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)".
M. De Choudhury, M. Morris, and R. White. Proceedings of the 32Nd Annual ACM Conference on Human Factors in Computing Systems, page 1365--1376. New York, NY, USA, ACM, (2014)
A. McDonald, and L. Cranor. Proceedings of the 9th annual ACM workshop on Privacy in the electronic society, page 63--72. New York, NY, USA, ACM, (2010)
M. Heckner, and C. Wolff. Information: Droge, Ware oder Commons? Wertschöpfungs- und Transformationsprozesse auf den Informationsmärkten. Proc. 11. Internationales Symposium für Informationswissenschaft, Konstanz, April 2009, volume 50 of Schriften zur Informationswissenschaft, vwh Verlag Werner Hülsbusch, Boizenburg, Beitr. teilw. dt., teilw. engl..(April 2009)