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Web search behavior of Internet experts and newbies

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Searching for relevant information on the World Wide Web is often a laborious and frustrating task for casual and experienced users. To help improve searching on the Web based on a better understanding of user characteristics, we investigate what types of knowledge are relevant for Web-based information seeking, and which knowledge structures and strategies are involved. Two experimental studies are presented, which address these questions from different angles and with different methodologies. In the first experiment, 12 established Internet experts are first interviewed about search strategies and then perform a series of realistic search tasks on the World Wide Web. From this study a model of information seeking on the World Wide Web is derived and then tested in a second study. In the second experiment two types of potentially relevant types of knowledge are compared directly. Effects of Web experience and domain-specific background knowledge are investigated with a series of search tasks in an economics-related domain (introduction of the Euro currency). We find differential and combined effects of both Web experience and domain knowledge: while successful search performance requires the combination of the two types of expertise, specific strategies directly related to Web experience or domain knowledge can be identified.
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Web Search Behavior of Internet Experts and Newbies
Christoph Hölscher & Gerhard Strube
Center for Cognitive Science, Institute for Computer Science & Social Research,
University of Freiburg, Germany
Email: hoelsch@cognition.iig.uni-freiburg.de, strube@cognition.iig.uni-freiburg.de
Abstract:
Searching for relevant information on the World Wide Web is often a laborious and frustrating task for
casual and experienced users. To help improve searching on the Web based on a better
understanding of user characteristics, we investigate what types of knowledge are relevant for Web-
based information seeking, and which knowledge structures and strategies are involved. Two
experimental studies are presented, which address these questions from different angles and with
different methodologies. In the first experiment 12 established Internet experts are first interviewed
about search strategies and then perform a series of realistic search tasks on the WWW. From this
study a model of information seeking on the WWW is derived and then tested in a second study. In the
second experiment two types of potentially relevant types of knowledge are compared directly. Effects
of Web experience and domain-specific background knowledge are investigated with a series of
search tasks in an economics-related domain (introduction of the EURO currency). We find differential
and combined effects of both Web experience and domain knowledge: While successful search
performance requires the combination of the two types of expertise, specific strategies directly related
to Web experience or domain knowledge can be identified.
Keywords: Expertise, Information Retrieval, Internet Search Engines, Logfile Analysis
1. Introduction
The accelerated growth of the World Wide Web has turned the Internet into an immense information
space with diverse and often poorly organized content. Online users are confronted with rapidly
increasing amounts of information as epitomized by the buzzword "information overload." While skills
necessary for browsing individual websites seem to be available to users after only minimal training
(Hurtienne and Wandtke, 1997), considerably more experience is required for query-based searching
(Pollock and Hockley, 1997) and intersite navigation.
The underlying question of the research presented in this paper is, what types of knowledge are
relevant for Web-based information seeking, and which knowledge structures and strategies are
involved. Two experimental studies are presented, which address these questions from different
angles and with different methodologies.
Search engines such as Altavista or Excite are a central part of information seeking on the Internet.
Their efficient use requires sophisticated knowledge. Since experienced users make use of search
engines regularly for diverse information needs, i.e., using them quite often, it is reasonable to assume
that they will develope particular expert knowledge in mastering these more complex services. Thus
the research presented here is focused on interactions with search engines and related services. In
addition, query-based searching allows for comparisons with research on search behavior of end-
users in traditional IR systems.
Investigations on the search behavior of both expert and novice Web users have several practical
applications. First and foremost, a model of search behavior can serve as the basis for improving
interfaces and functionality of existing search systems. The varied needs of experts and novices can
be identified and considered by more sophisticated future systems. Also, help-systems and Internet
education (e.g., courses and tutorials) can also benefit from a better understanding of users' difficulties
with the search process.
1.1 Related Research on Web Search:
The first influential studies on Web user behavior mainly investigated aspects of Browsing when
navigating the WWW (Cockburn & Jones, 1996; Catledge & Pitkow, 1995; Tauscher & Greenberg,
1997). Byrne, John and Crow (1999) have recently proposed a "taxonomy of WWW user tasks" that
span a user's complete range of behaviors while surfing the Web, but does not have a focus on
information seeking or Web search.
