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AQUA - Automatic Quality Assessment and Feedback in eLearning 2.0 The current development of Web 2.0 makes the distinction between author and reader fading away. Users now produce huge amounts of data which sometimes is of questionable quality. This leads to the problem of information overload: how to make the most of this information without overwhelming the users? One key challenge to solve this issue is to assess the quality of the user generated content. In AQUA, we seek to develop algorithms to assess the quality of content automatically. We focus on two sources for this assessment: (1) user generated content; (2) feedback by users of the content. To do so, we investigate techniques from the fields of natural language processing (NLP), information retrieval, and machine learning. So, in a nutshell, AQUA will answer the following questions: What is quality of information? How does it matter in information search? How to model the quality of user generated content? How far can you go with automatic methods in assessing quality? How to give feedback to users regarding quality? The AQUA project is associated with the project "Mining Lexical-Semantic Knowledge from Dynamic and Linguistic Sources and Integration into Question Answering for Discourse-Based Knowledge Acquisition in e-learning (QA-EL)".

JWARP is an international refereed journal dedicated to the latest advancement of water resources and protection. The goal of this journal is to keep a record of the state-of-the-art research and promote the research work in these fast moving areas.

This IPIP Website is intended to provide rapid access to measures of individual differences, all in the public domain, to be developed conjointly among scientists worldwide. Later, the site may include raw data available for reanalysis; in addition, it should serve as a forum for the dissemination of psychometric ideas and research findings.