Embed Size (px)
Citation preview
ORI GIN AL PA PER
Evaluating students’ perceptions of library and scienceinquiry: Validation of two new learning environmentquestionnaires
Barbara A. Schultz-Jones • Cynthia E. Ledbetter
Received: 7 November 2010 / Accepted: 7 January 2012 / Published online: 1 September 2013� Springer Science+Business Media Dordrecht 2013
Abstract As part of a larger study, the How My Library Supports Inquiry and the How
My Science Class Supports Inquiry questionnaires were developed for evaluating the
extent of inquiry-based teaching in classrooms and school libraries and the effect of this
instruction on student literacy and, by extension, the social good. Each has 28 items in
seven scales measuring students’ perceptions of the degree to which certain psychosocial
factors are prevalent in the science class and the school library. Using data from 872
elementary students and 639 secondary students, principal components factor analysis with
varimax rotation and Kaiser normalization confirmed the a priori structure of the ques-
tionnaires. The factor structure, internal consistency reliability, discriminant validity, and
the ability to distinguish between different classes and groups were supported for both
instruments. Validation of these instruments enables consideration of a new approach for
assessing the contribution of school libraries to the field of education, with specific
emphasis on science education through the study. In addition, this study makes a unique
contribution to the field of learning environments by evaluating the relationship between
school library programs and classroom environments.
Keywords Assessment � Evaluation � Inquiry � Library learning environment �School libraries � Science learning environment
Theoretical framework
Science teachers and school librarians share a common focus of developing and encour-
aging literacy. Standards for both professions recognise the value of inquiry-based learning
B. A. Schultz-Jones (&)College of Information, University of North Texas, 1155 Union Circle 311068, Denton,TX 76203-5017, USAe-mail: [email protected]
C. E. LedbetterThe University of Texas at Dallas, 800 West Campbell Road, Richardson, TX 75080-3021, USAe-mail: [email protected]
123
Learning Environ Res (2013) 16:329–348DOI 10.1007/s10984-013-9141-y
and encourage collaborative learning environments within the school community (National
Research Council 2000; American Association of School Librarians 2007).
School librarians and science teachers are challenged to provide environments that
positively affect the development of student science literacy skills. Research studies
conducted in many States (Lance et al. 1997, 2000a, b, c, 2001, 2002; Smith 2001)
demonstrated the impact of strong school library programs on student achievement in
reading. A study based on student evaluation of school library media centres (Todd and
Kuhlthau 2004) further supports the positive role of school libraries in affecting overall
student achievement. However, despite substantial efforts to document the positive rela-
tionship between school library programs and student achievement, Mardis (2007) con-
tends that ‘‘the effect of strong school library media programs on science achievement is
largely unreported’’ (} 4). With a national emphasis both on science and library inquiry
skills, a method for investigating students’ perceptions of inquiry in these learning envi-
ronments is useful and instructive to the school learning community.
Inquiry receives national attention because of the recognised need to develop the critical
thinking skills that empower independent, lifelong learners within an enlightened society.
As Wilson (1974) explains:
Inquiry, then, is a broad range of activities performed to explore and search out
variables and attributes relevant to discrepant stimuli. Descriptions and models of
inquiry are attempts to represent these activities as thinking patterns in verbal form.
Many of the attempts to produce these models have focused on steps of scientific
inquiry rather than the evoked processes related to the search and analysis. These
processes represent the heuristics of inquiry. (p. 127)
For school librarians, the inquiry process follows the progression of active engagement
between students and ideas using models such as the Information Search Process (ISP)
(Kuhlthau 1989, 2004), which has been applied to an examination of student science
projects and the role of the school library (Kuhlthau and McNally 2001). An extension of
this process is Guided Inquiry, a form of evidence-based practice that involves a ‘‘carefully
planned, closely supervised, targeted intervention by an instructional team that leads
children through inquiry learning from the early ages through their teen years, with the
goal of developing deep understanding and independent learning’’ (Kuhlthau et al. 2007,
p. 28). Table 1 compares the inquiry practice in science with the Kuhlthau model used by
school librarians.
Learning environment research applies to the evaluation of inquiry as pedagogy (e.g.
Maor and Fraser 1996) and the library learning environment (Schultz-Jones and Ledbetter
2009, 2010). Fraser’s definition provides the construct for conducting this research:
‘‘Learning environment refers to the social, psychological and pedagogical contexts in
which learning occurs and which affect student achievement and attitudes’’ (1998a, p. 3).
The body of learning environment research encompasses a lengthy historical development
to current applications. In 1936, Lewin wrote about relationships between the environment
and the personal characteristics of the inhabitants, as well as the environment’s effects on
human behaviour. Murray (1938) followed Lewin’s research on behaviour and the envi-
ronment and introduced his famous needs-press personality model that examined the
external influences that ‘press’ on motivation in conjunction with latent and manifest
personal ‘needs’. During the 1960 and 1970s, Walberg developed the Learning Environ-
ment Inventory (LEI) to use for an evaluation of Harvard Project Physics (Walberg and
Anderson 1968). About the same time, Moos (1974) developed his Classroom Environ-
ment Scale (CES). The purposes of these evaluation instruments are to: determine how
330 Learning Environ Res (2013) 16:329–348
123
individuals and groups of individuals react to their environment; to investigate what factors
can affect their reaction to the environment; and to explore associations between the
environment and student outcomes. Since the original work of Walberg and Moos, many
questionnaires have been developed to examine classroom life (Fraser 1998b, 2012). One
distinguishing feature of current classroom learning environment instruments is that they
have a number of scales for assessing different dimensions of the learning environment.
