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Unformatted text preview: Hypermedia, Learning and Adaptive Hypermedia What is Hypermedia? hypertext deals with the associative linking between texts, hypermedia extends the hypertext ability to also include other types of media such as image, graphics, audio and simulation. World Wide Web In 1990 TimBernersLee demonstrated the concept of hypertext clientserver approach in a remote and distributed environment, producing what today is known as the World Wide Web or the Web (BernersLee and Cailliau, 1990). Today the Web has emerged as the dominant hypertext technology because it is highly accessible to anyone, anywhere in the world (Ng, 2003). Hypermedia navigation Like any other hypertext system, the Web uses the concept of documents, nodes or pages that are interrelated by a set of navigational links. Users explore the Web by activating the links to navigate from one page to another. Hypermedia navigation While the underlying hypertext structure of the Web easily "permits users to freely explore and follow links in whenever and whatever sequence they please" (Ng, 2003), its freebrowsing behaviour often leads to two shortcomings: `Disorientation' and `Cognitive Overload' (Conklin, 1987). Disorientation Syndrome Also called the "lost in hyperspace syndrome" is experienced by users who browse an information space which have a complex hypermedia structure, and then get lost in it. This occurs when each new encounter interests users, and thus gradually drifts them away from their initial goals. Disorientation Syndrome As highlighted by Jonassen "simply browsing hypertext is not engaging enough to result in more meaningful learning" (Jonassen, 1993). Bajraktarevic confirms it by saying that "simple browsing of Web documents does not necessarily lead to successful learning" (Bajraktarevic, 2003). Cognitive Overload The cognitive overload occurs when user is overwhelmed with massive, difficult and unguided information and options while interacting with the hypermedia system (e.g. Web). Often result in low efficiency of the human mind to absorb and process useful information which may lead to unsuccessful learning (Ng, 2003). heterogeneous learners Webbased applications should cater for heterogeneous learners by applying a specific adaptation according to the characteristic of an individual. Users are different! Fischer states that "the challenge in an information rich world is not only to make information available to people at any time, at any place, and in any form, but specifically to say the `right' thing at the `right' time in the `right' way" (Fischer, 2001). To address these issues... Adaptive Hypermedia Research (appears in early 1990s) plays an important role To adapt links / content to user etc. Users' navigations are guided based on their current needs Users will be presented with information relevant to their knowledge level, learning goals, etc personalising users' learning environment Adaptive Hypermedia techniques enhance a hypertext and hypermedia system by providing directional and cognitive support to users while browsing. This is accomplished by storing some personal features about the user in a user model and then applies this model in order to adapt the presentation of links and content of hypermedia pages to the current need of the user (Brusilovsky, 2001). Adaptive Hypermedia System Components A typical Adaptive Hypermedia System is composed of (Wu et al., 1998):
1. 2. 3. 4. a domain model, a user model, an adaptation model, and an adaptive engine Domain Model Contains a set of domain concepts along with their relationships. There are several commonly used methods to structure a domain model: linear, concept graph, semantic network, hierarchical tree, combined structure, and teaching task and rule-based structure (Carro, 2002). Linear a linear relationship is established among a set of identified concepts or information units, allowing only a sequential type exploration of the hyperspace. Concept Graph defines a domain structure in terms of nodes (represent the information units, concepts, or tasks) and arcs (represent the relationships among the nodes). The arcs of a common concept graph usually represent an `is relatedto' relationship. A specific type of a concept graph called a prerequisite graph is normally used to represent `isprerequisiteof' relationship. Semantic Network Similar to a concept graph, domain structuring based on a semantic network is also composed of a set of nodes and arcs. The only distinctive feature is that the arcs can represent different types of relationships such as `is similarto', `isoppositeto', `ispartof', `is prerequisiteof', `isexampleof' and