Studying the Factors Influencing Automatic User Task Detection on the Computer Desktop

Supporting learning activities during work has gained momentum for organizations since work-integrated learning (WIL) has been shown to increase productivity of knowledge workers. WIL aims at fostering learning at the workplace, during work, for enhancing task performance. A key challenge for enabling task-specific, contextualized, personalized learning and work support is to automatically detect the user's task. In this paper we utilize our ontology-based user task detection approach for studying the factors influencing task detection performance. We describe three laboratory experiments we have performed in two domains including over 40 users and more than 500 recorded task executions. The insights gained from our evaluation are: (i) the J48 decision tree and Naive Bayes classifiers perform best, (ii) six features can be isolated, which provide good classification accuracy, (iii) knowledge-intensive tasks can be classified as well as routine tasks and (iv) a classifier trained by experts on standardized tasks can be used to classify users' personal tasks.

[1]  Andreas S. Rath,et al.  Analysis of machine learning techniques for context extraction , 2008, 2008 Third International Conference on Digital Information Management.

[2]  Arne Beckhaus,et al.  Enhancing Just-in-Time E-Learning Through Machine Learning on Desktop Context Sensors , 2007, CONTEXT.

[3]  Gregory D. Abowd,et al.  A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications , 2001, Hum. Comput. Interact..

[4]  Matthias Baldauf,et al.  A survey on context-aware systems , 2007, Int. J. Ad Hoc Ubiquitous Comput..

[5]  Erik Duval,et al.  A LOM Research Agenda , 2003, WWW.

[6]  M. Eraut,et al.  Informal learning in the workplace , 2004 .

[7]  George Karypis,et al.  Hierarchical Clustering Algorithms for Document Datasets , 2005, Data Mining and Knowledge Discovery.

[8]  Roman Kern,et al.  Knowledge Discovery using the Knowminer Framework , 2009 .

[9]  Thomas G. Dietterich,et al.  A hybrid learning system for recognizing user tasks from desktop activities and email messages , 2006, IUI '06.

[10]  Stefanie N. Lindstaedt,et al.  Knowledge Services for Work-Integrated Learning , 2008, EC-TEL.

[11]  Mark Dredze,et al.  Automatically classifying emails into activities , 2006, IUI '06.

[12]  Peter J. Smith.  Workplace Learning and Flexible Delivery , 2003 .

[13]  Stefanie N. Lindstaedt,et al.  Modeling competencies for supporting work-integrated learning in knowledge work , 2008, J. Knowl. Manag..

[14]  Ian Witten,et al.  Data Mining , 2000 .

[15]  Stefanie N. Lindstaedt,et al.  A Context-Model for Supporting Work-Integrated Learning , 2006, EC-TEL.

[16]  Guus Schreiber,et al.  Knowledge Engineering and Management: The CommonKADS Methodology , 1999 .

[17]  Claudia Linnhoff-Popien,et al.  A Context Modeling Survey , 2004 .

[18]  Erik Duval,et al.  Tracking Actual Usage: the Attention Metadata Approach , 2007, J. Educ. Technol. Soc..

[19]  Andreas S. Rath,et al.  UICO: an ontology-based user interaction context model for automatic task detection on the computer desktop , 2009, CIAO '09.

[20]  Jude W. Shavlik,et al.  Learning users' interests by unobtrusively observing their normal behavior , 2000, IUI '00.

[21]  Wolfgang Nejdl,et al.  Innovative Approaches for Learning and Knowledge Sharing, First European Conference on Technology Enhanced Learning, EC-TEL 2006, Crete, Greece, October 1-4, 2006, Proceedings , 2006, EC-TEL.

[22]  Gerhard Fischer,et al.  User Modeling in Human–Computer Interaction , 2001, User Modeling and User-Adapted Interaction.

[23]  Yiming Yang,et al.  A re-examination of text categorization methods , 1999, SIGIR '99.

[24]  Stefanie N. Lindstaedt,et al.  Applying Scruffy Methods to Enable Work-integrated Learning , 2008 .

[25]  Thomas G. Dietterich,et al.  Detecting and correcting user activity switches: algorithms and interfaces , 2009, IUI.

[26]  Clemens H. Cap,et al.  Paving the Path to Automatic User Task Identification , 2008, CSKGOI.

[27]  Ulrike Cress,et al.  Learning in the Synergy of Multiple Disciplines, 4th European Conference on Technology Enhanced Learning, EC-TEL 2009, Nice, France, September 29 - October 2, 2009, Proceedings , 2009, EC-TEL.

[28]  Nuria Oliver,et al.  SWISH: semantic analysis of window titles and switching history , 2006, IUI '06.

[29]  Andreas Schmidt.  Impact of context-awareness on the architecture of learning support systems , 2007 .

[30]  Stefanie N. Lindstaedt,et al.  Getting to Know Your User - Unobtrusive User Model Maintenance within Work-Integrated Learning Environments , 2009, EC-TEL.