These scales can generally still be classified into one of Moos’ three basic domains:
1. Relationship Dimensions Involvement, extent to which students are attentive and
interested in class activities and participate in discussions; Affiliation, student
friendship and the extent to which students help each other and enjoy working
together; Teacher support, help, interest trust, and friendship the teacher shows toward
students.
2. Personal Growth or Goal Orientation Dimensions Task orientation, importance of
completing planned activities and sticking to the subject matter; Competition,
emphasis placed on students competing with each other for grades and recognition,
and the difficulty of achieving good grades.
3. System Maintenance and Change Dimensions Order and organization, emphasis on
students behaving in an orderly manner and on the organization of assignments and
class activities; Rule clarity, emphasis on establishing and following a clear set of rules
and on students knowing what the consequences will be if they do not follow them;
Teacher control, how strictly the teacher enforces rules and the severity of punishment
for rule infractions; Innovation, how much students contribute to planning class
activities and the number of unusual and varying activities planned by the teacher.
(Moos 1979, p. 141)
Table 1 Process models of inquiry
Science Inquiry Library Inquiry Stimulus Initiation, Selection, Exploration
Empirical Inquiry Conceptual Inquiry Affective Cognitive Action Curious events Contradictory
phenomena Uncertainty Vague Exploring
Data gaps Limit determination Optimism Chance observations Theory articulation Confusion,
frustration, doubt Seeking relevant
information Search Processes Formulation and Collection
Empirical Experiments
Conceptual Experiments
Affective Cognitive Action
Observation Attribute search Clarity Focused Classification Symbolic
representation Seeking pertinent
information Inferring Conceptual testing Sense of direction Increased interest Predicting Idealization Confidence Quantifying Analysis for Cause Simplification
Results Presentation and Assessment New Phenomena New Explanations Affective Cognitive Action
Objects, events Paradigms, models Satisfaction or disappointment
Reflection Observable relationships
Relationships, principles, theories, laws
Correlated occurrences
Sense of accomplishment
Increased self awareness
Documenting
After Wilson, 1974 Kuhlthau, 1989, 2004
Learning Environ Res (2013) 16:329–348 331
123
With these domains and dimensions as applicable to constructivist learning and
indicative of aspects of the psychosocial environment of schools, two learning environment
questionnaires are of specific interest for evolving our research.
The Constructivist Learning Environment Survey (CLES) was developed to assess
innovative classroom environments with a psychological view of learning that focused on
students as co-constructors of their own knowledge (Taylor et al. 1995, 1997; Taylor and
Fraser 1991). The CLES was used in the development of the new questionnaires because of
its ability to characterise specific dimensions of the constructivist classroom. The five
scales of the CLES (Personal Relevance, Uncertainty of Science, Shared Control, Critical
Voice, and Student Negotiation) provided a useful basis for our design.
The established validity and usefulness of the CLES was important when selecting it to
construct questionnaires that would elicit students’ perceptions of their science and library
learning environments. Taylor et al. (1997) established the factorial validity and reliability
of the CLES with a sample of 494 13-years-old students in 41 science classes in 13 schools
in Western Australia. Further, in 2000, Aldridge, Fraser, Taylor and Chen cross-validated
the CLES in Australia with a sample of 1,081 science students in 50 classes and in Taiwan
with a sample of 1,879 students in 50 classes. Consideration was also given to the cultural
adaptability of the instrument (Lee and Taylor 2001) for use in longitudinal studies within
the school district.
The What Is Happening In this Class? (WIHIC) Questionnaire (Fraser et al. 1996) was
developed for use in many classroom environment contexts using the most reliable features
of existing instruments. The final version had seven, eight-item scales: Student Cohe-
siveness, Teacher Support, Involvement, Investigation, Task Orientation, Cooperation and
Equity. The first six scales were adapted from existing instruments; the Equity scale was
added to reflect more recent educational concerns thereby supporting constructivist
learning theory (von Glasersfeld 1989).
The WIHIC draws out students’ perception of their individual positions within the
classroom, as opposed to their perceptions of the class as a whole. Since its initial
development, the WIHIC has been found to be valid and reliable in studies to assess
learning environments around the world (Allen and Fraser 2007; Dorman et al. 2006;
Gabler and Fraser 2007; Martin-Dunlop and Fraser 2008; Koul and Fisher 2006; Ogbuehi
and Fraser 2007; Saunders and Fisher 2006; Telli et al. 2006; Wahyudi and Treagust 2006;
Zandvliet and Fraser 2005). Further, it has also been validated using multitrait–multi-
method modelling within a confirmatory factor analysis framework (Dorman 2008).
The CLES and the WIHIC were selected for use with secondary students and adapted
for elementary students in this study because of their distinctive ability to characterise
specific dimensions of the classroom. Both questionnaires have two distinct applications.
The first is the assessment of the preferred learning environment, and the second is
assessment of what is actually occurring in the current learning environment.
Learning environment assessment instruments continue to evolve and offer a variety of
possibilities for a variety of educational contexts. According to Geelan (1997), ‘‘If edu-
cational innovations are to succeed, they must take a more realistic view of the realities of
classroom life than have some past curricular projects’’ (p. 4). To reflect the reality of
inquiry based support the How My Library Supports Inquiry (HMLSI) and the How My
Science Class Supports Inquiry (HMSSI) were developed to evaluate the extent of inquiry-
based teaching in classrooms and school libraries. Each instrument has 28 items com-
prising seven scales to measure students’ perceptions of the degree to which certain
psychosocial factors are prevalent in the science class and the school library.
