July 2007 Volume 10 Number 3 - Educational Technology & Society

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grammars as a way to characterize story texts. In the points structure used in the story understanding program PAM (Wilensky, 1982) a story has three levels: the story itself; the important content of the story; and the points. The points are a template for the important content of the story in terms of the goals of the characters. A story grammar defines a story as having a certain form, whereas a story point model defines a story as having certain content. The form of a story is viewed as being a function of the content of the story. The narrative theory of Bennett and Feldman (1981) describes the structure of a story as consisting generally of a setting, concern, resolution sequence. The setting usually includes the time, place and some of the characters. The concern is an action that given the setting creates a climactic (eventful, ironical, suspenseful) situation. For example, if someone is rock-climbing and slips and falls, slipping and falling are the concern. If the story ended at this point, the audience would be left wondering: what happened to the climber? Was he hurt or killed? A complete story will provide an answer to these questions. This stage is the resolution. The central action is the structural element that creates the central question the story must resolve. The resolution normally resolves both the predicament created by the problem and the questions listeners might have had about the outcome. In the rock-climbing story the resolution consisted of telling the audience that the climber was taken to the hospital for treatment. Bennett and Feldman (1981) argue that it is not the weighting of the individual elements of the story, each in terms of the evidence for that element, which renders a case persuasive or not, but rather the plausibility of the story structure taken as a whole. In a good story all elements are connected to a central action and nothing is left standing on its own. The context provides a full and compelling account of why the central action should have developed in the way that it has. If this is not the case then the story contains ambiguities. Wagenaar et al. (1993) proposed the theory of anchored narratives moving on from the work of Bennett and Feldman (1981) where the task of the judge was seen as determining the plausability of the stories presented by the prosecution and the defence. This narrative theory has its basis in cognitive psychology and contends that evidence derives its meaning from a story context. The plausibility of the story is related to its narrative coherence. Various schema-based approaches have been used in the study of story understanding. Story grammars try to capture and define the internal structure of stories as grammars. Many story grammars have been proposed and studied (Mandler & Johnson, 1977; Rumelhart, 1975; Simmons & Correira 1979; Thorndyke, 1977). Frames could be used to represent stories with slots for setting, protagonist, main event, moral or point, characters. However, progression through the story may need modification. The main way in which frames have been used in story modelling is as scripts. A script is a predetermined, stereotyped sequence of actions that defines a well known situation (Schank & Abelson 1977). They do not have the capacity to deal with unfamiliar and novel situations which is important in stories. SAM (Script Applier Mechanism) is a program that `understands’ stories that are heavily based on scripts. Plans (Schank & Abelson 1977) can be used to tackle the problem of dealing with tasks for which there is no script. PAM is a program that understands plan-based stories. The setting, concern, resolution sequence of Bennett and Feldman (1981) is well suited to the IILE in this work. The rich domain model deployed in the IILE is sufficiently expressive to obviate the need to embed structures to represent causal relations between elements found in more complex story grammars. A relatively simple story grammar is sufficient because the domain model is so expressive. The domain model deployed is based on argument structures and is described in the next section. Representation of Domain Reasoning An approach for representing knowledge called the Generic Actual/Argument Model (GAAM) has been advanced by Yearwood and Stranieri (2006). The GAAM model has been applied to the development of numerous decision support systems in law including; Split Up, predicting the percentage split of assets a Family Court judge awards divorcees (Stranieri et al., 1999), Embrace, assessing the strength of claims for refugee status (Yearwood and Stranieri, 1999), GetAid, determining eligibility for legal aid in Victoria (Stranieri et al., 2001) and witness selection in Scotland (Bromby and Hall, 2002). A web-based engine developed to implement the GAAM (justReason http://www.justsys.com.au) automatically generates a web based decision support system accessible with any web browser, using the knowledge. 197
The GAAM represents reasoning to a decision at two levels of abstraction, the generic argument level and the actual argument level. A generic tree that captures reasoning regarding risks associated with roof light design is illustrated in Figure 2. The ‘root’ of the tree is the OHS risk rating associated with a particular roof light element in a building. The linguistic variables “extreme”, “high”, “moderate” and “low” represent acceptable terminology for denoting the magnitude of risk in that field. Every variable in a generic argument tree has a reason depicting its relevance. The factors likelihood that an injury or illness will occur; and the likely severity of the consequence of that injury or illness should it occur are relevant because risk management theory and Australian legislation dictates these two factors are relevant for determining risk. Argument trees are intended to capture a shared understanding of relevant factors in the determination of a value (in this case the level of OHS risk). Irrelevant factors are not included in an argument tree. Thus, the roof light colour is not considered relevant by designers or safety experts, so is not represented as a node in the tree - though one can imagine circumstances where a colour is indeed relevant to OHS, such as in the specification of emergency lighting or signage. An actual argument is an instantiation of variables in the generic tree by setting leaf node values and inferring up the tree. A linguistic variable value on a parent node is inferred from values on children nodes with the use of inference procedures. An inference procedure is essentially a mapping of child variable values to parent variable values. In Figure 2, the inference procedures are denoted by A, B, C, D and R. Thus, for example the inference procedure R could take the form of a commonly used risk matrix where assessments of likelihood and consequence combine to determine the level of risk presented by a hazard. Thus a hazard for which the likelihood of occurrence is rated moderate but the consequence is major would be considered “Extreme.” For example, the risk rating is inferred using an inference procedure, R from values instantiated on two factors: In Australia, the inference derives from a risk matrix formula