Choo, Detlor and Turnbull (1999) have investigated the information seeking behavior of knowledge
workers over a period of two weeks. Combining surveys, interviews and client-side logging they were
able to characterize a number of information seeking behaviors of Web users that are summarized in a
model of behavioral modes and moves.
Navarro-Prieto, Scaife and Rogers (1999) identified cognitive strategies related to Web searching.
They compared Web searchers with high and low experience and concluded that expert searchers
plan ahead in their searching behavior based on their knowledge about the Web, while novice
searchers hardly plan at all and are rather driven by external representations (what they see on the
screen).
Several researchers (e.g., Jansen et al., 1998; Silverstein et al., 1998) have collected impressively
large datasets derived from the logs of Internet search engines like Excite or AltaVista. Their studies
give a detailed picture of how the average Web user approaches a search service, but they also have
drawbacks: Since the data is anonymous, we do not know anything about the context of
the individual user, that is, we do not know what information problem he or she was trying to find or
how experienced a user is with respect to the Internet in general or searching in particular. In the
present study we use aggregated data from a large German search service to complement data
collected from individual users.
In the User Modeling community the behavior of Web users has also attracted some attention. Lau
and Horvitz (1999), for example, constructed Bayesian networks to model the successive search
queries issued by users of a search engine. Augmenting the search engine logfile with manually
assigned categories of presumed information goals they are able to predict query modifications.
Similarly, Zukerman, Albrecht and Nicholson (1999) propose the use of Markov models to predict a
Web user's next request based on the timing and location of past requests. Again, these studies do
not address personal characteristics of the user and his level of expertise.
2. Experiment 1: Exploratory Investigation of Expert Knowledge and Search Behavior
The behavior of experienced Internet users and their specific knowledge has not been systematically
investigated. Thus the first study is exploratory, aiming at a detailed description of Web expertise,
describing typical search behavior of Web experts and constructing a descriptive model of information
seeking with search engines. Comparable models for searching in electronic information systems were
proposed by Marchionini, Dwiggins, Katz and Lin (1993) and Shneiderman, Byrd and Croft (1997), but
did not consider, for example, the specific differences between the World Wide Web and bibliographic
database systems.
We define Web expertise as a type of media competence, i.e., the knowledge and skills necessary
to utilize the WWW and other Internet resources successfully to solve information problems. This has
to be clearly distinguished from background-knowledge related to the topic area of a specific Web
search (see Experiment 2).
Well established Internet professionals were recruited for this study. All had at least 3 years of
intensive experience with this medium and a daily use of the Internet as a source of information at
their workplace. Among the 12 participants each of them participated in both parts of the expert
study were information brokers, Web masters, Internet consultants, Web content designers,
librarians and authors of books about online searching. It is noteworthy that most participants had not
received formal training in Internet use, they are cleary to be characterized as self-trained experts.
2.1 Phase I: Interviews.
First the participants were asked to describe their experience with the available search services, their
search behavior and their intentions and rationales for using certain sources and strategies. With the
help of mental walk-throughs the process of searching for online information was then discussed step
by step. To reveal those experts' conceptual structures, the interview was augmented with a
specialised card-sorting task (Janetzko, 1998; Strube, Janetzko & Knauff, 1996): During the interview,
relevant terminology and actions were made explicit by recording them on colour-coded cards.
Afterwards, the experts were asked to build a graphic structure with these cards. This structure is
supposed to represent an expert's personal conceptualisation of the search process. To support the
participants in this task, some appropriate concept categories and relations were predefined and
presented to the experts.
2.2 Phase II: Experiment using web-based information tasks
Web-based information-seeking tasks. In the second phase of this expert study, a number of real-life
information-seeking tasks were employed that had to be performed by the experts on the Internet.
