Fostering Information Literacy in German Psychology Students
Using a Blended Learning Approach
Nikolas Leichner, Johannes Peter, Anne-Kathrin Mayer and Günter Krampen
Leibniz Institute for Psychology Information, Universitätsring 15, 54296 Trier, Germany
Keywords: Information Literacy, Blended Learning, Distance Education, Psychology, College Students.
Abstract: This paper reports about the experimental evaluation of a blended learning course for information literacy
tailored to the needs of undergraduate Psychology students. The course consists of three modules delivered
online and two classroom seminars; the syllabus includes scholarly information resources, ways to obtain
literature, and criteria to evaluate publications. For evaluation purposes, a multimethod approach was used:
The students completed an information literacy knowledge test and three standardized information search
tasks (ordered by ascending difficulty) before and after taking the course. A sample of N = 67 undergraduate
Psychology students (n = 37 experimental group, n = 30 waiting control group) participated in the course.
As it was expected, students’ knowledge test scores as well as performance in the search tasks improved
markedly during the course. Results are discussed with regard to the soundness of the evaluation criteria
used and to further development of the course.
1 INTRODUCTION
The term Information literacy is used to describe the
ability to realize when there is a need for infor-
mation, and the ability to identify, locate, and evalu-
ate additional information which is required to meet
this need (National Forum on Information Literacy
n. d.; American Library Association 1989). Against
the background that advances in information tech-
nology lead to a growing number of information
resources, information literacy can be considered a
“basic skills set” (Eisenberg 2008).
As information literacy is of importance in near-
ly all circumstances (e.g. Eisenberg 2008), a clear
definition of information literacy has to be provided
first of all. Hence, we limit our research to infor-
mation literacy in higher education, especially in
Psychology. Our definition of information literacy is
based on the ACRL Psychology information literacy
standards (Association of College and Research
Libraries 2010), because this framework includes
detailed performance indicators. This definition
includes four standards of information literacy:
1) Determining the nature and amount of infor-
mation needed: exemplary performance indicator:
“understands basic research methods and scholarly
communication patterns in psychology necessary to
select relevant resources”;
(2) Assessing information effectively and effi-
ciently: exemplary performance indicator: “selects
the most appropriate sources for accessing the need-
ed information”;
(3) Evaluating information and incorporating in-
formation into one’s knowledge system: exemplary
performance indicator: “compares new information
with prior knowledge to determine its value, contra-
dictions, or other unique characteristics”;
(4) Using the information effectively to accom-
plish a specific purpose; exemplary performance
indicator: “applies new and prior information to the
planning and creation of a particular project, paper,
or presentation”.
We decided to focus on standards one to three, as
our course was devoted primarily to the improve-
ment of information seeking skills. Skills related to
standard four are part of the curriculum of academic
writing courses at German universities; consequent-
ly, they should not be part of our course. With re-
gard to our curriculum, we expected the participants
inter alia to be (better) able to understand scholarly
communication patterns, distinguish between several
research methods (e.g. meta-analysis and empirical
study), to select the most appropriate resource for
their information need and to evaluate the literature
found.
There is indication that incoming students are not
353
Leichner N., Peter J., Mayer A. and Krampen G..
Fostering Information Literacy in German Psychology Students Using a Blended Learning Approach.
DOI: 10.5220/0004795103530359
In Proceedings of the 6th International Conference on Computer Supported Education (CSEDU-2014), pages 353-359
ISBN: 978-989-758-021-5
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
sufficiently information literate (Smith et al. 2013)
and that students do not become information literate
during the course of their studies (Warwick et al.