set by a government based standards organisation. The height and location of a roof light are factors that lead to an inference describing the consequence of a fall (i.e. the severity of the injury). The trolley system and protection for external work are used to infer an overall level of protection, and therefore the likelihood that a fall will occur. The existing protection is also coupled with the frequency with which the roof light will be maintained to infer the likelihood of a fall. In argumentation-based KBSs different inference mechanisms can be used according to the nature of knowledge being modelled. For example, in the ‘Split Up’system (described in Stranieri et al., 1999), neural networks trained on data drawn from divorce property judgements were used to infer about half of the 35 nodes. In a different system, known as ‘Embrace,’ which supported the determination of someone’s refugee status, inferences were always left to the discretion of the decision-maker (Yearwood and Stranieri, 1999). In another system called GetAid, Stranieri et al. (2001) assigned weights to each linguistic variable and then summed these weights before and compared the result with a pre-determined threshold to infer eligibility for legal aid. Argument trees, such as that depicted in Figure 2, represent a template for reasoning in complex situations. Thus, in a discussion about the level of risk posed by a particular roof light, two designers might disagree at the root node level in that one designer perceives the risk to be high while the other perceives it to be moderate. This difference in perception may derive from the different values assigned by each designer to subordinate nodes in the argument tree. For example, when one designer believes that existing protection is certainly adequate, whereas the other does not. The difference may also derive from alternate inference procedures; one uses inference A, the other uses a different mapping mechanism. However, although the two designers disagree, they can both reasonably accept the argument tree structure as a valid template for the expression of their beliefs. The generic argument level is very useful in determining the mapping to the narrative model because it is rich in contextual information as well as providing information on reasons for relevance of premises (which ensures coherence) and reasons for inference procedures which captures values and principles behind the reasoning as well as sequencing information for events when their order is critical to the reasoning. Table 1 illustrates the elements of the GAAM and the corresponding story elements that comprise the structured reasoning to narrative mapping central to the IILE. 198
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July 2007 Volume 10 Number 3
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Advertisements Educational Technolo
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Distinguishing between games and si
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“sometimes” is less demanding t
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Figure 2. From generic skill to lea
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The large window on the left presen
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define competencies of individual a
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competency is composed of a single
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Generic Skills Classes 1 2 3 Receiv
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Figure 6. Extension of the competen
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Competency scale By combining the g
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activities, and resources present i
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Conclusion The importance given to
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Ainsworth, S. (2007). Using a Singl
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different instruction. For example,
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Author-focused studies Studies 1 an
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egression showed that students were
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REDEEM is now a senior citizen in t
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Chieu, V. M. (2007). An Operational
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When students discuss with peers, t
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COFALE as a learning environment an
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COFALE supports a set of predefined
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Tom is asked to prepare a list of g
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45-minute-long lecture and 2-hour-l
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as an easy-to-use technological mea
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Spiro, R. J., & Jehng, J. C. (1990)
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learning settings. However, such a
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introduced in the LOCO-Cite ontolog
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‘virtual subsection’) of the LO
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Accordingly, TANGRAM provides stude
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learning environment is to define t
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Acknowledgment This work is support
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Dron, J. (2007). Designing the Unde
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wikis and blogs (or at least, colle
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The darker side of social software
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for the beetles, whose guts act as
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The principle of sociability The pr
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Bonabeau, E., Dorigo, M., & Theraul
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Klamma, R., Chatti, M. A., Duval, E
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We perceive adaptivity and personal
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Mixed-method evaluation approach tr
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Garden of Knowledge - with a commun
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equirements for collaborative adapt
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Law, E. L.-C., & Hvannberg, E. T. (
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Wang, T. I., Tsai, K. H., Lee, M. C
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2.3 Recommendation Model Survey In
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Figure 1. The portion of the concep
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STEP4: Associated with concepts in
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A modified TF-IDF approach is used
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P(CK j x LOM ) = k NW max ∀ T∈
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4.2.2 Correlation-based algorithm I
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content feedback, it can be intuiti
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66% improvement in average, and, wh
