Perception based User Profiles for Web Personalization

Personalized web services reduce the burden of information overload by collecting facts that match the needs of the user. An important aspect of personalized web services is the creation of user profiles that contain user information and settings. This article introduces a unique method called Perception-Based User Profiles (PUP) based on perception and browsing order, develops and updates user profiles. User profiles include perceptions and relationships, which can help guarantee that user interests are represented semantically. Second, when calculating the perception and duration of the relationship, for each site in a session, the user's browsing order is considered. Third, cognitive psychometric memory model is used to update the user profile's perceptions and relationships at the end of each session, ensuring the user profile's dynamics. The results of the tests suggest that this strategy works well for building and updating user profiles

E-Learning: From A Pedagogical Perspective

The steady growing innovations in the area of information and communication technology have raised new concepts and possibilities in different life aspects. In the field of further education and professional training, electronic learning and Web-based education are perhaps the most prominent ones. Proponents of this technology claim that e-learning courses are at least as effective as corresponding traditional ones, and therefore sometimes a very good substitute for it. Although there are so many similarities between traditional and e-learning systems, confronting the extended range of e-learning users -with very different prior knowledge of the domain, backgrounds, learning styles, interests and preferences- is no more possible with the “one-size-fits-all” approach. Hence, creation and management of instructional content would be the major hazard in e-learning industry. Contents should be provided considering social, cultural and pedagogical characteristics of the learners. E-learning covers a wide set of applications and processes. With such an extended scope, covering number of available e-learning tools is extensive. Though, in recent years, features and capabilities of authoring tools have been drastically improved. Concepts such as “adapting to the needs of learners” and “personalized content” make authoring tools play a more prominent role in the process of creating learning contents. In this paper, we propose a new pedagogical perspective in web-based learning environments. This perspective explores the most prominent opportunities of the information technology era, in order to ensure a more meaningful learning. Advantages, limitations and particularly deficiencies of e-learning systems are investigated based on this perspective. Also, in order to cover the importance of authoring tools in the performance of e-learning systems, capabilities and limitations of current available authoring tools are comparatively studied. These comparisons are based on criteria such as compatibility with e-learning standards, the amount of time and cost needed for the instructional design and potential features. Outcomes of the study emphasize on the importance of the learning variables such as cognitive, social and affective learners’ characteristics, which play a critical role in the design and implementation of web-based learning systems. These outcomes would certainly be of significant help with enhancing the decision making procedure for managers and presidents of learning areas, which may be overwhelmed by all the technology decisions they have to make, the number of choices available, and the terminology they may not be familiar with. These outcomes would basically lead to determining basic factors of learner satisfaction and therefore improving educational performance

A Personalized E-Learning System Based on User Profile Constructed Using Information Fusion

In this paper, we describe a personalized e-learning system which can automatically adapt to the interests and levels of learners. The system is designed based on the IEEE Learning Technology Systems Architecture (IEEE LTSA) to achieve high scalability and reusability. A feedback extractor with fusion capability is proposed to combine multiple feedback measures to infer user preferences. User profile, which stores user preferences and levels of expertise, is collected by user profiler to deliver personalized information using the collaborative filtering algorithm. 1

INCREMENTAL QUERY PROCESSING IN INFORMATION FUSION SYSTEMS

This dissertation studies the methodology and techniques of information retrieval in fusion systems where information referring to same objects is assessed on the basis of data from multiple heterogeneous data sources. A wide range of important applications can be categorized as information fusion systems e.g. multisensor surveillance system, local search system, multisource medical diagnose system, and so on. Up to the time of this dissertation, most information retrieval methods in fusion systems are highly domain specific, and most query systems do not address fusion problem with enough efforts. In this dissertation, I describe a broadly applicable query based information retrieval approach in general fusion systems: user information needs are interpreted as fusion queries, and the query processing techniques e.g. source dependence graph (SDG), query refinement and optimization are described. Aiming to remove the query building bottleneck, a novel incremental query method is proposed, which can eliminate the accumulated complexity in query building as well as in query execution. Query pattern is defined to capture and reuse repeated structures in the incremental queries. Several new techniques for query pattern matching and learning are described in detail. Some important experiments in a real-world multisensor fusion system, i.e. the intelligent vehicle tracking (IVET) system, have been presented to validate the proposed methodology and techniques

System architecture, content authentication and digital right management (DRM) for eLearning

This thesis provides a frame for Personal eLearning System (PELS) with content security, authentication and Digital Right Management. It presents an efficient algorithm for eLearning problem-solution using graph partitioning and weighted bipartite graph. The research provides an eLearning Analytics Ecosystem using statistical methods for validation of Quality of Results (QoR).<br /

A framework for an adaptable and personalised e-learning system based on free web resources

An adaptable and personalised E-learning system (APELS) architecture is developed to provide a framework for the development of comprehensive learning environments for learners who cannot follow a conventional programme of study. The system extracts information from freely available resources on the Web taking into consideration the learners' background and requirements to design modules and a planner system to organise the extracted learning material to facilitate the learning process. The process is supported by the development of an ontology to optimise and support the information extraction process. Additionally, natural language processing techniques are utilised to evaluate a topic's content against a set of learning outcomes as defined by standard curricula. An application in the computer science field is used to illustrate the working mechanisms of the proposed framework and its evaluation based on the ACM/IEEE Computing Curriculum.A variety of models are developed and techniques used to support the adaptability and personalisation features of APELS. First, a learner’s model was designed by incorporating students’ details, students’ requirements and the domain they wish to study into the system. In addition, learning style theories were adopted as a way of identifying and categorising the individuals, to improve their on-line learning experience and applying it to the learner’s model. Secondly, the knowledge extraction model is responsible for the extraction of the learning resources from the Web that would satisfy the learners’ needs and learning outcomes. To support this process, an ontology was developed to retrieve the relevant information as per users’ needs. In addition, it transforms HTML documents to XHTML to provide the information in an accessible format and easier for extraction and comparison purposes. Moreover, a matching process was implemented to compute the similarity measure between the ontology concepts that are used in the ACM/IEEE Computer Science Curriculum and those extracted from the websites. The website with the highest similarity score is selected as the best matching website that satisfies the learners’ request. A further step is required to evaluate whether the content extracted by the system is the appropriate learning material of the subject. For this purpose, the learning outcome validation process is added to ensure that the content of the selected websites will enable the appropriate learning based to the learning outcomes set by standard curricula. Finally, the information extracted by the system will be passed to a Planner model that will structure the content into lectures, tutorials and workshops based on some predefined learning constraints. The APELS system provides a novel addition to the field of adaptive E-learning systems by providing more personalized learning material to each user in a time-efficient way saving his/her time looking for the right course from the hugely available resources on the Web or going through the large number of websites and links returned by traditional search engines. The APELS system will adapt better to the learner’s style based on feedback and assessment once the learning process is initiated by the learner. The APELS system is expected to develop over time with more users