332 Learning Environ Res (2013) 16:329–348
123
Data source
The research setting was a public school district covering pre-school to Grade 12 in north
Texas that provides innovative structures for education through elementary mathematics
and pre-engineering integrated curriculum, themed middle-school cohorts, and dual-credit
coursework in the high school. District demographics show 6,658 students (eight ele-
mentary schools, one middle school, and one high school). Subpopulations include 49 %
African–American, 8 % hispanic, and 42 % white; 61.4 % economically disadvantaged;
13 % special education; 3 % limited english proficiency (LEP), 2 % Bilingual/English as a
second language (ESL) education; and 7 % gifted and talented education.
Methods
The study was based on quantitative data derived from the learning environment dimen-
sions in the two new questionnaires. The actual and preferred forms of the elementary
version of the HMLSI and HMSSI were administered to 872 grades 3–5 students in science
classrooms and in regard to their school library experiences. The actual and preferred
forms of the secondary version of the HMLSI and HMSSI were administered to 639 grades
6–12 students in science classrooms and in regard to their school library experiences.
The HMLSI and HMSSI have 28 questions with four items in each of the seven climate
scales of Reflection, Librarian/Teacher Support, Involvement, Investigation, Task Orien-
tation, Cooperation, and Equity. The elementary version has a three-point frequency
response scale (3 = Almost Always, 2 = Sometimes, 1 = Almost Never) and the sec-
ondary version used a five-point frequency response scale (5 = Almost Always,
4 = Often, 3 = Sometimes, 2 = Seldom, and 1 = Almost Never). The HMLSI was
designed for the school library setting and was a modification of the HMSSI version used
for the science classroom. Modifications included replacing the terms; science classroom’
with ‘library’, and modifying the concept of ‘doing schoolwork’ to ‘finding resources (such
as books and magazines)’.
The questionnaires were presented to the students through a district-wide online portal.
Teachers read the instructions to the students, if necessary, and assured the students that the
answers would remain anonymous. The only help that the teachers provided was if a
student did not know a specific word. Once completed, the questionnaires were collected
electronically and delivered to an administrator, who then transferred all data to the
researchers.
Using data from 872 elementary students and 639 secondary students, principal com-
ponents factor analysis with varimax rotation and Kaiser normalisation was conducted to
check the structure of the HMLSI and HMSSI. Internal consistency reliability, discriminant
validity, and the ability to distinguish between different classes also were checked for both
instruments.
Results
Factor analyses of the elementary HMLSI and HMSSI
Factor analyses (Kim and Mueller 1982) is a statistical technique used in data reduction to
identify a small number of underlying variables, or factors, that explain most of the
Learning Environ Res (2013) 16:329–348 333
123
variance observed in a much larger number of manifest variables. Using both cases (Prefer
and Actual) of the Elementary HMLSI and HMSSI data, factor and item analyses were
conducted to identify faulty items that could be removed to improve the internal consis-
tency reliability and factorial validity of the seven scales in these questionnaires. Principal
components factor analysis with varimax rotation (in which the factor axes are kept at right
angles to each other) was used to check the scale structure.
Two items on the elementary versions appeared to be problematic for the students: Item
12 HMLSI (Preferred) and Item 16 HMLSI (Actual) were ambiguously interpreted.
Removal of Item 12 from the Involvement scale (Preferred) and Item 16 from the
Investigation scale (Actual) enhanced the reliability and factor structure of the instrument.
Following removal of these two items, all of the remaining 26 items had a factor loading of
at least 0.4 on their a priori scale and no other scale for the analyses. Table 2 presents the
resulting factor loadings for both cases of the Elementary HMLSI. None of the items on
either form of the Elementary HMSSI were problematic (see Table 3).
These factor analyses confirmed the a priori structure of the elementary version of the
HMLSI and HMSSI. The percentage of the total variance and eigenvalue associated with
each factor are shown at the bottoms of Tables 2 and 3. The total variance accounted for by
the 26 items within the seven scales on the HMLSI was 59.02 % for Preferred and 59.66 %
for Actual, and ranged from 3.59 to 11.65 % for different scales and cases. The eigen-
values ranged from 1.00 to 2.86 for Preferred and from 1.01 to 2.62 for Actual. On the
HMSSI, the total amount of variance accounted for by the 28 items within the seven scales
was 51.85 % for Preferred and 59.80 % for Actual, and ranged from 5.43 to 9.88 % for
different scales and cases. The eigenvalues ranged from 1.52 to 2.35for Preferred and from
1.75 to 2.77for Actual. Overall, these data provide strong support for the factorial validity
of the seven-scale HMLSI and HMSSI.
Internal consistency reliability and discriminant validity of the elementary HMLSI
and HMSSI
Cronbach’s alpha coefficient was used as an index of internal consistency reliability for
each HMLSI and HMSSI scale for the individual unit of analysis. The alpha coefficients of
different HMLSI scales were high, ranging from 0.74 to 0.87 for Preferred and from 0.68 to
0.87 for Actual with the individual as the unit of analysis. Further, the alpha coefficients of
different HMSSI scales were also high, ranging from 0.74 to 0.87 for Preferred and from
0.63 to 0.88 for Actual with the individual as the unit of analysis.