2009). To address this need, almost every university
library in Germany provides information literacy
courses that complement the academic writing
courses offered by university departments. However,
most of these courses are two-hour events covering
only a facet of academic information literacy (e.g.
the use of bibliographic databases). Due to limited
time, these courses offer few possibilities to practice
information seeking, or to ask questions and discuss
matters. As there is no standardized test to assess
information literacy for German speaking popula-
tions, in most cases, evaluation efforts are based on
feedback provided by participants. Finally, most
courses are not tailored to specific disciplines, or
fields (e.g. economics, social sciences). This is prob-
lematic, as scholarly communication patterns and
information resources differ between disciplines.
Course content should therefore be adapted to the
needs of Psychology students (e.g. Thaxton et al.
2004).
The aim of this is study is to create a blended
learning approach to teach information literacy to
undergraduate students in Psychology. At the start,
the course will exist alongside the courses offered by
university libraries. However; we hope that our
materials (at least some of them) will later on be
used by university libraries to offer courses tailored
to Psychology students. An important component of
our effort is carrying out an evaluation study based
on performance indicators using a control group.
The main reason for choosing a blended learning
approach was to give participants the chance to work
on the online materials adapted to their individual
schedule. This is particularly important as students
are often pressed for time. However, there should
also be traditional classroom teaching as online
learning alone seems to be fraught with higher drop-
out rates (Carr 2000). There is research indicating
that blended learning can reduce dropout rates
(López-Pérez et al. 2011) and is more effective than
traditional classroom teaching or online learning
(Clardy 2009).
2 OUTLINE OF THE COURSE
2.1 Content
The content of the course was determinated based on
the Psychology specific information literacy stand-
ards provided by the ACRL (Association of College
and Research Libraries 2010) and based on our own
considerations. The target group were undergraduate
students, so the content was mainly basic infor-
mation about
scholarly communication patterns in Psychology
and common publication types (e.g. empirical ar-
ticle, review article, edited books);
different information resources (inter alia biblio-
graphic databases, internet resources) and their
advantages and disadvantages;
appropriate use of these resources (e.g. under-
standing of the thesaurus and of Boolean opera-
tors);
inclusion of resources provided by related disci-
plines (e.g. PubMed, ERIC) in case the topic is
of interdisciplinary nature;
options for the acquisition of literature (e.g. use
of electronic journal subscriptions, the local li-
brary catalogue, or interlibrary loan);
criteria for the selecting publications beyond
their content, e.g. Journal Impact Factor.
2.2 Structure
As mentioned before, the course combined online
and traditional classroom teaching. In total, there
were three modules to be completed online and two
classroom seminars. We expected that completing
the online materials would take up to four hours;
both classroom seminars were designed to take 90
minutes each. The course was scheduled to be com-
pleted within two weeks, what seems a reasonable
workload for college students.
The concept of the course envisaged that most of
the knowledge should be imparted by the online
materials, while the main purpose of the classroom
seminars was to provide an opportunity to solve
information problems under the guidance of the
instructor and to ask questions. For this reason, par-
ticipants had to complete certain online materials
before attending the related classroom seminar.
To present an example, online modules 1 and 2
were related to the first classroom seminar. These
modules dealt mainly with scholarly communication
patterns, information sources (and their functions),
as well as the acquisition of literature. A central
element of the related first seminar was the task to
find scientific literature on the question how distrac-
tions impact car driving performance. At the begin-
ning of the seminar, the task was presented to the
participants. Then, the task was split into steps (de-
termining the information need, finding search
terms, conducting the search, selecting literature)
and participants worked on the steps either individu-
CSEDU2014-6thInternationalConferenceonComputerSupportedEducation
354
ally, or in small groups. The instructor was available
in case questions arose. After completing each step,
one of the participants (one group, respectively) had
to present his/her outcome to the other participants
and the outcome was discussed.
All online modules were provided via the e-
learning platform “Moodle”. Most of the content
was presented using short passages of text which
were enriched by illustrations or screenshots of the
relevant computer programs. This content was pro-
vided using lessons, or pages inside of Moodle. The
materials also included several videos. At the end of
every section of the course, short quizzes were pro-
vided, so that the participants could apply their
knowledge right after learning.
