Web Interfaces Destined for People with Disabilities

One of the main characteristics of the world that we live in is the access to information and one of the main ways to reach the information is the Internet. Most Internet sites put accessibility problem on a secondary plan. If we try to define this concept (accessibility) we could say that accessibility it’s a way to offer access to information for the people with disabilities. For example blind people can’t navigate on the Internet like usual people. For that reason Internet sites have to put at their disposal ways to make their content known to this people. Accessibility does not refer only at blind people the web accessibility refers to all people who lost their ability to access the Internet sites. The web accessibility includes every disability that stops people with disabilities to access the web sites content like hearing disability, neurological and cognitive. People that have low speed Internet connection or with low performance computers can use the web accessibility.

Collaborative Learning and Authoring in the Frame of e-Projects

It is presented a research on the application of a collaborative learning and authoring during all delivery phases of e-learning programmes or e-courses offered by educational institutions. The possibilities for modelling of an e-project as a specific management process based on planned, dynamically changing or accidentally arising sequences of learning activities, is discussed. New approaches for project-based and collaborative learning and authoring are presented. Special types of test questions are introduced which allow test generation and authoring based on learners’ answers accumulated in the frame of given e-course. Experiments are carried out in an e-learning environment, named BEST.

An Approach to Collaborative Filtering by ARTMAP Neural Networks

Recommender systems are now widely used in e-commerce applications to assist customers to find relevant products from the many that are frequently available. Collaborative filtering (CF) is a key component of many of these systems, in which recommendations are made to users based on the opinions of similar users in a system. This paper presents a model-based approach to CF by using supervised ARTMAP neural networks (NN). This approach deploys formation of reference vectors, which makes a CF recommendation system able to classify user profile patterns into classes of similar profiles. Empirical results reported show that the proposed approach performs better than similar CF systems based on unsupervised ART2 NN or neighbourhood-based algorithm.