DOCTORAL ABSTRACT

Knowledge Work in Context:
User Centered Knowledge Worker Support
Maya Sappelli
TNO & Radboud University
The Netherlands
m.sappelli@cs.ru.nl

Abstract

There is an increase in stress during the job. This can lead to health issues such as burn-out for
employees. In the SWELL project (http://www.swell-project.net) we investigate intelligent ICT
solutions that can support knowledge workers to achieve a healthy way of living at work and athome.
One of the causes of stress at work is the problem of ‘information overload’. The availabilityof smartphones and tables with continuous internet access causes individuals to become
overwhelmed with information. It becomes more difficult to separate work and home, but also to findthe information you need to execute your work properly.
A possible solution to this problem is to make application more ‘context-aware’. This meansthat the application understands what a person is doing, such that it can provide the optimal support at
the optimal time. This is the underlying motivation for this thesis.
This thesis consists of three parts. In the first part we discuss the question “How can we
design, develop and evaluate context-aware methods that make knowledge work more effective andefficient?” To answer this question, we have observed knowledge workers during their activities andcollected data about their behavior. From these we can conclude that many intentions and goals of theknowledge worker are implicit, and cannot be recognized easily [1,4]. We can, however, get a feelingof the knowledge worker’s intentions by observing his interactions with the computer (i.e. key
presses and mouse activity). The challenge in this type of data is that it contains a significant amountof noise.
In the second part of the thesis we model the context that is necessary to support theknowledge worker in a context-aware manner. The context model is dynamic and driven by events inthe surroundings of the knowledge worker. The knowledge worker is in the center of the model, but
the context also influences the knowledge worker himself. The context can be observed with sensors
that are independent from the knowledge worker, but the interpretation of the context can only beachieved in combination with the knowledge worker.
In this part we also describe the Contextual Interactive Activation Model (CIA) that canrecognize the context of the knowledge worker automatically based on his or her interactions with thecomputer [2]. Based purely on data, without intervention by a user, it can already recognize the user’scontext. Enhanced with only a few example it can also identify the context, meaning that it can put alabel on it, for example project ‘Dissertation’.

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In the final part of the thesis we describe algorithms that support knowledge workers while
requiring little user input. One of the application is the categorization of e-mail messages, forexample based on whether they need to be replied to or not, or based on which project the message
belongs to. For this later categorization problem, we use an adapted version of CIA [2]. The
advantage of CIA is that it requires less training examples than existing methods. In anotherapplication we use CIA to find documents without the need for a query [5]. We describe therequirements for evaluation of these kind of recommendation systems and conclude that a multidimensional evaluation is important. Each method has its own benefits and issues and the best
method is dependent on the user’s priorities.
The main limitation of the thesis is that it is explorative in nature. Little data was available totest our methods and hypotheses; therefore, the conclusions cannot be generalized easily. Thisillustrates the need to collect more datasets that reflect the complexity of the knowledge worker, histasks and his context. Future research need to investigate the effect of using context-aware
applications on the knowledge worker’s well-being. One of the remaining question is which
supporting application to use at which time. In an ideal case we can combine these methods andapplication to support the knowledge worker in such a way that he or she is not required to put in a
lot of effort.

References[1] Maya Sappelli, Suzan Verberne, Saskia Koldijk, Wessel Kraaij(2014) Collecting a dataset ofinformation behaviour in context. In: Proceedings of the 4th Workshop on Context­awareness inRetrieval and Recommendation (CARR @ ECIR 2014)[2] Maya Sappelli, Suzan Verberne, Wessel Kraaij (2014) E­mail categorization using partially related
training examples. In: Proceedings of the 5th Information Interaction in Context Symposium (IIiX2014)[3] Maya Sappelli, Suzan Verberne, and Wessel Kraaij (2016) Adapting the interactive activation
model for context recognition and identification In: ACM Transactions on Interactive Intelligent
System, Vol 6, Issue 3[4] Maya Sappelli, Gabriella Pasi, Suzan Verberne, Maaike de Boer, and Wessel Kraaij (2016)
Assessing e­mail intent and tasks in e­mail messages in: Information Sciences, Vol 358­368, p1­
17 .
[5] Maya Sappelli, Suzan Verberne, Wessel Kraaij (2016) Evaluation of context­aware
recommendation systems for information re­finding.. Accepted for publication in: Journal of theAssociation for Information Science and Technology (JASIST) .

Supervisors: Prof. Dr. W. Kraaij & Dr. S. Verberne
Available at: http://repository.ubn.ru.nl/handle/2066/151710

ACM SIGIR Forum108

Vol. 50 No. 2 December 2016