etc. Hierarchical Tree Usually consists of a set of nodes, which represent basic units of knowledge, and a set of arcs that represent the decomposition relations among them (`is partof' relationship). Each node represents a concept with only one ancestor and its direct descendents represent its sub concepts. teaching task and rule based structure teaching task and rule based structure, an atomic task is defined as the basic unit that represents the concepts, topics or procedures to be learned. teaching task and rule based structure A composed task represents ways of grouping those tasks. A rule is then assigned to the composed task. The rule contains an activation condition that is associated with a user's characteristic or behaviour. By defining several rules for the same composed task using a different set of activation conditions, different structures for different kinds of users that access the same set of topics can be achieved. User Model User model captures individuals' characteristics that encompass each specific user. In an adaptive hypermedia system, a user model is crucial in determining the success of the adaptation process. User Model According to Kavcic, there are three important aspects that have to be considered when designing a user model (Kavcic, 2000): 1) the types of user's information that needs to be captured and how to obtain it; 2) how to represent the information in the system; 3) how to construct and update the model. Capture User Info Information that is normally captured in a user model can be divided into two categories: static and dynamic information. 1. Static information conveys users' personal data such as users' identification and background, for instance user's background knowledge or career. 2. Dynamic information referring to user's information that requires update as a result of their interactions with the system such as knowledge levels and learning goals. Capture User Info This information can be obtained by requesting users to fill in the required information in a form from a dialogue window. The initial value for the dynamic information can also be obtained using the same approach. However, the value should be updated at the end of a session or throughout users' interactions with the system. Representing User Info In representing user's information in an adaptive educational hypermedia system, several types of user models are normally used. The most commonly employed are the overlay model, stereotype model or a combination of both. Representing User Info Overlay over the concepts from Domain Model User's knowledge is regarded as a subset of expert knowledge. Therefore the user model usually contains a list of concepts from the domain model with the corresponding values that indicate the system's belief of how much a student has mastered a given concept. AHS with Overlay Model Among the adaptive hypermedia systems that uses the overlay model are MetaLinks (Murray et al., 1998), Dynamic Course Generator (Vassileva, 1997) and AHA! (DeBra and Calvi, 1998). Representing User Info An individual user is assigned to one or more stereotypes after responding to a series of questions or other form of user input. Each stereotype has its predefined properties and users that belong to that stereotype also inherit its properties. AHS with Stereotype Model For example, HyperTutor (Perez et al., 1995) uses stereotypes where users can belong to one of the following three groups: novice, medium or expert. AHS with Both Models The implementation of both models can be found in WHURLE (Brailsford et al., 2002). ways to create and update a user model. According to Bajraktarevic, in some systems, the user model is created at the start of the learning process and continuously updates stored information as the learner interacts with the system such as in AHA! (DeBra and Calvi, 1998); In other systems it is created at the end of a learning session in which user's performance or interest is tracked over a longer period of time (Bajraktarevic, 2003). In certain systems, it's a mixture of both. Adaptation Model usually contains rules that define how the domain model relates to the user model in order to specify adaptation. The rule usually takes the form of "if <condition> then <action>" (Wu et al., 1998). Adaptation Model By interpreting rules, an adaptive engine will generate the adaptation outcomes by either manipulating the presentation of the link anchors or the fragments of the hypermedia content pages. The adapted page will then be delivered to the users. Source of Adaptation There is a variety of adaptations, including those that adapt to users' knowledge levels (Brailsford et al., 2002 ; DeBra and Calvi, 1998; Vassileva, 