3 EMPIRICAL STUDY
3.1 Instruments
In most cases, information literacy is assessed using
knowledge tests consisting of multiple choice items
(e.g. Project SAILS 2013; Center for Assessment
and Research Studies 2013; Noe and Bishop 2005).
These tests have been shown to provide a reliable,
valid and economic way of measuring information
literacy. However, as information literacy is a com-
plex ability, it can be doubted whether a knowledge
test can assess information literacy comprehensive-
ly. For instance, appropriate information seeking
behavior is an elementary part of being information
literate (Timmers and Glas 2010) and the assessment
of information seeking behavior requires observation
(e.g. Julien and Barker 2009), or self-reports (e.g.
Timmers and Glas 2010).
Besides, several authors argue that competencies
should be assessed using real-life tasks instead of
knowledge tests (Shavelson 2010; McClelland
1973).
For these reasons, we decided to use a multi-
method approach consisting of two standardized
tests which were applied in a laboratory setting: a
knowledge test and information search tasks.
The knowledge test consisted of 35 multiple-
choice items and had previously been developed by
our research group. When developing the items, we
relied on Standards 1 to 3 of the aforementioned
information literacy definition (Association of Col-
lege and Research Libraries 2010). A sample item is:
Which differences exist between Internet search
engines (e.g. Google Scholar) and bibliographic
databases?
a. bibliographic databases usually have a thesau-
rus search
b. Boolean operators can only be used with biblio-
graphic databases
c. the order of items on the results page is not af-
fected by the number of clicks on each item
The test had been used in a previous study with a
sample of N = 184 participants who had completed
the test online. In this study, an acceptable internal
consistency of the test of Cronbach’s Alpha α = 0.49
was found. Furthermore, it was found that Master
level-students scored significantly higher than un-
dergraduate students in their first and second year.
These results can be considered an indication of the
validity of the test.
The information search tasks are based on a tax-
onomy of tasks from which instances of tasks can be
derived. When reviewing the literature on infor-
mation search tasks, we found that the existing tax-
onomies are of a descriptive nature and do not pro-
vide indications for the difficulty of a certain task
type (e.g. Kim 2009). Another problem with these
taxonomies was that they had been developed to
classify non-scholarly search tasks in electronic
resources. There are several differences between
academic and non-scholarly searches; the most im-
portant one might be the use of bibliographic data-
bases (e.g. PsycINFO) instead of internet search
engines (e.g. Google, Yahoo!).
For these reasons, we decided to develop a task
taxonomy specifically for academic information
search tasks in Psychology. The taxonomy provides
three types of information search tasks differing in
their difficulty. To be more precise, the tasks differ
in the abilities and competencies required to solve
the task. The taxonomy is designed in a way that
abilities required to solve tasks of the first type are
also required to solve tasks of type 2. However,
solving tasks of type 2 requires additional compe-
tencies, as do tasks of type 3. The taxonomy can be
used to develop several tasks of the same structure
and difficulty which can be used for assessing in-
formation literacy. For illustration purposes, a type 2
task (medium difficulty) is provided:
Are there meta-analyses published after 2005
investigating “risk factors” for the development
of a “Posttraumatic stress disorder”? If possi-
ble, indicate two publications.
To solve this task, the participant has to under-stand
the keyword search function in a bibliographic data-
base, and needs an understanding of Boolean opera-
tors and complex filter functions in bibliographic
databases to find publications using a certain meth-
FosteringInformationLiteracyinGermanPsychologyStudentsUsingaBlendedLearningApproach
355
odology (e.g. meta-analysis). Type 1 tasks are easier
as they do not require an understanding of Boolean
operators and complex filter functions. Type 3 tasks
are more difficult, as they additionally require the
participant to identify appropriate search terms be-
fore conducting the search. To score the tasks, ru-
brics for scoring the search task outcome (which
publications were found) and the procedure applied
by the students when completing the tasks were
created. In line with the rubric for scoring the out-
come, scores were awarded depending on how close
the publications found come to the requirements
mentioned in the task description (e.g. thematic
focus of the publication, publication date). As stated
in the rubric for scoring the procedure, scores were
awarded for working on the tasks in an efficient and
information literate way as defined by the infor-
mation literacy standards (Association of College
and Research Libraries 2010). For example, for a
type 2 task, the maximum number of procedure
scores was rewarded if the participant solved the
task using bibliographic databases, used Boolean
operators to combine two search terms and limited
the results using the corresponding functions of the
database.
