In hypertext research, log files represent a useful source of information about users’ navigational behavior. Since log files can contain enormous amounts of data, methods for data reduction with a minimum loss of information are needed. In this paper, LOGPAT (Log file Pattern Analysis) is presented, a Web-based tool for analyzing log files. With LOGPAT, single-unit, sequential, and graph-theoretic measures (including distance matrices) for the description of user navigation can be computed. The paper gives an overview of these methods and discusses their value for psychological research on hypertext. Components and analysis options of LOGPAT are described in detail. The program’s basic options are illustrated by data from a study on learning with hypertext.
Rough Logs: A Data Reduction Approach for Log Files