1997), learning goals (Murray et al., 1998; Vassileva, 1997), users' interests (DeBra and Calvi, 1998), users' backgrounds (Brailsford et al. 2002), users' experiences (Vassileva, 1997), learning styles (Stash et al., 2004; Kinshuk and Lin, 2004; Bajraktarevic, 2003; Paredes and Rodriguez, 2004; Carver et al., 1999), reading speed (Ng, 2003) and navigational history (Murray et al., 1998, Ng, 2003; DeBra and Calvi, 1998). AH Techniques Source: Brusilovsky (2001) Adaptive Presentation comprises of a collection of techniques for altering the content of page accessed according to the needs of a particular user or a group of users. the majority of the work is in the area of canned text adaptation that is under the adaptive text presentation category. Adaptive Presentation Canned text adaptation deals with text and fragment processing like inserting or removing fragments, altering fragments, stretchtext, sorting fragments and dimming fragments (Brusilovsky, 2001). Adaptive Navigational Support Authoring Reference BernersLee, T., Cailliau, R. (1990) WorldWideWeb: Proposal for a HyperText Project. CERN, Geneva, 1990. Retrieved May 3, 2006 at http://www.w3.org/Proposal.html Brusilovsky, P. (2001). Adaptive Hypermedia. In Alfred Kobsa (Ed.), User Modeling and UserAdapted Interaction, Ten Year Anniversary, 11. 2001. pp. 87129. Brusilovsky, P. (2003). Developing Adaptive Educational Hypermedia Systems: From Design Models to Authoring Tools. In T. Murray, S. Blessing and S. Ainsworth (Eds.): Authoring Tools for Advanced Technology Learning Environmen, Dordrecht: Kluwer Academic Publishers. 2003. Conklin, J. (1987). Hypertext: An Introduction and Survey, IEEE Computer, 20(9). 1987. pp. 1741. Carro, R. M. (2002). Adaptive Hypermedia in Education: New Considerations and Trends, In Proceedings of the 6th World Multi conference on Systemics, Cybernetics and Informatics , Orlando, Florida, 2002, pp. 452458. References Jonassen, D. H. (1993). Effects of Semantically Structured Hypertext Knowledge Bases on Students' Knowledge Structures. In C. McKnight, A. Dillon and J. Richardson (Eds.) Hypertext. A Psychological Perspective. Chichester: Ellis Horwood. 1993. Kavcic, A. (2000). The Role of User Models in Adaptive Hypermedia Systems. In Proceedings of the Electrotechnical Conference , MELECON 2000, 10th Mediterranean, 1, 2000. pp. 119122. Bajraktarevic, N. (2003). Adaptive Hypermedia, Learning Styles and Strategies within the Educational Paradigm. PhD Thesis, University of Southampton. September 2003. Ng, M.H. (2003). Integrating Adaptivity into Webbased Learning. PhD Thesis, University of Southampton, March 2003. Fischer, G. (2001). User Modeling in HumanComputer Interaction, Journal of User and UserAdapted Interaction , 11, 2001. pp. 6586. References AHS DeBra, P., Aerts, A., Smits, D., and Stash, N. (2002). AHA! Version 2.0: More Adaptation Flexibility for Authors, In Proceedings of the AACE ELearn'2002 Conference, October, 2002, pp. 240246. Murray, T. (2002). MetaLinks: Authoring and Affordances for Conceptual and Narrative Flow in Adaptive Hyperbooks, In International Journal of Artificial Intelligence in Education. 2002. Retrieved February 20, 2006 at http:// helios.hampshire.edu/~tjmCCS/papers/ MLIJAIED2001. subm1.doc Perez, Gutierrez, Lopistequy, and Uzandizaga (1995). HyperTutor: From Hypermedia to Intelligent Adaptive Hypermedia'. In Proceedings of the World Conference on Educational Multimedia and Hypermedia, (EDMEDIA'95). Graz, Austria, 1995. pp. 529534. Vassileva, J. (1997). Dynamic Course Generation on the WWW. In the Proceedings of the Workshop Adaptive Systems and User Modeling on the World Wide Web, 6th International Conference on User Modeling, Chia Laguna, Sardinia, 1997. Retrieved February 20, 2006 at http://www.contrib.andrew.cmu.edu/~plb / AIED97_workshop/Vassileva/Vassileva.html Brailsford, T.J., Stewart, C.D., Zakaria, M.R. and Moore, A. (2002). Autonavigational, Links and Narrative in An Adaptive WebBased Integrated Learning Environment. In Proceedings of the 11th International World Wide Web Conference (WWW2002). Honolulu, Hawaii, USA. May 711, 2002. Retrieved April 3, 2006 at http://whurle.sourceforge.net/ Tutorial Questions Write a summary about the following Adaptive Hypermedia Systems, emphasizing on their system components and adaptive hypermedia techniques employed: AHA! (Adaptive Hypermedia Architecture) DCG (Dynamic Course Generator) MetaLink WHURLE (Webbased Hierarchical Universal Reactive Learning Environment ) ...
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This note was uploaded on 02/06/2012 for the course FACULTY OF WXGE6320 taught by Professor Noraini during the Winter '09 term at University of Malaya.
- Winter '09