3.2 Method
3.2.1 Participants
The sample consisted of N = 67 undergraduate Psy-
chology students who took the course. Out of these
students, 34 were first year students, while 33 were
second year students. The average age was 21.67
(SD = 2.38). Participants had agreed to additionally
participate in three data collection sessions for which
they were compensated. Participants were randomly
assigned to one of two groups; group 1 (experi-
mental group) consisted of n = 37 participants, group
2 (waiting control group) of n = 30.
3.2.2 Procedure
The duration of the evaluation study was four weeks,
while the actual course took only two weeks. Data
collection 1 took place right at the start of the evalu-
ation study. Subsequently, group 1 participated in
the course, while group 2 served as a waiting control
group. Two weeks later, when the course was com-
pleted for group 1, data collection 2 took place.
After that, group 2 participated in the course. The
final data collection took place after all participants
had completed the course.
The data collections were scheduled in the com-
puter lab of Trier University. Students were tested in
groups of 15 to 22 participants under the supervision
of two experimenters. They first completed three
information search tasks (one task of each type),
followed by two questionnaires concerning episte-
mological beliefs, and the information literacy
knowledge test.
To complete the information search tasks, the
participants could use all resources available from
these computers (access to the internet, to biblio-
graphic databases, and to the online library cata-
logue). The information search tasks were presented
ordered by difficulty. Participants were required to
record the publications found by using input boxes
that were provided by the software used. After the
completion of every task, the participants had to
answer several questions concerning the procedure
of their search. These data were the basis for scoring
the procedure. As it would be beyond the scope of
this paper, the results concerning the epistemological
beliefs questionnaires are not reported.
3.2.3 Hypotheses
With regard to the search tasks, we expected that
those tasks requiring more abilities should be more
difficult; in this case, we expected type 3 tasks to be
the most difficult followed by type 2 tasks, which, in
turn, should be more difficult than type 1 tasks (Hy-
pothesis 1).
As explained above, there were three variables
designed to assess information literacy: outcome and
procedure of the search tasks, and the knowledge
test. We expected to find significant correlations
among these instruments. As the knowledge test had
already been tried and tested in a different study,
finding correlations between the test and the search
tasks would corroborate the status of the search tasks
as indicator of information literacy (Hypothesis 2).
Furthermore, we expected that participants
would score higher on all instruments after partici-
pating in the course. Specifically, group 1 should
outperform group 2 at data collection 2. At the final
data collection, there should not be any difference
between the groups (Hypothesis 3).
3.3 Results
Before the course could be evaluated, the infor-
mation search tasks had to be scored independently
by two raters. The inter-rater-reliability (correlation
between the scores awarded by the two raters) was
in the range from r = 0.62 to r = 0.92; most correla-
tions were above r = 0.70. In those cases where the
scores differed, the raters agreed on one solution
which was used for the analyses.
CSEDU2014-6thInternationalConferenceonComputerSupportedEducation
356
The first hypothesis to be examined was whether
the expected order of task difficulties could be veri-
fied empirically. Data from data collection 1 is pre-
sented in table 1. As can be seen, tasks of type 3
were more difficult than tasks of type 2, which, in
turn, were more difficult than type 1 tasks. The table
shows the percentage of the maximum score for the
different tasks types.