Examples: 'Which finger is unaffected in RSI?' or 'Find a sound archive for the VIRUS music
synthesiser'. The experts were not limited in their choices for searching the Web and could freely
choose which search engines - if any - they wanted to use.
All inputs to the computer were mediated through an assistant of the experimenter who had to be
orally instructed by the expert for each action. This procedure forced the expert to make every step of
the interaction process verbally explicit, including those that might otherwise be missed because of
rapid interaction sequences. Additionally, the experts were asked to think aloud about their search
activities. The method can be categorized as being between a classic thinking-aloud and a teaching-
aloud scenario (Ericsson and Simon, 1993). All utterances were audio-taped and later transcribed for
the analysis. Web-page requests and search queries were also included in the protocol.
2.3 Results of the expert study
Interviews
The experts reported a wealth of Internet-related knowledge, most of it highly idiosyncratic. Therefore,
their statements relating to the search process were collected from the transcripts and entered into a
matrix to determine which concepts, heuristics and strategies were common to the majority of the
experts. Likewise, the concept-card models were inspected for interindividually common knowledge
structures. The statement matrix and the card models were aggregated into an initial process model of
information seeking with search engines. This model describes the search process from the experts'
shared perspective.
Web-based information-seeking tasks
We distinguish two levels of data analysis, the level of information seeking steps, and the level of
individual search queries. For the analysis of information seeking steps, a set of rules was derived
from the experts' process model for segmentation and categorization of the protocol into action units.
A total of 56 information problems was tackled by the subjects, two thirds of these successfully. A total
of 1956 action units were identified, each corresponding to a step in the process model. The matrix of
transition probabilities between all steps of the model was computed, allowing for an analysis of
interaction sequences. The main results are summarized below.
Figure 1 shows the experts information seeking behavior on a global level of browsing and searching.
In two thirds of the search tasks, the experts initially choose to use a search engine. Only in one third
of the cases did they opt for browsing as the initial strategy. Finding potentially relevant documents
with a search engine led to browsing episodes of varying length in about 47 percent of the cases.
Once the searchers were in "browsing mode" they continued browsing for several clicks, hence the .73
probability of one browsing move leading to the next. Such browsing episodes could lead directly to a
solution, but often enough, a return to the search engine for further queries was observed. This
indicates that the experts in our study quite frequently switched back and forth between browsing and
querying if necessary.
Figure 1: Global level of the process model of information seeking in Experiment 1: Browsing
vs. Searching. (values represent transition probabilities to the next unit. The transition
probabilities going out from a given step of the model add up to 100%, but transition
probabilities of .03 and below are omitted here to reduce visual clutter)
Figure 2: Close-up of direct interaction with a search engine. (values represent transition
probabilities to the next unit. Transition probabilities of .03 and below are omitted to reduce
visual clutter)
Figure 2 shows a close-up of actions directly involved in search-engine interaction. The straight
downward arrows represent the default handling of the search engine with correspondingly high
transitions probabilities. Additionally, the experts showed more complex behavior if no relevant
documents were found, including reformulations or reformatting of existing queries, changing search
engines, requesting additional result pages as well as backtracking to earlier result pages or queries.
Again we observe opportunistic behaviour making use of all the options a search engine provides.
Flexible use of availlable search behaviors is a characteristic feature of expert searchers.
Individual search queries were analysed as well, and compared to available data on user behavior.
Jansen et al. (1998) report a quantitative analysis of a large sample of search requests from the
EXCITE search engine, representing the search queries of the average Internet user. Similar data has
been reported by Silverstein et al (1998) for the AltaVista search service.
A corresponding sample from German search-engine users was made available to us by the
managers of the FIREBALL search engine, representing some 16 million queries and 27 million non-
unique terms. A comparison of data sets from average users with the experts' queries in our study
revealed several differences: First of all, the average length of a query in FIREBALL is only 1.66
words, while the experts used an average of 3.64 words, twice as many.
Table 1:Usage of query formating in experiment 1 (the Expert study) and aggregated
statistics from the Fireball search engine.
