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The influence of teacher perceived administration of self-regulated learning on students' motivation and information-processing

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This study investigates the influence of teacher perceived administration of self-regulated learning on students' motivation and information-processing over time. This was done in the context of the Interactive Learning group System (ILS®): a large-scale innovation program in Dutch vocational schools. A total of 185 students were grouped post facto over contrasting groups, which differed in the adherence of teachers to vital, instructional principles based on self-regulated learning. Differences over time in student motivation and information-processing between these contrasting groups were explored.Mean differences as well as striking differences in relations between variables over time were found. Strong teacher adherence to the instructional principles of ILS was associated with a significant increase in deep-level processing and a positive relation between motivation and deep-level processing strategies over time.
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The influence of teacher perceived
administration of self-regulated
learning on students’ motivation
and information-processing
J.S. Rozendaal
a,
*, A. Minnaert
a,b
, M. Boekaerts
a
a
Leiden University, Center for the Study of Education and Instruction, Wassenaarseweg 52,
P.O. Box 9555, 2300 RB Leiden, The Netherlands
b
University of Groningen, Center for the Study of Education and Instruction, Wassenaarseweg 52,
P.O. Box 9555, 2300 RB Leiden, The Netherlands
Abstract
This study investigates the influence of teacher perceived administration of self-regulated
learning on students’ motivation and information-processing over time. This was done in the
context of the Interactive Learning group System (ILS
Ò
): a large-scale innovation program in
Dutch vocational schools. A total of 185 students were grouped post facto over contrasting
groups, which differed in the adherence of teachers to vital, instructional principles based on
self-regulated learning. Differences over time in student motivation and information-
processing between these contrasting groups were explored.
Mean differences as well as striking differences in relations between variables over time
were found. Strong teacher adherence to the instructional principles of ILS was associated
with a significant increase in deep-level processing and a positive relation between motivation
and deep-level processing strategies over time.
Ó2005 Elsevier Ltd. All rights reserved.
Keywords: Self-regulated learning; Motivation; Information-processing; Learning styles; Anxiety;
Educational innovation
* Corresponding author. Tel.: C31 71 527 3391; fax: C31 71 527 3398.
E-mail address: rozendaal@fsw.leidenuniv.nl (J.S. Rozendaal).
0959-4752/$ - see front matter Ó2005 Elsevier Ltd. All rights reserved.
doi:10.1016/j.learninstruc.2005.04.011
Learning and Instruction 15 (2005) 141e160
www.elsevier.com/locate/learninstruc
Knowledge nowadays tends to become obsolete very quickly due to rapid
technological changes, market changes, and continuous innovations in how work is
organized to keep pace with our turbulent society (Onstenk, 1998). Consequently,
schools emphasize that students should be equipped for self-regulated learning,
which has been defined as a learning process in which self-generated thoughts, feel-
ings, and actions are systematically oriented towards attainment of the student’s own
goals (Zimmerman & Schunk, 1989). To become self-regulated learners, students
should learn to regulate the use of information-processing modes, the learning
process, and the self (Boekaerts, 1999). By recognizing the importance of regulating
the self, the focus of research into self-regulated learning is shifting from studying
principally cognitive processes to studying cognition in interaction with motivation
(Rozendaal, Minnaert, & Boekaerts, 2001). Motivational processes have proven to
be important determinants of why students are or are not inclined to do what is
expected from them (Boekaerts, 1999). It is not at all clear, though, how self-
regulated learning can be promoted in the classroom. Researchers often try to
circumvent this difficulty by arguing that ‘powerful learning environments’ should be
created because they facilitate the acquisition of self-regulatory skills. But although
the term ‘powerful learning environments’ is often used, it is hard to find an
unambiguous definition. Subsequently, insights into the possible effects of self-
regulated-learning-based innovation programs are largely absent (Onstenk, 1998).
In an effort to contribute to the insight into how self-regulated learning may affect
the interplay of motivation and cognition, this study explores the influence of teacher
perceived administration of self-regulated learning on students’ motivation and
information-processing over time. This is done in the context of the Interactive
Learning group System (ILS): a large-scale self-regulated-learning-based innovation
in Dutch vocational schools in which our research group has been engaged since
1995. The innovation, which is in line with the presumptions of social
constructivism, aims at changing the behavior of students, teachers, and school
managers through the realization of a powerful learning environment (Boekaerts &
Minnaert, 2003).