To assess the extent to which a scale is unique in the dimension that it covers and is not
included in another scale in the same instrument, the mean correlation of a scale with other
scales was used as a convenient index of discriminant validity. For HMLSI, in the pre-
ferred learning environment (Preferred), the mean correlation of a scale with the other
scales varied between 0.28 and 0.39 with the individual as the unit of analysis; for the
actual learning environment (Actual), the mean correlation of a scale with the other scales
varied between 0.18 and 0.34 with the individual as the unit of analysis. These results
suggest that each scale assesses a unique dimension and that, while there is some overlap
between raw scores on scales, they are relatively independent of each other. For HMSSI, in
the preferred learning environment (Preferred), the mean correlation of a scale with the
other scales varied between 0.22 and 0.39 with the individual as the unit of analysis; for the
actual learning environment (Actual), the mean correlation of a scale with the other scales
varied between 0.24 and 0.31 with the individual as the unit of analysis. These results
suggest that each scale assesses a unique dimension and that, while there is some overlap
334 Learning Environ Res (2013) 16:329–348
123
Ta
ble
2F
acto
rlo
adin
gs
for
the
HM
LS
Ifo
rel
emen
tary
stu
den
ts
Fac
tor
load
ing
s
Item
Refl
ecti
on
Lib
rari
ansu
ppo
rtIn
vo
lvem
ent
Inv
esti
gat
ion
Tas
ko
rien
tati
on
Coo
per
atio
nE
qu
ity
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
10
.698
0.6
71
20
.691
0.6
72
30
.631
0.5
15
40
.725
0.6
41
50
.736
0.7
20
60
.630
0.6
03
70
.621
0.6
65
80
.571
0.4
19
90
.50
30
.731
10
0.5
10
0.4
94
11
0.4
90
0.6
16
12
–0
.568
13
0.5
55
0.4
15
14
0.6
53
0.5
83
15
0.6
97
0.5
52
16
0.6
33
–
17
0.7
38
0.5
00
18
0.7
81
0.5
88
19
0.5
98
0.5
00
20
0.5
13
0.7
27
21
0.7
85
0.6
59
22
0.7
29
0.6
93
23
0.4
85
0.4
83
Learning Environ Res (2013) 16:329–348 335
123
Ta
ble
2co
nti
nued
Fac
tor
load
ing
s
Item
Refl
ecti
on
Lib
rari
ansu
ppo
rtIn
vo
lvem
ent
Inv
esti
gat
ion
Tas
ko
rien
tati
on
Coo
per
atio
nE
qu
ity
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
24
0.6
00
0.6
88
25
0.6
78
0.6
70
26
0.7
30
0.6
80
27
0.6
16
0.5
81
28
0.7
15
0.6
86
%V
aria
nce
10
.78
11
.65
9.7
21
0.7
49
.21
9.7
99
.19
9.3
49
.04
7.3
76
.31
7.2
04
.75
3.5
9
Eig
en-v
alu
e2
.42
2.2
61
.72
2.0
11
.41
2.7
41
.08
2.6
21
.03
2.0
61
.00
2.0
22
.86
1.0
1
N=
87
2st
ud
ents
ing
rad
es3
–5
in8
elem
enta
rysc
ho
ols
Item
s1
2an
d1
6w
ere
om
itte
d
‘Pre
ferr
ed’
refe
rsto
the
lear
nin
gen
vir
on
men
tst
ud
ents
wo
uld
lik
e;‘A
ctu
al’
refe
rsto
the
stud
ents
’cu
rren
tp
erce
pti
on
so
fth
ele
arn
ing
env
iron
men
t
336 Learning Environ Res (2013) 16:329–348
123
Ta
ble
3F
acto
rL
oad
ings
for
the
HM
SS
Ifo
rel
emen
tary
stu
den
ts
Fac
tor
load
ing
s
Item
Refl
ecti
on
Tea
cher
sup
po
rtIn
vo
lvem
ent
Inv
esti
gat
ion
Tas
ko
rien
tati
on
Coo
per
atio
nE
qu
ity
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
10
.764
0.6
99
20
.321
0.7
19
30
.633
0.6
66
40
.597
0.5
67
50
.68
40
.62
1
60
.50
80
.62
9
70
.46
00
.57
9
80
.71
60
.49
8
90
.58
20
.701
10
0.4
59
0.5
68
11
0.6
34
0.7
36
12
0.5
62
0.5
82
13
0.5
48
0.6
65
14
0.4
61
0.5
47
15
0.5
17
0.6
47
16
0.4
18
0.4
32
17
0.7
31
0.6
52
18
0.5
44
0.6
42
19
0.4
11
0.6
16
20
0.5
79
0.6
75
21
0.5
33
0.5
36
22
0.7
26
0.6
94
23
0.4
04
0.6
38
Learning Environ Res (2013) 16:329–348 337
123
Ta
ble
3co
nti
nued
Fac
tor
load
ing
s
Item
Refl
ecti
on
Tea
cher
sup
po
rtIn
vo
lvem
ent
Inv
esti
gat
ion
Tas
ko
rien
tati
on
Coo
per
atio
nE
qu
ity
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
24
0.5
96
0.6
28
25
0.5
91
0.6
92
26
0.5
90
0.6
65
27
0.6
96
0.6
27
28
0.4
38
0.7
38
%V
aria
nce
8.4
09
.88
7.7
89
.73
7.6
99
.14
6.4
78
.99
5.8
58
.65
5.4
37
.18
5.2
76
.24
Eig
en-v
alu
e2
.35
2.7
72
.18
2.7
22
.15
2.5
61
.81
2.5
21
.64
2.4
21
.52
2.0
11
.48
1.7
5
N=
87
2st
ud
ents
ing
rad
es3
–5
in8
elem
enta
rysc
ho
ols
‘Pre
ferr
ed’
refe
rsto
the
lear
nin
gen
vir
on
men
tst
ud
ents
wo
uld
lik
e;‘A
ctu
al’
refe
rsto
the
stud
ents
’cu
rren
tp
erce
pti
on
so
fth
ele
arn
ing
env
iron
men
t
338 Learning Environ Res (2013) 16:329–348
123
between raw scores on scales, they are relatively independent of each other. Additionally,
the factor analysis results support the independence of factor scores.
Factor analyses of the secondary HMLSI and HMSSI
Again, factor analyses were used to identify the underlying factors that explain most of the
variance observed. As with the elementary version, both Preferred and Actual forms of the
secondary version of the HMLSI and HMSSI data, factor and item analyses were carried
out to identify faulty items that could be removed to improve the internal consistency
reliability and factorial validity of the seven scales in these questionnaires. Principal
components factor analysis with varimax rotation was again used to check the scale
structure. As a result, no items on either form of the Secondary HMLSI and HMSSI were
found to be faulty (see Tables 4, 5).