We also found that web experts make use of advanced search options like Boolean operators,
modifiers, phrase search etc., much more frequently than the average user (see Table 1). A
noteworthy exception is the "+" operator. It is equally popular among the general public, making it the
most important query formatting tool for non-expert users.
This first expert study confirmes the significance of media-specific skills of Internet users, and gives
a detailed picture of Internet expertise. While IR skills were the focus of this study we found numerous
hints of the importance of content specific knowledge. Experts frequently complained about lacking
relevant domain knowledge regarding individual search questions and were highly aware of this
obstacle while being confident of their technical competence.
3. Experiment 2: The EURO study
Several authors, for example Hsieh-Yee (1993), were able to show that technical competencies in
using bibliographic database systems are necessary for successful information retrieval, but that such
knowledge has to be combined with background knowledge about the topic area to be searched. This
finding is in line with observations from the verbal protocols obtained in our expert study, where Web
experts complained that they lacked domain-specific background knowledge for particular search
tasks. The following experiment addressed these two types of knowledge that contribute to the
success of searching on the Web, and how the two interact.
Experiment 2 is designed to compare directly the contributions that technical Internet skills and
content-area specific domain knowledge make to the search process. A current topic from the domain
of economics - the European Monetary Union - was chosen for this laboratory experiment. The
subjects were given a set of information-search problems from this domain. A 2 x 2 design of the
independent factors Web expertise anddomain knowledge results in four experimental groups.
Participants with domain knowledge were recruited from students of economics. Web expertise was
assessed by interview and pre-test, allowing us to clearly identify novices and advanced web users,
thereby excluding intermediate level web users from data analysis.
In the experiment, two kinds of tasks were used, simulated search tasks and tasks that had to be
performed live on the Web.
3.1 Simulated Search tasks
Based on the process model developed in the expert study above, complex search tasks were broken
down into sub-tasks corresponding to individual steps of the process, such as search term selection or
query revision. The resulting sub-tasks allowed for a focussed investigation of the direct effects that
different types of expertise have on individual steps of the model.These simulated tasks were collated
in a questionnaire. The approach made sure that each participant worked on the same stimuli (words,
queries, result pages), allowing for comparisons that are not readily available from observing
unrestricted task performance on the Web. In "real" searches on the Web participants follow different
paths trying to solve given tasks and hardly ever face exactly the same pages of results or have to
reformulate the exact same search queries as another participant.
3.2 Web-based Search tasks
In the second part of the experiment, the actual Web searches, we tried to impose as few restrictions
as possible, and did not employ thinking aloud techniques. Participants were asked to solve five
information problems directly via the WWW. The only restriction imposed on the participants was a
time limit of 10 minutes per task. All interactions were recorded by a proxy server (Siemens
WebWasher) and a traditional observer protocol to complement the proxy log. Again, subjects could
freely choose how to tackle the search tasks and which search engines to consult.
While in experiment 1 interaction sequences and search statements were reconstructed from the
audio protocol of the thinking aloud tasks, in experiment 2 the same measures are recorded directly
with the proxy-server installed on the client computer. The proxy logfile contains most of the necessary
information like the date and time of each access, the Uniform Resource Locator (URL) of each file
viewed and its length. Additionally we have the HTTP result code (indicating, e.g., if the file to be
accessed was physically unavailable) and for most cases also the Referrer URL that indicates from
which URL a users requests another page and is an important tool for reconstructing the behavioral
trace. The logfile data can be processed to reveal the majority of the users interaction. Nonetheless a
traditional observer protocol was written during the experiment to complement the logfile, because
certain interactions are not adequately recorded in the proxy logfile, mainly concerning navigation in
FRAME-Sets, use of the Back-Button in the Browser and queries submitted via the POST method. For
the analysis, the proxy log and the observer protocol were combined for categorizing the user actions
in terms of the process model developed above.
Browsing and searching behaviors which manifest in the interaction sequences during the search
are compared to identify differences in information seeking strategies and tactics.