1. Conceptual framework
1.1. Regulation of information-processing modes and the learning process
In the last 20 years, research in the field of learning patterns has identified several
characteristic ways of information-processing (e.g., Biggs, 1987; Entwistle, 1988;
Marton & Sa
¨ljo
¨, 1984; Pask, 1988; Vermunt, 1992). With the exception of Pask’s
research (1988), whose typologies differ from those of other researchers, two broad
characteristics in information-processing are recurrent in relevant research. The first
we refer to as surface-level processing, and comprises information-processing
strategies such as memorizing, repetition, and analyzing. The second we refer to as
deep-level processing, and comprises information-processing strategies such as
relating, structuring, and critical thinking.
142 J.S. Rozendaal et al. / Learning and Instruction 15 (2005) 141e160
Important meta-cognitive skills are orienting/planning, executing, monitoring/
testing/diagnosing, adjusting/correcting, and evaluating/reflecting (Brown, 1987;
Vermunt & Verloop, 1999; Weinstein & Mayer, 1986). A student is considered to be
‘self-regulating’ when the use of these skills is initiated by the individual rather than
by a teacher or peers.
Inspection of the strategy descriptions in both information-processing modes
reveals that conceptually, a sense of regulation is embedded in each information-
processing mode. More specifically, students who tend to stick to and memorize the
material presented to them by the teacher (a characteristic of the surface-level
approach; Schmeck, Geiser-Bernstein, & Cercy, 1991) rely on a form of external
regulation. In contrast, students who process knowledge critically by thinking along
with authors, teachers, and fellow students, thus verifying the coherence between the
knowledge presented and their prior knowledge (a characteristic of deep-level
processing; Vermunt & Verloop, 1999) show initiative and are likely to be more self-
regulating. The relation between information-processing modes and regulation has
also been demonstrated empirically, both nationally and internationally, and even
within the context of educational innovative learning environments (c.f., Boekaerts,
Otten, & Simons, 1997; Entwistle, 2001; Pintrich, 1999; Slaats, 1999; Vermunt &
Minnaert, 2003). A preference for surface-level processing is related to a need for
external regulation of the learning process (by teachers or peers). A preference for
deep-level processing is related to self-regulation of the learning process.
1.2. Regulation of the ‘self’
For a long time, theories of self-regulated learning have focused mainly on the
(meta-) cognitive aspects of the learning process. Recently, the insight emerged that
the ability to regulate the self (Boekaerts, 1999) functions as an important
determinant of learning behavior. Relevant control systems are motivational control
(Pintrich, 1989; Wigfield & Eccles, 2000), emotional control (Minnaert, 1999;
Pintrich & Schunk, 1996; Tobias, 1985), and action control (Boekaerts, 1997;
Heckhausen, 1991; Kuhl & Goschke, 1994). But researchers have not yet reached
consensus on which aspects of these control systems are the most relevant to self-
regulated learning. We had four constructs at our disposal, which were regarded by
educators and change agents as visible (through behavior) and as important
indicators of motivation. These are interest, persistence, test anxiety, and
performance anxiety. Interest is known to positively affect students’ information-
processing (Pintrich & de Groot, 1990; Schiefele, Krapp, & Winteler, 1992; Solomon
& Guthrie, 1999; Wolters & Pintrich, 1998). It refers to the student’s evaluation of
the assignments in terms of personal meaning. Persistence refers to the willingness to
continue with a task until it is completed, even when personal interest in this
particular assignment is low (Vermeer, 1997). Test anxiety refers to tension, concern,
worry, and feelings of nervousness associated with outcome expectation during test
taking. There is ample research indicating that test anxiety impedes performance and
acts upon cognitive and affective regulatory strategies (Benjamin, McKeachie, Lin, &
Holinger, 1981; Boekaerts, 1997; Culler & Holahan, 1980; Minnaert, 1999; Sarason,
143J.S. Rozendaal et al. / Learning and Instruction 15 (2005) 141e160
1984; Wolters & Pintrich, 1998). Another reason why students do not use adequate
strategies is that they suffer from performance anxiety. This form of social anxiety
refers to students’ emotions in the context of public performance, which may become
an important emotional variable ranking with test anxiety during collaborative and
self-regulated learning. Not much has been published about performance anxiety
and learning (Rozendaal, 2002), but for the present, we assume that test and
performance anxiety may affect information-processing in a comparable way.