Table 1: Percentage of maximum score for search task
outcome and procedure at data collection 1.
task type outcome procedure
1 77% 55%
2 50% 46%
3 32% 36%
For the following analyses, the outcome and proce-
dure scores of each data collection were summed up
separately, so that 2 scores for each data collection
resulted. These scores were scaled, in order to re-
strict their range from 0 to 1 and are presented in
table 2. Before using these scores for evaluating the
course, we determined whether there were differ-
ences between the two groups of participants before
the course started. Our analysis revealed that there
were no differences between the groups, neither on
the outcome scores (t[65] = 1.34, n.s.), nor on the
procedure scores (t[65] = 1.23, n.s.). Furthermore,
the two groups did not differ in their performance on
the knowledge test (t[65] = 0.78, n.s.). Scores on the
information literacy knowledge test were also scaled
to restrict their range from 0 to 1, and can be found
in table 3.
To examine the second hypothesis, correlations
between the scores on the knowledge test and the
two search task variables were computed using data
from data collection 1. It was decided to analyze
data from data collection 1 only, as the performance
at the following data collections reflects to a great
extent how much the participants have benefited
from the course, so the results might be distorted.
The outcome and procedure scores of the search
tasks correlated significantly (r = 0.22, p < 0.05),
even though the correlation was weak. Both scores
also correlated significantly with the performance on
the knowledge test (for the outcome scores r = 0.29,
p < 0.01, and the procedure scores r = 0.48, p < 0.01,
both one-tailed).
To evaluate the course (Hypothesis 3), the three
information literacy performance indicators were
analyzed separately. For each variable, a repeated
measures analysis of variance (ANOVA) was com-
puted. The time of data collection was a within sub-
jects factor, while group membership was a be-
tween-subjects factor. The respective information
literacy performance indicator was used as depend-
ent variable.
The performance on the knowledge test was ana-
lyzed first. The analysis revealed a significant main
effect of the within-subjects factor (F[2,130] =
216.53, p < 0.01) and a significant interaction of the
two factors (F[2,130] = 73.13, p < 0.01), what is
depicted in figure 1. To analyze group differences, a
t-test was computed for every data collection. At
data collection 1, there was no difference between
the groups (t[65] = 0.78, n.s.). At data collection
two, a significant difference could be found (t[65] =
10.47, p < 0.01), indicating that group 1 outper-
formed the other group. There was no significant
difference at data collection 3 (t[65] = 0.37, n.s.).
Next, the outcome scores of the search tasks
were analyzed. The ANOVA revealed a significant
main effect of the within-subjects factor F[2,130] =
45.77, p < 0.01) and a significant interaction of the
two factors (F[2,130] = 5.45, p < 0.01). To investi-
gate the pattern in more detail, t-tests were calculat-
ed to compare the two groups at each data collec-
tion. There were no significant differences at data
collections 1 and 3 (t[65] = 1.33 and t
[65] = 0.32,
respectively). However, the two groups differed at
data collection 2 (t[65] = 3.32, p < 0.01). Once
again, group 1 outperformed group 2, as can be seen
in figure 2.
Finally, the procedure scores of the search task
were analyzed. The ANOVA revealed a significant
main effect of the within-subjects factor F[2,130] =
148.46, p < 0.01) and a significant interaction of the
two factors (F[2,130] = 37.38, p < 0.01). Once
again, t-tests were applied to analyze group differ-
ences. There was no significant difference at data
collection 1 (t[65] = 1.23, n.s.), but significant dif-
ferences at data collections 2 (t[65] = 9.21, p <0.01)
and 3 (t[65] = 3.21, p < 0.01) in such a way that
group 1 scores higher than group 2, as is displayed
in figure 3.
Figure 1: Mean scores (and standard deviations) on the
information literacy test. DC = data collection.
