Combining ethnographic and clickstream data to identify user browsing strategies Export

@article{citeulike:468423, abstract = {This paper describes a novel approach that combines data from direct observation, user surveys and server logs to analyse users' browsing behaviour. It is based on a longitudinal study of university students' use of a Website related to one of their courses. The data was analysed using footstep graphs to categorise browsing behaviour into pre-defined strategies and comparing these with data from questionnaires and direct observation of the students' actual use of the site.}, author = {Clark, Lillian and Ting, I-Hsien and Kimble, Chris and Wright, Peter and Kudenko, Daniel}, chapter = {paper249}, citeulike-article-id = {468423}, citeulike-linkout-0 = {http://informationr.net/ir/11-2/paper249.html}, day = {19}, journal = {Information Research}, keywords = {ai, artificial\_intelligence, browsing\_behaviour, clickstream, datamining, design, distance\_education, e-learning, education, hci, human\_factors, is, learning, linkanalysis, usability, virtual-ethnography, web\_mining, web\_usage\_mining, webanalytics, webmetrics, webstats}, month = {January}, number = {2}, posted-at = {2006-01-18 12:10:28}, priority = {3}, title = {Combining ethnographic and clickstream data to identify user browsing strategies}, url = {http://informationr.net/ir/11-2/paper249.html}, volume = {11}, year = {2006} }

TY - JOUR ID - citeulike:468423 L3 - citeulike-article-id:468423 N2 - This paper describes a novel approach that combines data from direct observation, user surveys and server logs to analyse users' browsing behaviour. It is based on a longitudinal study of university students' use of a Website related to one of their courses. The data was analysed using footstep graphs to categorise browsing behaviour into pre-defined strategies and comparing these with data from questionnaires and direct observation of the students' actual use of the site. IS - 2 JF - Information Research TI - Combining ethnographic and clickstream data to identify user browsing strategies VL - 11 KW - ai KW - artificial_intelligence KW - browsing_behaviour KW - clickstream KW - datamining KW - design KW - distance_education KW - e-learning KW - education KW - hci KW - human_factors KW - is KW - learning KW - linkanalysis KW - usability KW - virtual-ethnography KW - web_mining KW - web_usage_mining KW - webanalytics KW - webmetrics KW - webstats AU - Clark, Lillian AU - Ting, I-Hsien AU - Kimble, Chris AU - Wright, Peter AU - Kudenko, Daniel PY - 2006/01/19/ UR - http://informationr.net/ir/11-2/paper249.html ER -