3.3 Results of the double comparison of advanced and novice searchers
The data presented below is based on a sample of 24 participants, 6 from each cell of the 2 x 2 design
of Web expertise (high/low) and domain knowledge (high/low). We analyzed four types of data: rate of
success, action sequences (expressed as transition probabilities), time data and formal properties of
search queries.
Rate of success
The web-based search tasks proved to be rather difficult for all participants, resulting in low overall
success rates. In three of the experimental groups, participants solved no more than 2 of 5 tasks on
average. Only those users who could rely both on high Web expertise and high domain knowledge
("double experts") were able to solve an average of 3.2 out of the 5 tasks.
Action sequences
Across all experimental groups the pattern of action sequences is comparable to the data from the
expert study.
Figure 3: Global level of the process model of information seeking: all four groups of the EURO
study combined. (transition probabilities)
Figure 4: Close-up of direct interaction with a search engine: all four groups (novices
& experts) of the EURO study combined. (transition probabilities)
One important difference is the fact that participants now obviously found less useful pages and had to
reiterate their searches more frequently to find relevant information (see Fig. 3 and 4 for details). This
increased difficulty most likely reflects both differences in the tasks (harder) and the participants
(overall lower levels of expertise, since 50% were novices) of the two studies. Please note that the
coding scheme was slightly revised from the expert study to the EURO study. This accounts for
differences between the studies at the process stages "Access web site directly" (Fig. 3) and "Select +
Launch Search Engine" (Fig. 4).
Differences between experts and novices
Figure 5: Initial behavior - the first action performed after receiving a task. (Web +/-
refers to Web expertise, Econo +/- refers to domain knowledge)
Looking at the actions subjects choose as their initial behavior (Fig.5) we find several important
differences between groups. Only "double experts" initially tried to access directly web-sites related to
economics, while all others immediately accessed a search engine in one way or the other. Web
experts would type in the URL of their favorite search engine, while the "double novices" were highly
inclined to simply click on the Netscape Search button (these effects - and all others discussed below -
prove to be significant in HILOGLINEAR analysis, unless stated otherwise).
Figure 6:Actions selected on a search engine result page. (Web +/- refers to Web
expertise, Econo +/- refers to domain knowledge)
Once a Web search has led to a page of results (Fig. 6), Web experts were significantly more likely to
choose a target document for closer inspection than Web novices (35% vs. 25%), while Web novices
more often reiterate their search queries. We also found significant interactions of domain knowledge
and Web expertise: When Web experts had little domain knowledge, they were most likely to pick a
target document (possibly for lack of clear selection criteria). Double novices showed the highest
proportion of query re-formulations while choosing the smallest number of target documents for closer
examination and of these documents the highest proportion turned out to be irrelevant. A qualitative
inspection of the query re-formulations that were issued by the double novices indicated that they
often make only small and ineffective changes to their queries, forcing them to reiterate repeatedly.
Figure 7:Transitions while Browsing. (Web +/- refers to Web expertise, Econo +/- refers
to domain knowledge)
Looking at browsing behavior, we can once again identify some clear patterns. Figure 7 shows what
the participants choose to do next once they have accessed a document in a browsing episode. The
behavior of the double experts with technical and domain-specific knowledge can be characterised like
this: They are most likely to continue browsing (follow another link) to explore more content from a
Web site or to change their strategy and use a different search engine. They are the group least likely
to engage in backward-oriented behavior like clicking the backbutton to browse back or return to
previous search engine result. Such backward-oriented behavior is very common for the less
experienced users, with double novices showing it most often. It is not fully clear, if novices browse
less usefull material than the experts, but once they face a dead end their only way out is to go
backwards, while experts have more flexible ways fo reacting.
Time data
From the proxy logfile one can reconstruct how much time has elapsed between page transmissions.
These intervals cannot be translated into steps of our search model equally well for all steps. For
example, when a user refines an initial query, the corresponding interval between page loadings
contains three steps: reviewing the initial result set, generating terms, and formatting the revised
query, and submission of that query. This makes an analysis of time spent in direct search engine
interaction difficult and statistical results for these measures are not so clear.