The factor analysis with varimax rotation and Kaiser normalization confirmed the a
priori structure of the Secondary HMLSI and HMSSI. The percentage of the total variance
and eigenvalue associated with each factor are shown at the bottom of Tables 4 and 5. The
total amount of variance accounted for by the 28 items within the seven scales on the
HMLSI is 76.63 % for Prefer and 82.57 % for Actual, and ranges from 6.49 to 14.74 % for
different scales and cases. The eigenvalues range from 2.13 to 2.98 for Prefer and from
1.82 to 2.68 for Actual. On the HMSSI, the total amount of variance accounted for by the
28 items within the seven scales is 69.46 % for Prefer and 83.29 % for Actual, and ranges
from 8.05 to 13.03 % for different scales and cases. The eigenvalues range from 2.07 to
2.99 for Prefer and from 2.25 to 2.65 for Actual. On the whole, these data provide robust
support for the factorial validity of the seven-scale secondary school How My Library
Supports Inquiry (HMLSI) and How My Science Class Supports Inquiry (HMSSI).
Internal consistency reliability and discriminant validity of the secondary HMLSI
and HMSSI
Cronbach’s alpha coefficient was used as an index of internal consistency reliability for
each HMLSI and HMSSI scale. The alpha coefficients of different HMLSI scales were
high, ranging from 0.72 to 0.83 for Preferred and from 0.70 to 0.81 for Actual with the
individual as the unit of analysis. Additionally, the alpha coefficients of different HMSSI
scales were also high, ranging from 0.68 to 0.73 for Preferred and from 0.71 to 0.84 for
Actual, again, with the individual as the unit of analysis.
As with the elementary versions, the mean correlation of a scale with other scales was
used as an index of discriminant validity. For the secondary HMLSI for the preferred
learning environment (Preferred), the mean correlation of a scale with the other scales
varied between 0.23 and 0.36 with the individual as the unit of analysis; for the actual
learning environment (Actual), the mean correlation of a scale with the other scales varied
between 0.28 and 0.33 with the individual as the unit of analysis. These results suggest that
each scale assesses a unique dimension and that, while there is some overlap between raw
scores on scales, they are relatively independent of each other. For the secondary HMSSI
for the preferred learning environment (Preferred), the mean correlation of a scale with the
other scales varied between 0.26 and 0.38 with the individual as the unit of analysis; for the
actual learning environment (Actual), the mean correlation of a scale with the other scales
varied between 0.22 and 0.30 with the individual as the unit of analysis. These results
suggest that each scale measures a single dimension and that scales are relatively
Learning Environ Res (2013) 16:329–348 339
123
Ta
ble
4F
acto
rlo
adin
gs
for
the
HM
LS
Ifo
rse
condar
yst
uden
ts
Fac
tor
load
ing
s
Item
Refl
ecti
on
Lib
rari
ansu
ppo
rtIn
vo
lvem
ent
Inv
esti
gat
ion
Tas
ko
rien
tati
on
Co
op
erat
ion
Eq
uit
y
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
10
.767
0.7
84
20
.785
0.7
65
30
.803
0.7
13
40
.765
0.7
42
50
.737
0.7
31
60
.678
0.7
69
70
.682
0.8
07
80
.607
0.7
51
90
.559
0.5
69
10
0.6
86
0.4
63
11
0.6
08
0.5
49
12
0.6
15
0.5
35
13
0.6
87
0.6
93
14
0.6
81
0.6
72
15
0.6
33
0.6
76
16
0.7
02
0.6
38
17
0.6
90
0.7
30
18
0.7
66
0.7
33
19
0.7
29
0.7
48
20
0.6
84
0.7
69
21
0.7
30
0.6
72
22
0.7
44
0.7
24
23
0.6
95
0.6
82
340 Learning Environ Res (2013) 16:329–348
123
Ta
ble
4co
nti
nued
Fac
tor
load
ing
s
Item
Refl
ecti
on
Lib
rari
ansu
ppo
rtIn
vo
lvem
ent
Inv
esti
gat
ion
Tas
ko
rien
tati
on
Co
op
erat
ion
Eq
uit
y
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
24
0.6
92
0.7
46
25
0.6
75
0.7
17
26
0.7
43
0.7
78
27
0.6
85
0.7
67
28
0.7
04
0.7
77
%V
aria
nce
12
.87
14
.74
11
.74
13
.15
11
.18
12
.88
11
.17
12
.63
10
.64
11
.53
10
.50
11
.15
8.5
46
.49
Eig
en-v
alu
e2
.60
2.1
32
.29
2.6
82
.13
2.6
12
.13
2.5
42
.98
2.2
32
.94
2.1
22
.39
1.8
2
N=
63
9st
ud
ents
ing
rad
es6
–9
in2
seco
nd
ary
sch
ools
‘Pre
ferr
ed’
refe
rsto
the
lear
nin
gen
vir
on
men
tst
ud
ents
wo
uld
lik
e;‘A
ctu
al’
refe
rsto
the
stud
ents
’cu
rren
tp
erce
pti
on
so
fth
ele
arn
ing
env
iron
men
t
Learning Environ Res (2013) 16:329–348 341
123
Ta
ble
5F
acto
rlo
adin
gs
for
the
HM
SS
Ifo
rse
condar
yst
uden
ts
Fac
tor
load
ing
s
Item
Refl
ecti
on
Tea
cher
sup
po
rtIn
vo
lvem
ent
Inv
esti
gat
ion
Tas
ko
rien
tati
on
Co
op
erat
ion
Eq
uit
y
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
10
.783
0.7
61
20
.761
0.7
67
30
.756
0.7
30
40
.682
0.6
66
50
.779
0.6
94
60
.755
0.7
16
70
.730
0.6
90
80
.626
0.6
37
90
.740
0.7
12
10
0.5
21
0.4
19
11
0.7
40
0.6
36
12
0.5
83
0.4
42
13
0.7
01
0.7
27
14
0.6
80
0.6
99
15
0.6
84
0.6
72
16
0.7
12
0.5
73
17
0.6
79
0.6
95
18
0.7
64
0.7
25
19
0.7
32
0.6
37
20
0.6
82
0.6
56
21
0.7
23
0.7
51
22
0.7
63
0.7
39
23
0.6
92
0.6
76
342 Learning Environ Res (2013) 16:329–348
123
Ta
ble
5co
nti
nued
Fac
tor
load
ing
s
Item
Refl
ecti
on
Tea
cher
sup
po
rtIn
vo
lvem
ent
Inv
esti
gat
ion
Tas
ko
rien
tati
on
Co
op
erat
ion
Eq
uit
y
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
Pre
fer
Act
ual
24
0.6
99
0.6
94
25
0.7
25
0.7
85
26
0.7
57
0.7
47
27
0.6
87
0.6
40
28
0.7
32
0.6
52
%V
aria
nce
10
.95
13
.03
10
.67
12
.97
10
.17
12
.68
10
.04
12
.31
9.6
41
2.1
89
.55
12
.06
8.4
58
.05
Eig
