component
Figure 5-1 : Structure Overview of Cognitive Trait Model
Each node in an ITN corresponds to one MOT (e.g. non-linear navigational pattern) of the cognitive trait that the ITN represents (e.g. working memory). The major
Multiple Portrayal Network
function of any ITN is to calculate the value that represents the corresponding cognitive trait.
Once a particular MOT is detected from the learner's actions, the corresponding node is activated. The result of the execution of an ITN determines how the nodes in the ITN should be updated. The results of the execution of the ITNs are then sent to the Trait Model Gateway, which is responsible for all the transactions to the Trait Model.
Each ITN in the Individualised Trait Networks Component is an instance of a type of network we called multiple portrayal network (MPN). ITN is the name of MPN used in cognitive trait model whereas MPN is the general name of this type of network that is not bounded to a single application. Undeniably, MPN is resulted from the investigation of cognitive trait model, but the intention is to generalise it to make it usable in other applications as well. Cognitive trait model can then be taken as one of its example application - the term MPN is used hereafter in this chapter.
5. 3 Multiple Portrayal Network
Multiple portrayal network (MPN) is a network representation of an entity of which the constituents are nodes N. N =
{no
.. . nn } Each nodeni
is a partial portrayal of theentity. The overall value of the entity, 0, is determined by its constituent nodes. Each node ni contains a numerical value called weight Wi which determines the node's influence over the overall value 0 of the entity. Figure 5-2 depicts an example of MPN consisting of three nodes.
Figure 5-2 : Example of a 3-node MPN
As shown in Figure 5-3, each node consists of a pair of attributes: I (stands for low) and h (stands for high). The pair shares the weight w of the node - if one attribute's value increases, then the other decreases. Therefore, the values of the two attributes are metaphorically similar to the two ends of a scale (see Figure 5-2). Values of attributes are therefore best represented by percentage, for example when I is 40%, h should be 60%. Each of the two attributes in a node represents one of the dichotomic properties of the node, i.e. they are opposite to each other. For example, if one attribute represents linear navigation, the other should represent non-linear navigation.