Automatic detection of accommodation steps as an indicator of knowledge maturing

Jointly working on shared digital artifacts – such as wikis – is a well-tried method of developing knowledge collectively within a group or organization. Our assumption is that such knowledge maturing is an accommodation process that can be measured by taking the writing process itself into account. This paper describes the development of a tool that detects accommodation automatically with the help of machine learning algorithms. We applied a software framework for task detection to the automatic identification of accommodation processes within a wiki. To set up the learning algorithms and test its performance, we conducted an empirical study, in which participants had to contribute to a wiki and, at the same time, identify their own tasks. Two domain experts evaluated the participants’ micro-tasks with regard to accommodation. We then applied an ontology-based task detection approach that identified accommodation with a rate of 79.12%. The potential use of our tool for measuring knowledge maturing online is discussed

Exploiting the user interaction context for automatic task detection

Detecting the task a user is performing on her computer desktop is important for providing her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classifiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction context model that can be automatically populated by (i) capturing simple user interaction events on the computer desktop and (ii) applying rule-based and information extraction mechanisms. We present evaluation results from a large user study we have carried out in a knowledge-intensive business environment, showing that our ontology-based approach provides new contextual features yielding good task detection performance. We also argue that good results can be achieved by training task classifiers `online' on user context data gathered in laboratory settings. Finally, we isolate a combination of contextual features that present a significantly better discriminative power than classical ones

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 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

Studying the factors influencing automatic user task detection on the computer desktop

International audienceSupporting 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

Computer detection of spatial visualization in a location-based task

An untapped area of productivity gains hinges on automatic detection of user cognitive characteristics. One such characteristic, spatial visualization ability, relates to users’ computer performance. In this dissertation, we describe a novel, behavior-based, spatial visualization detection technique. The technique does not depend on sensors or knowledge of the environment and can be adopted on generic computers. In a Census Bureau location-based address verification task, detection rates exceeded 80% and approached 90%