1.3. The Interactive Learning group System (ILS)
The ILS innovation program aims at diminishing the student’s dependence on
teachers for learning by redefining the complementary roles of teachers and students
in such a way that teachers should limit their instruction time to make space for
students to experiment with and reflect on the subject matter in the collaborative
setting of interactive learning groups (Boekaerts, 1996). Eventually, ILS practice is
expected to increase motivation and deep-level processing in students, and to
decrease anxiety and surface-level processing.
At the start of the innovation program, teachers were trained to teach according
to the ‘Heptajump’ (Witteman, 1997). This is a sequence of self-regulated-learning-
based teacher characteristics, formulated as instructional principles, which sets
a standard for ‘a qualitatively good lesson’. Elements from the cognitive
apprenticeship learning theory of Collins, Brown, and Newman (1990) can be
found in the Heptajump, but ILS differs from this theory in that some of the
coaching in the ILS learning group is done by teachers as well as by fellow students
(apprentice to apprentice). The first principle is ‘prepare group assignments at home
and write them on the blackboard as soon as you enter the classroom’. In any lesson
this is the teacher’s first action. This ensures that teachers come to class with an
explicit plan and that students are aware of the amount of work to be done. The
students are homework free when they complete the specified tasks during the lesson,
which is considered a motivational incentive. The second principle is ‘activate
relevant prior knowledge’. This principle is considered a scaffold that supports the
construction of new learning (Alexander, 1996), since it creates the opportunity for
students to integrate new knowledge into prior knowledge (Dochy & Alexander,
1995). The third principle is ‘prepare the students for group assignments by providing
prior knowledge’. The teacher models the learning processes, making them more
transparent to students (Witteman, 1997). This principle ensures that students are
more actively involved in and responsible for their own learning, provided they are
invited to reflect on their own and other students’ task approaches. The fourth
principle is ‘invite students to work in interactive learning groups’. These
heterogeneous groups consist of four students with contrasting learning styles and
personalities to reflect cooperation in real-life work settings where people work
together, not on the primary basis of affection, but of quality. According to
Witteman (1997), ILS creates a learning scenario that allows students to develop
communication skills and the ability to take mutual responsibility. The assignments
presented to these interactive learning groups are typically ill-structured problems
144 J.S. Rozendaal et al. / Learning and Instruction 15 (2005) 141e160
that provoke collective discussion of ideas. The distribution of different qualities
over groups operating in the classroom is assumed to ensure that learning problems
are tackled from different angles, and that the heterogeneous qualities of the group
makes students better suited for a more complex learning environment. The
variation of learning styles across a learning group to a certain extent guarantees that
deficiencies in the nature and organization of knowledge bases of individual students
are ‘‘compensated by the higher quality of prior knowledge present in the knowledge
bases of their fellow students’’ (Witteman, 1997, p. 50). The reference in this citation
to the repair of conceptual deficiencies or cognitive change (Limo
´n, 2001) implies
that clashes of competing insights between students as provoked during collabora-
tive learning may generate a ‘cognitive conflict’ in students. This is expected to make
them more receptive to learning. In their synthesis of research literature on group
processes in the classroom, Webb and Palincsar (1996) argued that a moderate level
of overt cognitive conflict may act positively on cognitive development and that
collaborative learning creates the opportunity to co-construct knowledge that their
students did not have prior to collaboration. Furthermore, peers helping peers may
yield positive cognitive, motivational, and emotional effects for both the help giver
and receiver. During collaborative learning, the teacher coaches student behavior by
drawing students’ attention to relevant knowledge elements or learning strategies,
only assisting them when problematic steps have to be taken (Witteman, 1997). The
next principle is ‘the teacher gives feedback on what is learned at the end of a lesson’.
In most models of self-regulated learning, feedback aims at modeling desired
problem-solving behavior, but also at informing students about their motivation and
dedication for learning process (Boekaerts & Simons, 1995). In ILS, the teacher has
to check ‘‘the work done by the groups, and summarizes what the student (should)
have learned’’ (Witteman, 1997, p. 30). In fact, the meaning of feedback for the
learning process was not elaborated sufficiently at the time of implementation
(Rozendaal, 2002). The other instructional principles indicate that the teacher holds
diagnostic and summative evaluations at the end of a cycle of lessons. These
evaluations were not implemented at the time, since governmental assessment
criteria did not give the freedom to use evaluation criteria and measures in line with
the goals and procedures of ILS.
