Author manuscript, published in "Proc. EC-TEL '10, Barcelona: Espagne (2010)" Studying the Factors Influencing Automatic User Task Detection on the Computer Desktop (2013)
BibTeX
@MISC{Rath13authormanuscript,,
author = {Andreas S. Rath and Didier Devaurs and Stefanie N. Lindstaedt},
title = {Author manuscript, published in "Proc. EC-TEL '10, Barcelona: Espagne (2010)" Studying the Factors Influencing Automatic User Task Detection on the Computer Desktop},
year = {2013}
}
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Abstract
Abstract. 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 Naïve 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
Keyphrases
computer desktop author manuscript learning activity user task user personal task knowledge worker recorded task execution work-integrated learning work support ontology-based user task detection approach j48 decision tree task detection performance key challenge standardized task task performance bayes classifier good classification accuracy knowledge-intensive task routine task laboratory experiment