This problem is far less pronounced for the timing of content pages. Consequently, the stronger
differences could be established for the time users spent with content pages (Figure 8).
For the time spent with a document that was directly selected from a page of search engine results,
we find a clear independent main effect of domain knowledge (MANOVA: F=11.44, p<.003). People
with considerable background knowledge about the domain spent significantly less time with a
document from that domain. It takes them less time to read it and make a decision about the next
move. Descriptively, Web expertise also reduces the time spent in content documents, but this effect
was not found to be significant.
Figure 8:Time spent with Web pages. (Web +/- refers to Web expertise, Econo +/- refers to
domain knowledge)
No significant differences between the groups were found for pages accessed during browsing
episodes. Quite likely this can be attributed to the nature of pages included in this category. The
category not only includes content pages relevant to the domain of economics, but also a number of
function pages like navigation pages (which lead to content pages, but do not contain longer
paragraphs of task-related information) and even search engine help pages. This may have
diminished the influence of domain knowledge on reading times for these kinds of documents.
Descriptively we again find shorter reading times for Web experts, but no significant effect. Thus the
influence of Web expertise on the time measure is rather weak, but less dependent on whether or not
a topic-related page is accessed.
Query properties
We found the same general pattern of query formulations for both the web-based search and the
simulated search tasks, with the data from the search simulations being somewhat more clear-cut
(Table 2).
Table 2: Query formatting in Simulated Search tasks (percent of all queries).
Web experts relied significantly more on query formatting tools than Web novices (87 % vs. 47 %),
while higher domain knowledge corresponded to a lower number of Boolean operators and modifiers
being used. A very clear effect of Web expertise was found for the number of queries with formatting
errors (19.6% vs. 1.9%).
Effects of the experimental conditions could also be established for the number of search terms per
query, and the sources of search terms, but only in the searches actually performed on the Web, not in
the simulated search.
From the expert study one would have expected Web experts to use longer queries. This
hypothesis was not confirmed: the queries issued by Web experts were only marginally longer than
those of Web novices (2.61 vs. 2.32 words/query). Instead we found a significant effect of domain
knowledge: Participants with little domain knowledge made significantly longer queries (average query
length: 2.96 vs 1.97 words). Maybe domain experts know more appropriate terms and hence need
fewer of them.
The analysis of query formatting (Table 2) revealed that participants who know a lot about the
subject domain, but lack Web expertise are quite reluctant to use query formatting (see above). But
they seem to compensate for this by showing more verbal creativity and flexibility than the other
groups: They most likely used their own terminology instead of relying on the words that were already
in the original task statement. Also they more often than others used completely different terminology
from one query to the next.
4. Discussion
In the Expert study we investigated how Internet professional conceptualize the search process and
derived aprocess model of search engine interaction. This model was first applied to the search
behavior of the same Internet professionals and we believe that it has shown its value as a tool for
capturing expert searching behavior.
In the second study, the EURO study, we focus on a direct comparison of expert and novice web
searchers. It turns out that the process model can be applied to the behavior of both expert and novice
searchers and that it also captures differences between these groups.
Expertise was further differentiated into technical Web expertise and domain-specific background
knowledge, in this case the field of economics. The two types of expertise have shown independent
and combined effects. Participants which could rely on both types of expertise were overall most
successful in their search behavior. Deficits in one or the other type of expertise led to compensatory
behavior, for example, domain-expert/web-novices relying heavily on terminology and avoiding query
formatting. Participants with lower levels of knowledge are less flexible in their strategies and return to
previous stages of their search more often rather than trying new approaches (like changing the
search engine).
The severe troubles that the "double novices" faced when dealing with the tasks in the EURO study
again point at the joint contribution that both domain-knowledge and Internet expertise make to the
search process.
Overall Web-based information seeking turned out to be rather difficult for the participants of both
Experiment 1 and Experiment 2. This indicates that there still is much room for improvement in Web-
based searching. The behavioral differences we found between the experimental groups clearly show
that search engine users are a heterogenous crowd and may need to be catered to differently. Novice
users had severe problems with formulating a reasonable query and tools that support the query
formulation process would seem desirable.
