Development of an intranet website

Established in 1 990, Logical Design Solutions Incorporated (LDS) was among the first to recognize the World Wide Web (Web) as a platform for deploying serious enterprise solutions. LDS provides Global 2000 companies, such as AT&T Corporation (AT&T), with Internet, extranet, and intranet applications that solve real-world business problems. A leader in Web consulting, LDS offers a comprehensive set of solutions and services, from design to development, tailored to organizations with complex enterprise technology needs. In late 1997, AT&T requested LDS to begin developing an intranet strategy that would support AT&T\u27s Cash Balance Pension Plan. AT&T, formerly known as the American Telephone and Telegraph Company, is the largest telecommunications company in the United States, and a worldwide leader in communications services. The objective of this thesis project was for the author to learn, create, and document the Analysis and Design, Development and Implementation, and Quality Assurance Testing phases of AT&T\u27s Pension Update Information Center Intranet Website. The author, the HTML developer, was a key team member of LDS\u27s project team. The LDS project team acted as an extension of AT&T\u27s internal management team providing industry-specific knowledge and Web expertise throughout the development of Pension Update Information Center. While the primary business objective of Pension Update Information Center was to maximize internal communications and at the same time, reduce the rollout cost of AT&T\u27s pension benefit program that impacts 72,600 employees, the LDS project team also had to meet the following secondary business objectives: e To reduce call and work volume at AT&T\u27s Customer Care Center (CCC). e Support AT&T\u27s overall pension communication strategy. Establish the intranet Website as a credible, reliable, and engaging source of information. In order for the LDS project team to meet these business objectives, the following three evolutionary phases of Pension Update Information Center had to be achieved: 1 . Analysis and Design during this phase, the LDS project team worked closely with AT&T\u27s management team and formulated Web strategies and requirements that mirrored the business objectives. 2. Implementation and Development during this phase, the LDS project team integrated the formulated business requirements with leading edge technology, a usable interface design, creative look and feel design, and optimized content. 3. Quality Assurance Testing during this phase, the LDS project team thoroughly tested the implemented the Website until all erroneous information was fixed and business objectives were met. After the above three phases were implemented and approved, the LDS project team delivered a Website that contained a viable, successful Web strategy that balanced AT&T\u27s business and enterprise objectives for user needs and competitive market challenges. The documentation that follows discusses the lifecycle approach that the LDS project team used to meet AT&T\u27s objectives and that the author used to gain an understanding of intranet methodology

Adaptive hypermedia for education and training

Adaptive hypermedia (AH) is an alternative to the traditional, one-size-fits-all approach in the development of hypermedia systems. AH systems build a model of the goals, preferences, and knowledge of each individual user; this model is used throughout the interaction with the user to adapt to the needs of that particular user (Brusilovsky, 1996b). For example, a student in an adaptive educational hypermedia system will be given a presentation that is adapted specifically to his or her knowledge of the subject (De Bra & Calvi, 1998; Hothi, Hall, & Sly, 2000) as well as a suggested set of the most relevant links to proceed further (Brusilovsky, Eklund, & Schwarz, 1998; Kavcic, 2004). An adaptive electronic encyclopedia will personalize the content of an article to augment the user's existing knowledge and interests (Bontcheva & Wilks, 2005; Milosavljevic, 1997). A museum guide will adapt the presentation about every visited object to the user's individual path through the museum (Oberlander et al., 1998; Stock et al., 2007). Adaptive hypermedia belongs to the class of user-adaptive systems (Schneider-Hufschmidt, Kühme, & Malinowski, 1993). A distinctive feature of an adaptive system is an explicit user model that represents user knowledge, goals, and interests, as well as other features that enable the system to adapt to different users with their own specific set of goals. An adaptive system collects data for the user model from various sources that can include implicitly observing user interaction and explicitly requesting direct input from the user. The user model is applied to provide an adaptation effect, that is, tailor interaction to different users in the same context. In different kinds of adaptive systems, adaptation effects could vary greatly. In AH systems, it is limited to three major adaptation technologies: adaptive content selection, adaptive navigation support, and adaptive presentation. The first of these three technologies comes from the fields of adaptive information retrieval (IR) and intelligent tutoring systems (ITS). When the user searches for information, the system adaptively selects and prioritizes the most relevant items (Brajnik, Guida, & Tasso, 1987; Brusilovsky, 1992b)

Knowledge Representation with Ontologies: The Present and Future

Recently, we have seen an explosion of interest in ontologies as artifacts to represent human knowledge and as critical components in knowledge management, the semantic Web, business-to-business applications, and several other application areas. Various research communities commonly assume that ontologies are the appropriate modeling structure for representing knowledge. However, little discussion has occurred regarding the actual range of knowledge an ontology can successfully represent

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Information Outlook, June 1997

Volume 1, Issue 6https://scholarworks.sjsu.edu/sla_io_1997/1005/thumbnail.jp

Reviews

Teaching and Learning Materials and the Internet by Ian Forsyth, London: Kogan Page, 1996. ISBN: 0–7494‐ 20596. 181 pages, paperback. £18.99

E-Commerce Applications Ranking

The paper presents the cycle of development of e-Commerce applications. The e-commerce applications are analyzed being considered to be a subject for complex evaluations. A set of criteria and factors are presented being considered relevant for e-commerce applications used in complex assessments. A ranking algorithm is proposed based on the AHP (Analytic Hierarchy Process) process, which was implemented and tested with online application IAID. The objective of this paper is to build, implement and test this algorithm with the online application IAID.E-Commerce, Hierarchy, Evaluation Criteria, Analyses, Ranking Algorithm

The Best Trail Algorithm for Assisted Navigation of Web Sites

We present an algorithm called the Best Trail Algorithm, which helps solve the hypertext navigation problem by automating the construction of memex-like trails through the corpus. The algorithm performs a probabilistic best-first expansion of a set of navigation trees to find relevant and compact trails. We describe the implementation of the algorithm, scoring methods for trails, filtering algorithms and a new metric called \emph{potential gain} which measures the potential of a page for future navigation opportunities.Comment: 11 pages, 11 figure