0,00
0,20
0,40
0,60
0,80
1,00
DC1DC2DC3
group1
group2
FosteringInformationLiteracyinGermanPsychologyStudentsUsingaBlendedLearningApproach
357
Table 2: Mean scores (and standard deviations) for the outcome and procedure scores.
data collection 1 data collection 2 data collection 3
outcome
group 1 (n=37) 0.45 (0.19) 0.63 (0.19) 0.78 (0.17)
group 2 (n=30) 0.51 (0.16) 0.49 (0.13) 0.76 (0.18)
procedure
group 1 (n=37) 0.43 (0.13) 0.81 (0.10) 0.78 (0.08)
group 2 (n=30) 0.47 (0.13) 0.54 (0.12) 0.70 (0.11)
Table 3: Mean scores (and standard deviations) for the
information literacy knowledge test.
group
data
collection 1
data
collection 2
data
collection 3
group 1 0.59 (0.06) 0.76 (0.05) 0.75 (0.05)
group 2 0.61 (0.06) 0.62 (0.05) 0.75 (0.05)
Figure 2: Outcome scores (and standard deviations) of the
information search tasks.
Figure 3: Procedure scores (and standards deviations) of
the information search tasks.
3.4 Discussion
The results show that the hypotheses were con-
firmed. The first two hypotheses relate to the sound-
ness of the evaluation instruments. First, the analysis
of the search task difficulties indicated that the ex-
pected order of task difficulties could be verified
empirically. This shows that those tasks requiring
more competencies are also harder to solve for the
participants, what can be seen as an indication of
validity for the search tasks and the underlying task
taxonomy. The second hypothesis, postulating that
there would be significant correlations among all
three information literacy performance indicators,
could also be upheld. The fact that the correlations
are far from perfect lead us to the conclusion, that all
three instruments capture different facets of the
concept information literacy. It is of significance
that search task outcome and procedure scores both
correlated significantly with the knowledge test, as
this is a signal that the search tasks are valid, be-
cause the test had already been tested in a different
study.
The third and most important hypothesis was that
participation in the course improves information
literacy. As can be seen in figures 1 to 3, participa-
tion in the course improved information literacy on
all three performance indicators. To be exact, the
hypothesis was that group 1 would outperform group
2 at data collection 2. This was hypothesized be-
cause at that time, group 1 had already taken the
course, while group 2 had not. At data collection 3,
there should not be any group differences left. This
pattern could be observed when analyzing the scores
on the knowledge test and the outcome scores of the
search tasks. Analysis of the procedure scores dis-
played the same pattern at data collections 1 and 2.
At data collection 3, however, group 1 still per-
formed better, even though at this time both groups
had taken the course. A more detailed analysis re-
vealed that both groups improved; however, group 1
performance was enhanced stronger. No substantial
explanation for this result can be found except that
this might be random variations due to relatively
small sample size. As mentioned above, group sizes
were n = 30 and n = 37, respectively. Notwithstand-
ing that participants had been assigned to one of the
groups in a randomized way, it cannot be ruled out
that some group 1 participants learned more due to
individual differences. As this difference can only be
observed on one of the three performance indicators,
it can be ascribed to random variations. The hypoth-
esis can still be confirmed as both groups had im-
proved by taking the course. It should also be men-
tioned that there were significant differences be-
tween the groups at data collection 2. At this time, a
0,00
0,20
0,40
0,60
0,80
1,00
DC1DC2DC3
group1
group2
0,00
0,20
0,40
0,60
0,80
1,00
DC1DC2DC3
group1
group2
CSEDU2014-6thInternationalConferenceonComputerSupportedEducation
358
treatment group was compared to a waiting control
group. The group differences show that participants
did not become more information literate without
training, corroborating the attribution of information
literacy improvements to the participation in the
course.
To sum up, the results show that the information
literacy course is effective. So, this research adds to
the field a blended learning course that is tailored to
Psychology students and has been rigorously evalu-
ated. In the future, it might be developed further by
adding elements tailored to Master levels students.