Because successful search on the Web turns out to be so difficult for novice users, learning how to
use search engines efficiently should be a central part of any Internet skills training. Novices in our
study were ignorant about a number of core problems of Web searching, e.g. the limited scope of
individual search engines or the necessity to state a search query at an adequate level of specificity.
The differences found between the Web novices and the Web experts point at specific deficiencies in
the novices' knowledge and could be directly addressed in Internet skills training.
Acknowledgements
The first author is a Ph.D. student in the Virtual PhD Program (VGK: Knowledge Acquisition and
Knowledge Exchange with New Media - see http://www.vgk.de for details) and the work was funded by
the German Research Council (DFG). We would like to thank Gruner+Jahr EMS, Hamburg for
supporting the expert study and giving us access to the Fireball statistics reported in this paper.
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... Οι έρευνες για τη μελέτη της αναζήτησης της πληροφορίας στο Διαδίκτυο αφορούν κύρια (α) στη μελέτη διαφόρων χαρακτηριστικών της διαδικασίας της αναζήτησης της πληροφορίας (Jenkins et al., 2003;Walraven et al., 2008Walraven et al., , 2009Holscher & Strube, 2000;Liaw et al. 2006) και (β) στην ανάπτυξη 5ο Πανελλήνιο Συνέδριο Διδακτική της Πληροφορικής _________________________________________________________________________________________ εργαλείων, μεθόδων και τεχνικών για την υποστήριξη περισσότερο σύνθετων ερευνών λόγω της πολυπλοκότητας της διαδικασίας της αναζήτησης της πληροφορίας στο Διαδίκτυο (Hwang et al., 2007). ...
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... It is known that the WWW is widely used as a source of information in today's world (Rodi et al., 2017). Due to the increasing amount of information in WWW, users struggle with information overload during online searches (Hölscher & Strube, 2000). Information search strategies affect not only the effort, time, and efficiency of the search process, but also learning performance (Ay & Erdem, 2020). ...
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Digital systems, such as phones, computers and PDAs, place continuous demands on our cognitive and perceptual systems. They offer information and interaction opportunities well above our processing abilities, and often interrupt our activity. Appropriate allocation of attention is one of the key factors determining the success of creative activities, learning, collaboration, and many other human pursuits. This book presents research related to human attention in digital environments. Original contributions by leading researchers cover the conceptual framework of research aimed at modelling and supporting human attentional processes, the theoretical and software tools currently available, and various application areas. The authors explore the idea that attention has a key role to play in the design of future technology and discuss how such technology may continue supporting human activity in environments where multiple devices compete for people's limited cognitive resources.
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Digital systems, such as phones, computers and PDAs, place continuous demands on our cognitive and perceptual systems. They offer information and interaction opportunities well above our processing abilities, and often interrupt our activity. Appropriate allocation of attention is one of the key factors determining the success of creative activities, learning, collaboration, and many other human pursuits. This book presents research related to human attention in digital environments. Original contributions by leading researchers cover the conceptual framework of research aimed at modelling and supporting human attentional processes, the theoretical and software tools currently available, and various application areas. The authors explore the idea that attention has a key role to play in the design of future technology and discuss how such technology may continue supporting human activity in environments where multiple devices compete for people's limited cognitive resources.
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Digital systems, such as phones, computers and PDAs, place continuous demands on our cognitive and perceptual systems. They offer information and interaction opportunities well above our processing abilities, and often interrupt our activity. Appropriate allocation of attention is one of the key factors determining the success of creative activities, learning, collaboration, and many other human pursuits. This book presents research related to human attention in digital environments. Original contributions by leading researchers cover the conceptual framework of research aimed at modelling and supporting human attentional processes, the theoretical and software tools currently available, and various application areas. The authors explore the idea that attention has a key role to play in the design of future technology and discuss how such technology may continue supporting human activity in environments where multiple devices compete for people's limited cognitive resources.
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