en-v
alu
e2
.07
2.6
52
.99
2.6
32
.85
2.5
52
.81
2.4
52
.70
2.4
12
.67
2.3
82
.37
2.2
5
N=
63
9st
ud
ents
ing
rad
es6
–9
in2
seco
nd
ary
sch
ools
‘Pre
ferr
ed’
refe
rsto
the
lear
nin
gen
vir
on
men
tst
ud
ents
wo
uld
lik
e;‘A
ctu
al’
refe
rsto
the
stud
ents
’cu
rren
tp
erce
pti
on
so
fth
ele
arn
ing
env
iron
men
t
Learning Environ Res (2013) 16:329–348 343
123
independent of each other. Again, the factor analysis results support the independence of
factor scores.
Scholarly significance
The factor structure, internal consistency reliability, and discriminant validity were sup-
ported for the HMLSI and HMSSI with our sample of elementary and secondary students.
Thus, the overall results support the validity of these questionnaires for use with students in
schools in north Texas. This study provides another example of the use of learning
environment variables in the evaluation of educational programs (Dryden and Fraser 1998;
Maor and Fraser 1996; Schultz-Jones and Ledbetter 2009, 2010).
Development and validation of the library form of the elementary and secondary
HMLSI and HMSSI adds to a useful range of instruments for a variety of classroom
contexts. These instruments can be used to gain knowledge about student perceptions in a
process of understanding and guiding the evolution and improvement of the library and
classroom learning environments, with emphasis on key dimensions for which there could
be differences between what is actually happening and what is preferred. When these
instruments are used with the same students, it would be instructive to gauge the extent to
which the school library is supporting the science curriculum and the extent to which
students perceive differences between the two learning environments. Reflection on and
analysis of the results can provide data for establishing common goals and coordinated
strategies to enhance the learning environments in both the science classroom and the
school library. This collaborative effort also is likely to strengthen the support for science
learning initiatives and could provide inspiration to school librarians and classroom
teachers on ways in which to enhance the interaction between the school library and the
classroom.
Educators could use these instruments to assess and evaluate the evolution of learning
environments in a cyclical process of continuous improvement. This could involve not only
assessment of student perceptions, but also assessment of the perceptions of classroom
teachers and school librarians. Applying the instruments to a variety of perspectives
engages all stakeholders and provides the opportunity to consider various interventions in a
coordinated effort to move the learning environments to an optimal level. These efforts
could initiate feedback from the students where clarification of perceptions is required,
offering an interactive exchange of ideas to strengthen and enhance the learning envi-
ronment. In essence, educators become action researchers as they assess the learning
environment over which they exert influence and identify possible intervention strategies.
The extent to which the elements of a positive learning environment are anchored
depends on the orientation and personal behaviour a school librarian or classroom teacher
exhibits. Assessing the relationship between this behaviour and student learning outcomes
can be used as a method to determine the impact of the current learning environment, new
teaching methods, staffing changes, and changes to the physical or virtual access to
resources.
Evaluating school library programs and their relationship to student achievement from
the perspective of a constructivist learning environment suggests a new approach for
considering the contribution of school libraries to the field of education. Instruction and
learning are integral to school library programs. Tools that enable constructive assessment
of the learning environments associated with these programs could enable improvement of
teaching methods and relationships between students and school librarians. This is likely to
344 Learning Environ Res (2013) 16:329–348
123
contribute to further recognition of the strong role of the school library program in the
school learning community.
Applying a psychosocial construct and a constructivist learning environment approach
to the school library extends the field of learning environment research while also
extending the scope of research on the impact of school libraries to student achievement.
Future research
School learning communities continue to deal with constrained resources and pressure to
advance student learning. As a result, consideration of the elements necessary to build and
maintain an optimal learning environment that supports, encourages and advances student
learning and achievement will continue to be a prominent theme. Future research, then,
must include further exploration of the learning environment dimensions and successful
interventions that influence a reduction in the gap between perceptions of what is occurring
and what is preferred. An application of evidence-based practice using learning environ-
ment instruments could also prove effective in identifying elements that support student
achievement. Bridging the gap between research and practice could extend the value of
utilising these learning environment instruments.
