Making sense of low-level usage data to understand user activities

Empirical studies of user activity, based on data collected from the systems with which users interact, present technical challenges related to the transformation of data streams to a form suitable for analysis. In this paper we discuss the particular challenges confronted during a study of user interruption behaviour based on low-level "keystroke" data and the ways in which these challenges were addressed. We also report on a method of data cleaning and analytical preparation that was developed and consider its effectiveness and potential applicability for similar studies.

[1]  Sheryl L. Miller.  Window of Opportunity: Using the Interruption Lag to Manage Disruption in Complex Tasks , 2002 .

[2]  Christopher A. Monk,et al.  The Attentional Costs of Interrupting Task Performance at Various Stages , 2002 .

[3]  Thomas C. Reeves,et al.  Interactive Learning Systems Evaluation , 2003 .

[4]  Eric Deeson,et al.  Interactive learning systems evaluation , 2004, Br. J. Educ. Technol..

[5]  F. J. Roethlisberger,et al.  Management and the Worker , 1941 .

[6]  Gregor Kennedy,et al.  GENERIC USAGE MONITORING OF PROGRAMMING STUDENTS , 2003 .

[7]  P. Cabon,et al.  Interruption of a Monotonous Activity with Complex Tasks: Effects of Individual Differences , 1990 .

[8]  F. J. Roethlisberger,et al.  Management and the Worker , 2003 .

[9]  Penelope M. Sanderson,et al.  Exploratory sequential data analysis: exploring continuous observational data , 1996, INTR.

[10]  Richard A. Schwier,et al.  Representing Interactive Multimedia and Hypermedia Audit Trails. , 1992 .

[11]  Ian T. Foster,et al.  The virtual data grid: a new model and architecture for data-intensive collaboration , 2003, 15th International Conference on Scientific and Statistical Database Management, 2003..

[12]  Brian P. Bailey,et al.  The Effects of Interruptions on Task Performance, Annoyance, and Anxiety in the User Interface , 2001, INTERACT.

[13]  Gregor E. Kennedy,et al.  ITERATIVE ANALYSIS AND INTERPRETATION OF AUDIT TRAIL DATA , 2003 .

[14]  D. C. McFarlane,et al.  Recovering Context After Interruption , 2019, Proceedings of the Twenty-Fourth Annual Conference of the Cognitive Science Society.

[15]  David M. Hilbert,et al.  A Survey of Computer-Aided Techniques for Extracting Usability Information from User Interface Events , 1998 .

[16]  Egon Berghout,et al.  Interrupts: Just a Minute Never Is , 1998, IEEE Softw..

[17]  Austin Henderson,et al.  A multiple, virtual-workspace interface to support user task switching , 1986, CHI '87.

[18]  Daniel C. McFarlane,et al.  Interruption of People in Human-Computer Interaction: A General Unifying Definition of Human Interruption and Taxonomy , 1997 .

[19]  Malcolm P. Atkinson,et al.  The pervasiveness of evolution in GRUMPS software , 2003, Softw. Pract. Exp..

[20]  R. H. Franke,et al.  The Hawthorne experiments: First statistical interpretation. , 1978 .

[21]  David F. Redmiles,et al.  Extracting usability information from user interface events , 2000, CSUR.

[22]  E. M. Altmann,et al.  Task Interruption: Resumption Lag and the Role of Cues , 2004 .

[23]  Wendy E. Mackay,et al.  DIVA: exploratory data analysis with multimedia streams , 1998, CHI.

[24]  Robert W. Irving.  Plagiarism and Collusion Detection using the Smith-Waterman Algorithm , 2004 .

[25]  J. George,et al.  Work Interrupted: A Closer Look at the Role of Interruptions in Organizational Life , 2003 .

[26]  M S Waterman,et al.  Identification of common molecular subsequences. , 1981, Journal of molecular biology.