On the average Web page, users have time to read at most 28% of the words during an average visit; 20% is more likely (Nielsen 2006).
Nielsen, le célèbre auteur de "Don't make me think", vient de publier un article synthétique sur l'étude "How Little Do Users Read" menée par Harald Weinreichs. On remarquera au passage son style simple et cependant amélioré si on regarde son site auparavant.
Visual tools have been developed to help users formulate advanced Internet queries. Users can control the query coordination process at the level of the individual search terms or at the level of the search engines to be consulted. This work addresses the usability issue of the level at which visual query tools can provide the greatest benefit to users searching the Internet. MetaCrystal and its Category View and Cluster Bulls-Eye tools are used to visualize the degree of overlap between the results returned by different query formulations or search engines. It is shown that certain types of similarity relationships between ranked lists give rise to unique visual patterns in the Cluster Bulls-Eye. The MetaCrystal toolset is used to show why visual query tools may provide greater benefits to users who want to coordinate meta searches rather than individual search terms.
The author reflects the conceptual and practical questions of network competence in the context of information seeking. Network competence is seen as one of the information-related competences and is defined as the mastery of four major areas: knowledge of information resources available on the Internet, skilled use of the ICT tools to access information, judgment of the relevance of information, and communication. Drawing on the ideas of the social cognitive theory developed by Albert Bandura, a model of network competence is introduced in order to discuss network competence ?in action?. In the model, network competence is put in practical context by relating five major factors: network competence, self-efficacy, outcome expectations, affective factors such as anxiety, and experiences received from information seeking on the Internet. Particular attention is devoted to the connections between network competence and self-efficacy which denote a person?s judgment of his or her ability to organize and execute action, such as finding information on the Web.
The author reflects the conceptual and practical questions of network competence in the context of information seeking. Network competence is seen as one of the information-related competences and is defined as the mastery of four major areas: knowledge of information resources available on the Internet, skilled use of the ICT tools to access information, judgment of the relevance of information, and communication. Drawing on the ideas of the social cognitive theory developed by Albert Bandura, a model of network competence is introduced in order to discuss network competence ?in action?. In the model, network competence is put in practical context by relating five major factors: network competence, self-efficacy, outcome expectations, affective factors such as anxiety, and experiences received from information seeking on the Internet. Particular attention is devoted to the connections between network competence and self-efficacy which denote a person?s judgment of his or her ability to organize and execute action, such as finding information on the Web.
Visual tools have been developed to help users formulate advanced Internet queries. Users can control the query coordination process at the level of the individual search terms or at the level of the search engines to be consulted. This work addresses the usability issue of the level at which visual query tools can provide the greatest benefit to users searching the Internet. MetaCrystal and its Category View and Cluster Bulls-Eye tools are used to visualize the degree of overlap between the results returned by different query formulations or search engines. It is shown that certain types of similarity relationships between ranked lists give rise to unique visual patterns in the Cluster Bulls-Eye. The MetaCrystal toolset is used to show why visual query tools may provide greater benefits to users who want to coordinate meta searches rather than individual search terms.