As this course was tailored to Bachelor students, the
participants were mainly taught the essentials of
seeking academic information. To make carrying out
the course more resource efficient, it might also be
an idea to leave out the classroom seminars. A fur-
ther evaluation study might show whether this is
equally effective.
REFERENCES
American Library Association. 1989. Presidential commit-
tee on information literacy. Final Report [online].
Chicago, IL: American Library Association. Available
at: http://www.ala.org/acrl/publications/whitepapers/
presidential [Accessed 30 September 2013].
Association of College and Research Libraries. 2010.
Psychology information literacy standards [online].
Chicago, IL: American Library Association. Available
at: http://www.ala.org/acrl/standards/psych_info_lit
[Accessed 19 March 2013].
Carr, S. 2000. As distance education comes of age, the
challenge is keeping the students. Chronicle of Higher
Education 46(23), pp. A39-A41.
Center for Assessment and Research Studies. 2013. In-
formation literacy test (ILT) [online]. Harrisonburg,
VA: James Madison University. Available at:
http://www.madisonassessment.com/uploads/Manual-
ILT-2013.pdf [Accessed 29 January 2014].
Clardy, A. 2009. Distant, on-line education: effects, prin-
ciples and practices [online]. Available at:
http://files.eric.ed.gov/fulltext/ED506182.pdf [Ac-
cessed 29 January 2014].
Eisenberg, M. B. 2008. Information literacy: essential
skills for the information age. DESIDOC Journal Of
Library & Information Technology 28(2), pp. 39–47.
Julien, H. and Barker, S. 2009. How high-school students
find and evaluate scientific information: a basis for in-
formation literacy skills development. Library & In-
formation Science Research 31(1), pp. 12–17.
Kim, J. 2009. Describing and predicting information-
seeking behavior on the Web. Journal of the American
Society for Information Science and Technology 60(4),
pp. 679–693.
López-Pérez, M., Pérez-López, M. and Rodríguez-Ariza,
L. 2011. Blended learning in higher education: stu-
dents’ perceptions and their relation to outcomes.
Computers & Education 56(3), pp. 818–826.
McClelland, D. C. 1973. Testing for competence rather
than for "intelligence". American psychologist 28(1),
pp. 1–14.
National Forum on Information Literacy. n. d. What is
information literacy? [online]. Cambridge, MA: Na-
tional Forum on Information Literacy. Available at:
http://infolit.org/?page_id=3172 [Accessed 6 June
2012].
Noe, N. W. and Bishop, B. A. 2005. Assessing Auburn
University Library's tiger information literacy tutorial
(TILT). Reference Services Review 33(2), pp. 173–
187.
Project SAILS. 2013. Project SAILS information literacy
assessment [online]. Kent, OH: Kent State University.
Available at: https://www.projectsails.org/ [Accessed
16 August 2013].
Shavelson, R. J. 2010. On the measurement of competen-
cy. Empirical research in vocational education and
training 2(1), pp. 41–63.
Smith, J. K., Given, L. M., Julien, H., Ouellette, D. and
DeLong, K. 2013. Information literacy proficiency:
assessing the gap in high school students’ readiness
for undergraduate academic work. Library & Infor-
mation Science Research 35(2), pp. 88–96.
Thaxton, L., Faccioli, M.B. and Mosby, A.P. 2004. Lever-
aging collaboration for information literacy in psy-
chology. Reference Services Review 32(2), pp. 185–
189.
Timmers, C. F. and Glas, C. A. W. 2010. Developing
scales for information-seeking behaviour. Journal of
Documentation 66(1), pp. 46–69.
Warwick, C., Rimmer, J., Blandford, A., Gow, J. and
Buchanan, G. 2009. Cognitive economy and satisfic-
ing in information seeking: a longitudinal study of un-
dergraduate information behavior. Journal of the
American Society for Information Science and Tech-
nology 60(12), pp. 2402–2415.
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