2. Research questions
Two research questions are central to this article. (1) Does reported adherence of
teachers to the instructional principles of the ILS innovation program affect the
students’ reports of motivation and information-processing over time? In line with the
objectives of ILS (Boekaerts & Minnaert, 2003; Witteman, 1997), we expect that
strong teacher adherence to these principles is associated with an increase in
students’ deep-level processing and motivation, on one hand, and a decrease in
students’ surface-level processing and anxiety, on the other. (2) Does adherence to the
instructional principles of the ILS innovation program affect the stability and cross-
lagged predictability of students’ reports of motivation and information-processing over
145J.S. Rozendaal et al. / Learning and Instruction 15 (2005) 141e160
time? According to Vermetten (1999), strategy use comprises a habitual as well as
a variable component. The habitual component in student learning can be
considered a person’s predisposition to learn in a consistent way, which is
comparable to a personal learning style. A significant part of the variance in
learning behavior over time is therefore ‘stable’ in terms of mean values, variances,
and cross-lagged predictability. The variable component may be explained by the
specific circumstances of the teaching context, which triggers students to use specific
learning strategies. In this study, the goal of instruction is essentially equated with
changing students’ motivation and the use of learning strategies. It is expected that
all the constructs will show a substantial level of stability over time, expressed in
testeretest values of 0.50 or higher, but strong teacher adherence to ILS is expected
to be associated with an increased malleability of motivation and learning behavior.
This malleability is evidenced by decreases in testeretest stability in favor of cross-
lagged predictability between constructs over time, which differ between students in
different instructional contexts. Under the influence of strong adherence to ILS, it is
expected that motivation becomes positively associated with deep-level processing
over time, rather than with surface-level processing.
3. Method
3.1. Sample
Three secondary vocational colleges participated in this study (Agricultural,
Economics, and Business Education). Completion of the questionnaires was
compulsory for students, since the results were used to compose the interactive
learning groups. However, due to a number of version changes, only a limited
number of students completed the same questionnaires at two measurement
occasions (November 1998 and May 1999). The final sample consisted of 185 valid
cases, divided over 14 classes. 75 of them were females, and 107 were males. The
mean age was 16.89 with a standard deviation of 0.85.
The teachers were requested to complete a self-report questionnaire that was
distributed to them by a contact person in the schools. The questionnaires were
returned directly to the researchers by mail. Teachers were reminded three times.
Ultimately, 86% of the teacher population (nZ55) responded to our questionnaire.
3.2. Variables
To obtain information about teacher behavior related to the intended educational
innovation strategy, the teachers were asked retrospectively how frequently they had
taught according to the set of ILS instructional principles, as well as how frequently
they had used the single instructional principles in their lessons in the past year
(assign tasks, activate prior knowledge, provide resources, collaborative learning,
coaching, feedback). The frequency of the use of each principle was assessed by a
146 J.S. Rozendaal et al. / Learning and Instruction 15 (2005) 141e160
5-point scale item. These seven items were used as indicators of teachers’ adherence
to the instructional principles.
The concepts that were used at the student level were chosen by educators and
change agents involved in the ILS innovation program. The scales originated from
the ‘Selector UL-98’ (Boekaerts, Minnaert, & Witteman, 2001), and the ‘Motiva-
tional Aspects of Learning in Groups’ self-report questionnaire (MALIG; Boekaerts
& Kooijman, 1996). These instruments were especially created for students in
secondary vocational education by change agents who monitored the innovation
process in the schools, in cooperation with researchers from the Department of
Education at the Leiden University. Therefore, our influence on the kind and quality
of the variables was limited. The primary function of the instruments is to make
a quick estimation of the students’ motivation and preferences for modes of
information-processing by using concepts that were identified by the change agents
and educators involved in the program. Our data included measures of surface-level
processing (SLP, aZ0.67, 5 items, example: ‘‘To me, memorizing is the best way to
really learn the subject matter’’) and deep-level processing (DLP, aZ0.71, 4 items,
example: ‘‘I try to relate new subject matter to what I already know’’), test anxiety
(TA, aZ0.83, 6 items, example: ‘‘When taking tests, I feel insecure’’), performance
anxiety (PA, aZ0.67, 5 items, example: ‘‘I don’t like to show my ability for X’’),
interest (INT, aZ0.69, 3 items, example: ‘‘I think the courses I follow are
interesting’’), and persistence (PER, aZ0.67, 5 items, example: ‘‘When I am not
interested in the subject matter, I postpone the tasks for X as long as possible’’). The
MALIG items (performance anxiety and persistence) are domain specifically stated.