Evaluation of student achievement on standardised tests, along with qualitative inter-
views with the classroom teachers and school librarians, would provide additional mixed-
method assessment. The participating school district is committed to exploring these
dimensions and a longitudinal study is underway with successive applications of the new
instruments to all schools in the district to gauge the degree to which assessment, reflection
and intervention influence the learning environment and student achievement. This cycle of
continuous improvement relies on teacher and school librarian collaboration, with the
researchers, to explore interventions and assess results. Extending the application of this
assessment across the school district also enables a breadth of coverage with diverse school
communities that is likely to provide additional depth to the utility of the instruments.
Evaluation of their utility could involve the addition of new dimensions or additional
instruments. Of interest is the development of teacher and school librarian version of the
learning environment instruments to gauge complementary educator perceptions in concert
with student perceptions.
Extending the application of these instruments to a variety of international school
communities is also underway. Data collection and assessment of the school library
learning environment in a European international school has been completed, together with
evaluation and reflection with the school librarian and researchers. Consideration of these
instruments across various school library situations is likely to contribute to a broader and
deeper understanding of their utility. Additionally, extending the application of these
instruments to other curricular areas would be beneficial for optimising the recognition and
delivery of the interdisciplinary role of the school library.
With a variety of opportunities and responsibilities for meeting the learning needs of
students, school librarians can develop and nurture an optimal learning environment that
makes a positive and measurable contribution to the educational process. The potential for
measuring and improving the learning environment indicates that this methodology can
contribute to research on the positive impact of school libraries on student achievement.
Our study makes a unique contribution to the field of learning environments by eval-
uating school library programs and their relationship to classroom environments with
parallel instruments for evaluating the same scales in the science classroom learning
Learning Environ Res (2013) 16:329–348 345
123
environment. While this study validated the use of two new instruments for learning
environment research, from a practical point of view, it is a starting point for considering
and extending the contribution of school libraries to the field of education, specifically
science education.
References
Aldridge, J. M., Fraser, B. J., Taylor, P. C., & Chen, C. C. (2000). Constructivist learning environments in across-national study in Taiwan and Australia. International Journal of Science Education, 22, 37–55.
Allen, D., & Fraser, B. J. (2007). Parent and student perceptions of classroom learning environment and itsassociation with student outcomes. Learning Environments Research, 10, 67–82. doi:10.1007/s10984-007-9018-z.
American Association of School Librarians [AASL]. (2007). Standards for the twenty first-century learner.Available at: http://www.ala.org/ala/mgrps/divs/aasl/guidelinesandstandards/learningstandards/standards.cfm
Dorman, J. P. (2008). Use of multitrait-multimethod modeling to validate actual and preferred forms of theWhat Is Happening In this Class? (WIHIC) questionnaire. Learning Environment Research, 11,179–193.
Dorman, J. P., Aldridge, J. M., & Fraser, B. J. (2006). Using students’ assessment of classroom environmentto develop a typology of secondary school classrooms. International Education Journal, 7, 906–915.
Dryden, M., & Fraser, B. J. (1998, April). The impact of systemic reform efforts in promoting constructivistapproaches in high school science. Paper presented at the annual meeting of the American EducationalResearch Association, San Diego, CA.
Fraser, B. J. (1998a). The birth of a new journal: Editor’s introduction. Learning Environments Research, 1,1–5.
Fraser, B. J. (1998b). Classroom environment instruments: Development, validity and applications.Learning Environments Research, 1, 7–33.
Fraser, B. J. (2012). Classroom learning environments: Retrospect, context and prospect. In B. J. Fraser, K.G. Tobin, & C. J. McRobbie (Eds.), Second international handbook of science education (pp.1191–1239). New York: Springer.
Fraser, B. J., Fisher, D. L., & McRobbie, C. J. (1996, April). Development, validation and use of personaland class forms of a new classroom environment instrument. Paper presented at the annual meeting ofthe American Educational Research Association, New York.
Gabler, C. T., & Fraser, B. J. (2007, April). Sustained, job embedded professional development and thelearning environment of middle-school mathematics classrooms. Paper presented at the annual meetingof the American Educational Research Association, Chicago.
Geelan, D. R. (1997). Weaving narrative nets to capture school science classrooms. Research in ScienceEducation, 27, 553–563.
Kim, J., & Mueller, C. W. (1982). Introduction to factor analysis: What it is and how to do it. Beverly Hills,CA: Sage Publications.
Koul, R. B., & Fisher, D. L. (2006). A contemporary study of learning environments in Jammu, India. In D.L. Fisher & M. S. Khine (Eds.), Contemporary approaches to research on learning environments (pp.273–296). Singapore: World Scientific.
Kuhlthau, C. C. (1989). Information search process: A summary of research and implications for schoollibrary media programs. School Library Media Quarterly, 18(5), 19–25.
Kuhlthau, C. C. (2004). Seeking meaning: A process approach to library and information services (2nd ed.).Westport, CT: Libraries Unlimited.
Kuhlthau, C. C., Maniotes, L. K., & Caspari, A. K. (2007). Guided inquiry: Learning in the twenty firstcentury. Westport, CT: Libraries Unlimited.
Kuhlthau, C. C., & McNally, M. (2001). Information seeking for learning: A study of librarians’ perceptionsof learning in school libraries. The New Review of Information Behaviour Research, 2, 167–177.
Lance, K. C., Hamilton-Pennell, C., Rodney, M. J., Peterson, L., & Sitter, C. (2000a). Informationempowered: The school librarian as an agent of academic achievement in Alaska schools. Juneau:Alaska State Library.
Lance, K. C., Rodney, M. J., & Hamilton-Pennell, C. (2000b). How school librarians help kids achievestandards: The second Colorado study. Denver, CO: Colorado State Library, Colorado Board ofEducation.