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(a) (b) (c) Figure 10. Comparisons
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index.aspx. ADL-2 (2001). Advanced
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Wolpers, M., Najjar, J., Verbert, K
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activity is taking place. For examp
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Figure 2: The CAM schema elements (
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• Context: the context element ca
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4.1 Generation of Attention Metadat
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addition, such keywords can be extr
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Action related data, data about sea
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Braun, S., & Schmidt, A. (2006). Do
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El-Bishouty, M. M., Ogata, H., & Ya
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collaboration (Ogata, et al., 1999)
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In case of LOI value is equal or cl
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Also, the learner can click on an e
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System Evaluation An experiment was
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In the fifth phase, 89% of the stud
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Fischer, G., & Konomi, S. (2005). I
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application for specific needs, whi
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(4) Multimedia: The truth is that e
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Program Company Price OS 6 eZediaMX
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Program Text editor Text import for
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Program Sound Formats Image Formats
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Page 165 and 166: Educational overview Having taken m
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Page 169 and 170: Developing the Interactivity Survey
Page 171 and 172: technique. be treated as a desired
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Page 177 and 178: Discussion and conclusion Although
Page 179 and 180: Juwah, C. (2003). Using peer assess
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Page 183 and 184: familiarity with computer terms, ex
Page 185 and 186: provide a framework for the integra
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Page 189 and 190: associated with the effective use o
Page 191 and 192: portrayed as even more time-consumi
Page 193 and 194: Clarke, B. (2001). Corporate curric
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Page 199 and 200: entertaining (Neuhauser 1993). The
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Page 207 and 208: Figure 6. NARRATE phase screen afte
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Page 213 and 214: Rumelhart, D. E. (1975). Notes on a
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Page 223 and 224: Herrington, J., & Herrington, A. (1
Page 225 and 226: services required by most clients i
Page 227 and 228: Wisdom. J. P., White, N., Goldsmith
Page 229 and 230: Work outside the U.S. has also prom
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Page 233 and 234: Parent-advocate respondents also re
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Page 239 and 240: Thus, while we have reason to belie
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Page 249 and 250: Conclusion This study examined the
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The design and validation of a text
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Predetermined goal of a game The pr
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A true, precise and valid model “
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Bain, C., & Newton, C. (2003). Art
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Prensky, M. (2005). Adopt and Adapt
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opportunities based on studying dif
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The game -called Chase the Cheese-
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another token. Other tokens further
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The system is organized in differen
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“to produce a single product or p
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(e.g., cheese) or loses the game (a
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collaboration is not an easy task.
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Collazos, C., Ortega, M., Bravo, C.
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Soller, A., & Lesgold, A. (2000). K
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Is the usual hierarchical organizat
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The final step 7 in the procedure i
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This methodology is summarised in F
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‘IsBasisOf’, ‘HasConstraint
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The presented results are a reflect
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‘HasExample’, and ‘HasFurther
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DCMI (2003). DCMI Metadata Terms, R
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Literature review Astronomy educati
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Systems of type I provide different
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night. To prevent students from get
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Figures 2-5 display screenshots of
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Results of the study reveal the imp
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vertical axis leads to the disappea
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Dede, C., Salzman, M., Loftin, R. B
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Trumper, R. (2000). University stud
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ased on pre-defined adaptation rule
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Therefore, the higher-level items o
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common prefix with the existing pa
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later should be omitted (presented
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5.1 The Average Length of each Sequ
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Recall 6. Discussion 0.8 0.7 0.6 0.
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easily take lessons in a good learn
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Laborda, J. G., & Royo, T. M. (2007
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Chapter 8, Online reference tools,
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Kenyon, D. M., & Malabonga, V. (200
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and concise manner (e.g. Weeding =
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stream are chapters 7 and 8 which f
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