Professional search in an engineering context includes users on di erent stages from novice to expert. We discuss how searching as learning can help to understand searching in a larger information seeking, workspace, and learning environment, giving users the tools and understanding to become experts over time. We present some ndings of user interaction with engineering domain retrieval systems where the searching as learning perspective can improve our understanding of information seeking behaviour.
In a number of professional search settings the goal is not only to retrieve a particular isolated fact or document, but also to satisfy more complex information needs as part of a surrounding work task. In an inherently knowledgedriven work environment, searching for and working with documents is a common task that is considered especially challenging for new personnel in a team or company due to an often steep learning curve.
In our ongoing work, we examine the use of search,
recommendation, and knowledge management tools to support
engineers [
In a specialised professional search system that is a large
part of daily information seeking activities and work tasks,
users gain increasing knowledge and expertise over time by
interacting with the search system. We argue in this
paper that the information seeking tasks of engineers working
on a complex project can be understood from a searching
as learning perspective. This perspective can help
understand the users as it better ts their progress from novice
to expert, both in the short and long term, it may explain
certain changes of information seeking behaviour over time,
and also inform the adaptability of the system. Previous
work has looked at conceptual types of learning as searching
in the workplace [
Learning in this search scenario takes place at at least two levels. The simpler and more general one is that users learn how to use the system, especially learning how to formulate queries to better retrieve relevant documents, how to judge relevance of results, etc. This is basically learning how to search and already a well-explored area. Still, from a research perspective, we are also interested to see how judgements are made in this context.
On a more speci c case, users learn where to nd
documents, which type or source of document is more useful
for a certain task, and also learn the relevant documents
and processes in their work environment, the unwritten (and
written) rules of the company or the team. Undoubtedly, a
learning e ect occurs when retrieved documents are studied.
Users internalize gained information and knowledge,
changing the information needs they will have from the search
system in the future. This last part is the most interesting
from the searching as learning angle. If we take the de
nitions of learning as a change in the knowledge structures of
a user [
The basis for the discussions in this paper are a number
of informal interviews and discussions with engineers from
a large engineering company that builds large-scale o shore
structures [
Engineering large complex products or projects is
inherently a collaborative exercise. In the student example,
learning is of course the main desired outcome. Students share
knowledge directly, but also search and read documents from
their own and previous groups as well as general
documentation to advance their knowledge. In the company example
with a large workplace organization, the same happens, and
novices are also guided by expert engineers. However, an
additional task there is expert search, in which users search
for experts in the system to know whom to ask for particular
questions. Information needs also change depending on the
work and project context [
Figure 1 shows a simpli ed view of an engineer's work
situation, work tasks, resulting information needs, and possible
engagement with a retrieval system from the view of
information seeking [
One important thing to note is that there is a lot of interaction with documents, external Web sources, discussions with colleagues or project veri cation and reviews that takes place outside the context of the search system and may be invisible from that system. From a learning perspective, information needs even for the exactly same work task may change over time. Novices need to nd their way around the system, nd out where documents are stored and how they are organised, and need to familiarise themselves with the relevant standards and processes while experts have learned a lot of these already. On the other hand, there are certain standards and protocols that need always be followed in the latest version for speci c parameters or processes. However, this may still mean that users have bookmarked the latest source and need not search for it or that they have a physical printout in the o ce for reference.
Understanding information seeking tasks with the searching as learning view helps understand some of our initial ndings in a new light. For example, an initial idea that by observing the interaction of experts with a retrieval system, certain document characteristics and relations as well as interesting work ow aspects could be mined. This proved extremely hard, as one of the ndings of our interviews was that for many tasks, expert searchers use less searching inside the system. In short, experts use retrieval systems a lot less and with a di erent focus due to their experience and expertise. This means that they may not need to use search to nd the relevant information or that they do know much more facts and processes and thus do not have any information need at all. Thus simply observing an expert to train or support novices turned out to be not feasible.
However, another nding is that engineering experts search
for answers that support more complex or unfamiliar tasks.
Because search tasks often consist of multiple linked searches
for di erent snippets of information, this is inherently
difcult to model. These ndings are consistent with results
from a study of another aspect of engineering support [
We see some angles to explore in future work. It would
be interesting to more systematically map previous work [