Students were asked to answer these items for math or math-related tasks.
The variables were placed in a measurement model, with test and performance
anxiety contributing to a second-order ‘anxiety component’, and interest and
persistence contributing to a second-order ‘task investment component’. The latter
component refers to the willingness of students to make an investment in the task as
a result of their engagement in school tasks. This model was successfully tested for
invariance for gender, students with different preferences for modes of information-
processing (Rozendaal et al., 2001) as well as for domain differences (Rozendaal,
2002).
4. Results
4.1. Creation and analysis of contrasting groups
The data lacked a real control group and the number of observations on each
level was insufficient for multi-level analysis. Therefore, a cluster-analytical approach
to studying multi-layered effects of complex educational innovations on student
learning was devised (Rozendaal, 2002). This approach uses the ‘natural’ variance in
teachers’ reports of the realization of the instructional principles to differentiate
students post facto over contrasting groups. The classes in our sample were all
taught by more than one teacher, thus average teacher team scores per class were
147J.S. Rozendaal et al. / Learning and Instruction 15 (2005) 141e160
used. Classes of students were allocated to contrasting groups on the basis of
information obtained from at least 66.67% of their teachers. Rozendaal (2002)
explored the possibilities of forming various ‘sets’ of contrasting groups by using
various (combinations of) ILS principles as differentiating criterion. In the present
study, contrasting groups were created by means of hierarchical cluster-analysis
(Everitt, 1993) with the level of teacher adherence to the ILS principles as a set. This
resulted in three contrasting groups (Table 1).
The differences between teachers in these groups were studied by means of a one-
way ANOVA, with the exception of the variables with non-homogenous variances
across groups as indicated by a Levene homogeneity of variance test (assign tasks,
limit instruction time, collaborative learning, use of instructional principles as a set).
For the latter variables, the non-parametric KruskaleWallis one-way analysis of
variance was performed. Obviously, the groups differed significantly on teacher
adherence to the ILS instructional principles as a set (c
2
Z9.18, df Z2, p!0.05)
but also with respect to the frequency of collaborative learning (c
2
Z8.62, df Z2,
p!0.05).
Wilcoxon’s signed rank tests were performed to examine mutual differences
between the remaining variables in pairs. These tests indicated that teachers reported
significantly less feedback in comparison to the other principles (z
assign tasks
Zÿ5.39,
z
prior knowledge
Zÿ5.75, z
limit instruction
Zÿ5.41, z
feedback
Zÿ8.03, p!0.01) and
significantly more coaching in comparison to the other principles (z
assign tasks
Z
ÿ2.70, z
prior knowledge
Zÿ5.84, z
limit instr uction
Zÿ4.23, z
coaching
Zÿ8.03, p!0.01).
4.2. Mean differences over time
The ‘strong adherence’ group consisted of 65 students divided over 4 classes.
The ‘weak adherence’ group consisted of 67 students divided over 6 classes. The
‘ambiguous adherence’ group consisted of 50 students divided over 4 classes. This
group functions as a ‘gray area’, which is maintained primarily to enlarge the
Table 1
Means, standard deviations, and homogeneity of variance statistics of teachers’ adherence to ILS in the
three contrasting groups (nZ55)
Teacher adherence
to ILS instructional principles
Levene
statistic
p
Total
M
(Sd)
Strong
M
(Sd)
Ambiguous
M
(Sd)
Weak
M
(Sd)
All principles 3.30 (1.22) 3.85 (0.73) 3.52 (0.94) 2.79 (1.46) 13.63 0.00
Assign tasks 3.58 (1.30) 4.00 (0.80) 3.71 (1.12) 3.23 (1.57) 12.21 0.00
Activate prior knowledge 3.81 (0.68) 3.65 (0.56) 3.71 (0.68) 3.98 (0.73) 0.32 0.73
Limit instruction time 3.65 (0.95) 3.65 (0.98) 3.85 (0.89) 3.50 (0.98) 1.00 0.37
Collaborative learning 3.82 (1.12) 4.31 (0.74) 3.94 (0.95) 3.34 (1.30) 6.79 0.00
Coaching 4.42 (0.65) 4.54 (0.71) 4.47 (0.62) 4.30 (0.64) 0.26 0.77
Feedback 3.08 (0.92) 3.00 (1.02) 3.09 (0.87) 3.11 (0.92) 0.41 0.67
148 J.S. Rozendaal et al. / Learning and Instruction 15 (2005) 141e160
contrast between the two extreme groups (Rozendaal, 2002). In Table 2, for each
of the contrasting groups, means and standard deviations are displayed on T1
and T2.
