1. INTRODUCTION
Searching the web is a daily activity for people from a
variety of backgrounds and skill sets [
8
] and is used for
learning and discovery. Within this text learning is conceived as
processing of information and as construction of knowledge,
which adheres to cognitive and constructivist approaches
[
12
]. Learning is always based on prior knowledge and is
therefore di erent for people depending on their experience,
context and abilities [
7, 12, 14
]. This is summarised by [
7
],
as a personal information infrastructure, which provides the
basis for processing information in order to construct new
knowledge.
Search as Learning (SAL), July 21, 2016, Pisa, Italy
The copyright for this paper remains with its authors. Copying permitted
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Searching and learning are intrinsically linked.
Learning has been conceptualized as the interactive intention of
searching by [
10
]. Learning can be the explicit aim of a
search - often involving several search sessions and results
that need to be interpreted and evaluated; or a byproduct
of search rather than a speci ed goal [
8
]. This is especially
typical for exploratory search tasks where sensemaking and
learning are inherent to the task [
8
]. This paper focuses
on how exploratory search for data can present di erent
issues than exploratory search for web pages. Data in this
case refers to structured, mostly numerical or factual data,
available on the web to download.
The remainder of this paper is structured as follows.
Section 2 introduces searching for data on the web as a distinct
activity opposed to searching for web pages. The
importance of context in enabling understanding when searching
for data on the web is discussed in section 3.
2.
DATA SEARCH
The majority of research about search as learning focuses
on traditional web pages, which can also contain data,
opposed to looking at data on the web as an independent source
[
6, 12
]. Data published on the web is used alongside the
content on traditional web pages to enable decision making
about complex situations [
1
].
Techniques to support the task of searching for data are
less advanced than those for searching for web pages. Web
search engines are based on algorithms which are designed
to rank web pages and do not equally support the indexing
of structured content [
9
]. Additionally, users are likely to be
not as familiar with the process of searching for data, as
different skills might be needed for a successful search activity.
[
1
] provide a hierarchy of levels of information that has data
at the bottom - which is de ned as raw facts; when context
is added to the data it is de ned as being information; and
when this information is integrated it is considered to be
knowledge, which means an understanding of the situation.
In that sense data can be seen as the raw source, but the
construction of knowledge requires an additional process.
As stated in section 1, a person's personal information
infrastructure at the time of the search task determines the
ability of building relationships between information sources
[
1, 8
]. The ability to transform data to information is
dependent on the context provided by the system as well as
on data literacy skills of the individual. A learning process
might be harder to predict or evaluate for data search
opposed to search for web pages.
Web pages often o er textual information and provide
therefore curated and processed data, or information, that
comes with context. Furthermore search engines are very
advanced in providing additional context - they can provide
contextual and personalised results by combining explicit
queries with implicit feedback, such as e.g. integrating the
user's browsing behaviour into a ranking system [
11, 13
].
Context is a necessary source of meaning [
4
], and there is
added complexity of context within data search due to the
additional information required to create meaning from data
opposed to from text documents. This additional
information can partly be provided through information about the
data - metadata. Learning can be enhanced by providing
reference points with the data or in the presentation of data
- to enable the user to build a web of relationships between
the di erent bits of information, which is needed to
understand complex information [
1
]. For example, sensemaking
of geographical data is easier when displayed in a map, and
meaning can be attached to numbers if a range or a graph is
presented that supports relating those numbers to reference
points.
[
10
] describe the sensemaking process as creating
knowledge structures between the data or information that has
been acquired through the information seeking task.
Decisions about the amount of context provided with the data
are made by data publishers or by those designing the
system; interface design plays a key role in representing the
context [
5
].
The presentation of data in uences sensemaking [
14
].
Interfaces should enable discovery of connections between
different data points, that represent data in a network to make
a user understand its meaning within the context of other
data. An overview of search results can enhance orientation
and understanding of the information provided, which can
enable learning activities [
10
]. For data search, learning can
be supported by allowing to zoom in and out of levels of
data, allowing ltering and cross ltering [
10
], rather than
displaying one piece of content at a time, such as is done
with a list of documents. Navigational structures can
support the cognitive representation of information [
10
] and this
is even more important when searching for data on the web,
to facilitate the transition of data to information and
subsequently to knowledge. Publishing structured data as Linked
Data can be seen as a partial realisation of this idea, as it
provides a basis for interlinking data by providing context
[
2
], however the majority of data on the web is not published
as Linked Data.
4.
CONCLUSIONS
Current search engines are optimised for searching and
learning factual knowledge from web pages [
10
], but are not
yet fully facilitating searching for data on the web or
providing the means to understand, analyse and synthesise this
data. Document search di ers to data search as nding,
accessing, understanding and using data requires additional
skills. The user's prior knowledge and experience with the
domain or topic determine the ability to understand the
data. Skills such as accessing, interpreting and critically
assessing data are part of a user's data literacy [
3
]. Data
usually requires additional context to be interpreted, as
discussed in section 3. Hence potentially more complex search
interfaces are required, that o er di erent viewpoints to
facilitate learning during the search process.
Further research is needed to understand how people make
use of data resources and progress from nding to
understanding [
8
]; which can be de ned as learning. The
challenges of searching for data should be an area of attention
in its own right, rather than extrapolating results from
traditional document search. The better the sensemaking process
from data to knowledge is understood, the better systems we
can create to facilitate learning from and by data search.