346 Learning Environ Res (2013) 16:329–348
123
Lance, K. C., Rodney, M. J., & Hamilton-Pennell, C. (2000c). Measuring up to standards: The impact oflibrary programs and information literacy in Pennsylvania schools. Harrisburg, PA: PennsylvaniaDepartment of Education.
Lance, K. C., Rodney, M. J., & Hamilton-Pennell, C. (2001). Good schools have good librarians: Oregonschool librarians collaborate to improve student achievement. Portland, OR: Oregon EducationalMedia Association.
Lance, K. C., Rodney, M. J., & Hamilton-Pennell, C. (2002). How school libraries improve outcomes forchildren: The New Mexico study. Santa Fe, NM: New Mexico State Library.
Lance, K. C., Welborn, L., & Hamilton-Pennell, C. (1997). The impact of school media centers on academicachievement. Castle Rock, CO: Hi Willow Research.
Lee, S., & Taylor, P. (2001, December). The cultural adaptability of the CLES: A Korean perspective. Paperpresented at the annual meeting of the Australian Association for Research in Education, Fremantle,Australia.
Lewin, K. (1936). Principles of topological psychology. New York: McGraw.Maor, D., & Fraser, B. J. (1996). Use of classroom environment perceptions in evaluating inquiry-based
computer-assisted learning. International Journal of Science Education, 18, 401–421.Mardis, M. (2007). School libraries and science achievement: A view from Michigan’s middle schools.
School Library Media Research, 10. Available at http://www.ala.org/ala/mgrps/divs/aasl/aaslpubsandjournals/slmrb/slmrcontents/volume10/mardis_schoollibrariesandscience.cfm
Martin-Dunlop, C. S., & Fraser, B. J. (2008). Learning environment and attitudes associated with aninnovative science course designed for prospective elementary teachers. International Journal ofScience and Mathematics Education, 6, 163–190.
Moos, R. H. (1974). Classroom Environment Scale manual. Palo Alto, CA: Consulting Psychologists Press.Moos, R. H. (1979). Evaluating educational environments. San Francisco: Jossey-Bass Publishers.Murray, H. A. (1938). Explorations in personality. New York: Oxford University Press.National Research Council [NRC]. (2000). Inquiry and the national science education standards. Wash-
ington, DC: National Academy Press.Ogbuehi, P. I., & Fraser, B. J. (2007). Learning environment, attitudes and conceptual development asso-
ciated with innovative strategies in middle-school mathematics. Learning Environments Research, 10,101–114.
Saunders, K. J., & Fisher, D. L. (2006). An action research approach with primary pre-service teachers toimprove university and primary school classroom environments. In D. L. Fisher & M. S. Khine (Eds.),Contemporary approaches to research on learning environments (pp. 247–272). Singapore: WorldScientific.
Schultz-Jones, B. A., & Ledbetter, C. E. (2009, September). School libraries as learning environments:Examining elementary school students’ perceptions. Paper presented at the 38th Annual InternationalSchool Library Conference incorporating the 13th International Forum on Research in SchoolLibrarianship, Abano Terme, Italy.
Schultz-Jones, B. A., & Ledbetter, C. E. (2010, September). Assessing school libraries as learning envi-ronments: Examining students’ perceptions in third, fourth and fifth grades. Paper presented at the 39thAnnual International School Library Conference incorporating the 14th International Forum onResearch in School Librarianship, Brisbane, Australia.
Smith, E. (2001). Texas school libraries: Standards, resources, services, and students’ performance. Austin,TX: Texas State Library and Archives Commission.
Taylor, P. C., Dawson, V., & Fraser, B. J. (1995, April). Classroom learning environments under trans-formation: A constructivist perspective. Paper presented at the annual meeting of the AmericanEducational Research Association, San Francisco, CA.
Taylor, P. C., & Fraser, B. J. (1991, April). Development of an instrument for assessing constructivistlearning environments. Paper presented at the annual meeting of the American Educational ResearchAssociation, New Orleans, LA.
Taylor, P. C., Fraser, B. J., & Fisher, D. L. (1997). Monitoring constructivist classroom learning environ-ments. International Journal of Educational Research, 27, 293–302.
Telli, S., Cakiroglu, J., & den Brok, P. (2006). Turkish secondary education students’ perceptions of theirclassroom learning environment and their attitude towards biology. In D. L. Fisher & M. S. Khine(Eds.), Contemporary approaches to research on learning environments (pp. 517–542). Singapore:World Scientific.
Todd, R. J., & Kuhlthau, C. C. (2004). Student learning through Ohio school libraries. Ohio EducationalLibrary Media Association. Available at: http://www.oelma.org/studentlearning/.
von Glasersfeld, E. (1989). Cognition, construction of knowledge, and teaching. Syntheses, 80, 121–140.
Learning Environ Res (2013) 16:329–348 347
123
Wahyudi, & Treagust, D. F. (2006). Science education in Indonesia: A classroom learning environmentperspective. In D. L. Fisher & M. S. Khine (Eds.), Contemporary approaches to research on learningenvironments (pp. 221–246). Singapore: World Scientific.
Walberg, H. J., & Anderson, G. J. (1968). Classroom climate and individual learning. Journal of Educa-tional Psychology, 59, 414–419.
Wilson, J. T. (1974). Processes of scientific inquiry: A model for teaching and learning science. ScienceEducation, 58, 127–133.
Zandvliet, D. B., & Fraser, B. J. (2005). Physical and psychosocial environments associated with networkedclassrooms. Learning Environments Research, 8, 1–17. doi:10.1007/s10984-005-7951-2.
348 Learning Environ Res (2013) 16:329–348
123
