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Personalized Educational Hypermedia - Introduction, Conceptual Framework and Functionality Layers of Personalized Education System

user learning based adaptive

Apple W. P. Fok and Horace H. S. Ip
Sun Centre of Excellence on Pervasive Computing
Centre for Innovative Applications of Internet and Multimedia
Technologies, City University of Hong Kong, Hong Kong

Definition: Enabling technologies that support the personalization of Web-based applications include technologies for user profiling, intelligent search, filtering and recommendation ofhypermedia contents, adaptive hypermedia, tracking and characterization of user browsing behaviors, and adaptive user interface.

Introduction

The emergence of personalization technologies on the Web allows customized information to be delivered to users according to their pertinent needs and interests as perceived by the system. Enabling technologies that support the personalization of web-based applications include technologies for user profiling, intelligent search, filtering and recommendation of hypermedia contents, adaptive hypermedia, tracking and characterization of user browsing behaviors, and adaptive user interface. In order to boost the commercial advantages and to retain internet-based customers’ loyalties, personalization technologies have been adopted and driven predominantly by e-commerce applications and information portals such as My Yahoo, Amazon, Persona etc. Enterprises increase their profits by providing personalization services to their customer on the web, for example, by providing personalized features such as making suggestions based on users’ transaction and historical records. The idea of personalization can be summarized into three main themes: “Building a meaningful one-to-one relationship, “Delivering appropriate content and services to fulfill user’s needs, and “Understanding where and when to suggest the ‘right’ things . The ultimate goal of personalization is “User satisfaction”. User satisfaction means getting the right thing at the right time in the right place.

In order to create a personalized experience, a general Personalized Hypermedia System must perform several distinct tasks: i) Identify the user; ii) Store and Update user information; iii) Learn and identify the user preferences, needs and interests; and iv) Provide and recommend specific personalized services. In summary, personalization techniques serve to enable the system to know the user, remember the user, and adjust their personal memory of the user according to the user’s changing needs.

With the increasing demand of learning just-in-time and just-in-place, the Web has become a new and effective channel for education and the acquisition of knowledge, particularly in adult education and distant learning. Streaming technology allows the delivery of continuous media such as video and audio content for educational purpose. The process of learning in the cyber space is more complex than conventional class learning for many reasons. From the educational point of views, the educational philosophers and psychologists are interested in the influences of technologies in human’s beliefs, knowledge and learning. Research in the learning environment, on the other hand, focused on the changes of traditional classroom settings to computer-based learning and the impacts of web-based learning have on the learning process.

The demand of adaptability in a computer-based educational system is due to the fact that every person possesses distinctive collection of talents, abilities, and limitations. One-size-fits-all approach to education forgets that individuals are different and have different needs. In response to this demand, research interest towards personalized education hypermedia systems has evolved. An emerging approach is the Adaptive Educational Hypermedia Systems, which inherits and combines two earlier paradigms of computer-based educational systems: Intelligent Tutoring systems (ITSs) and Adaptive Hypermedia Systems (AHSs). Adaptive educational hypermedia systems (AEH) interact with user through a learner model of the goals, preferences and knowledge of each individual learner and adapt the hypermedia to the needs of that user. A challenge to the development of adaptive hypermedia system is the authoring of adaptive hypermedia materials. In additional to hypermedia content, the presentation of the content should also be adaptive to the user needs through mechanisms such as link annotation link hiding, and conditional inclusion of materials. The Adaptive Hypermedia Architecture (AHA!) provides a set of authoring tools and a run-time environment for creating and presenting adaptive hypermedia courseware.

Conceptual Framework and Functionality Layers of Personalized Education System

Instead of focusing on the creation and presentation of adaptive hypermedia materials, a Personalized Educational System (PES) takes advantage of the vast resource of educational hypermedia on the Web and integrates a range of supportive tools for participants to conduct their teaching or learning activities in a personalized manner. These supports range from automatic search of relevant teaching and learning hypermedia materials from the Web, anticipating needs of the participants based on his/her individual descriptive profile and the filtering, matching and sequencing of the retrieved materials based on dynamic monitoring of the student’s progress supported by a learner model or based on the teacher’s intended pedagogy. Due to the diverse but inter-related functions that need to be provided in a Personalized Educational System (PES), software agent technology has been applied to the design and development of a PES. Particularly, an agent architecture has been proposed that deploys a team of Personalized agents to achieve the goals of a PES. Figure 1 shows the conceptual agent framework of PES.

The functionality of PES can be divided into three functional layers. Action Layer: Within this layer, personal agents positioned between the browser and the Web, capture a complete clickstream history for all hypermedia resources visited by the user, and the complete navigational history of the user.

A user profile is generated together with its schema that is both human readable and machine processable. Web servers use the profile information, obtained from the client to deliver personalized information. Domain-specific Search agents as intelligent searching assistant focus on getting relevant hypermedia or documents in one specific subject domain (i.e. educational websites or teaching/learning resources). These agents can recognize not only the types of information provided, but also automatically classified those retrieved documents and hypermedia into different categories based on the subject ontologies as well as the personalized attributes stored in the personalized knowledge base/libraries. Industrial Layer: this is where collaboration work takes place. The actual process matching retrieved content to the learner’s needs is carried out by a Matching Agent. The task of the matching agent is to match document metadata against registered profiles. Also in this layer, intelligent filtering agent transforms static data and information into meaningful and useful information for knowledge construction.

Various filtering and data mining techniques would be used. Demographic filtering technique uses to identify the relationship between a particular resource and the type of users. Content-based filtering technique is used to learn the relationship between the content and a single user. Collaborative filtering technique uses the “like minded” approach, in which the navigational history of each user is kept to determine the learning dependent behaviors to make recommendations based on the user profile groups. Knowledge Layer: Educational psychologies, philosophies and instruction design theories/principles as the cognitive knowledge domain of the decision-theoretic agents, these agents are capable of handling queries from other agents for expert advices or diagnosing learner behaviors. To generate different types of profiles and accurately locate the relevant information, the profiling agents take up the responsibilities of handling different representation of profiles and automatically construct an active profile for the other agents. Each learner has his/her own knowledge status which is constructed throughout his/her own development process (i.e. learning or experience). Various pieces of data form different episodes. The Knowledge Management agent helps to manage and organize the acquired knowledge for personalization processes. Figure 2 illustrates the layer structure of the PES agent functionality.

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