A series of six repeated measures ANOVAs was performed with ‘measurement
occasion’ defined as within-subject factor, ‘adherence’ defined as between-subject
factor, and respectively, surface-level processing, deep-level processing, test anxiety,
performance anxiety, interest, and persistence as dependent variables. Reported are
Wilks’ ls (a test statistic used in multivariate analysis of variance to test whether
there are differences in the means of identified groups of subjects on the combination
of dependent variables), the Fand pstatistic, effect size (expressed in ETA squared),
and statistical power. There were no significant main effects, but a significant
interaction effect was found for deep-level processing (lZ0.97, F(2,182) Z3.12,
p!0.05, h
2
Z0.03, power Z0.59), performance anxiety (lZ0.96, F(2,182) Z3.37,
p!0.05, h
2
Z0.04, power Z0.63), and interest (lZ0.96, F(2,182) Z3.20, p!
0.05, h
2
Z0.04, power Z0.61). Next, a Tukey’s HSD post hoc analysis was
performed to test the significance of the differences between groups in mean changes
over time. The increase in deep-level processing in the ‘strong adherence’ group in
comparison to the decrease of deep-level processing in the ‘weak adherence’ group
over time was significant at the p!0.05 level. The increase of performance anxiety
in the ‘ambiguous adherence’ group in comparison to decrease in performance
anxiety in the ‘weak adherence’ group was significant at the p!0.05 level. None of
the mean changes in interest over time differed significantly between the groups.
4.3. Disattenuated factor correlations between latent variables
Disattenuated factor correlations (i.e., coefficients from which the measurement
error is removed; Schumacher, 1996) were computed between latent variables for
every group. This was done by using structural equation modeling. For these
analyses, the Maximum Likelihood estimation algorithm was used. Covariances
Table 2
Means and standard deviations of students in the strong, ambiguous, and weak teacher adherence groups
on T1 and T2
Group SLP M(Sd) DLP M(Sd) TA M(Sd) PA M(Sd) INT M(Sd) PER M(Sd)
Strong teacher adherence to instructional principles (nZ65)
T1 3.26 (0.78) 3.52 (0.69) 2.33 (0.76) 2.21 (0.65) 3.30 (0.85) 3.56 (0.64)
T2 3.35 (0.80) 3.68 (0.64) 2.37 (0.79) 2.36 (0.70) 3.04 (0.90) 3.34 (0.71)
Ambiguous teacher adherence to instructional principles (nZ50)
T1 3.41 (0.70) 3.42 (0.45) 2.68 (0.77) 2.29 (0.60) 3.19 (0.78) 3.40 (0.58)
T2 3.35 (0.64) 3.56 (0.54) 2.68 (0.74) 2.54 (0.74) 3.16 (0.68) 3.42 (0.67)
Weak teacher adherence to instructional principles (nZ67)
T1 3.43 (0.66) 3.60 (0.57) 2.33 (0.80) 2.28 (0.57) 3.16 (0.76) 3.47 (0.57)
T2 3.32 (0.71) 3.50 (0.61) 2.30 (0.89) 2.18 (0.79) 3.16 (0.65) 3.40 (0.64)
Note: SLP, surface-level processing; DLP, deep-level processing; TA, test anxiety; PA, performance
anxiety; INT, interest; PER, persistence.
149J.S. Rozendaal et al. / Learning and Instruction 15 (2005) 141e160
were used in the estimation procedure. For all these analyses EQS for Windows
version 6.1 was used. The results are displayed in Tables 3e5.
The statistical power did not allow multi-group comparisons. Therefore,
correlations between the latent variables over time were tested one by one for
significant differences between the contrasting groups (one-sided). Surface-level
processing was significantly more stable over time in the ‘strong’ (r
1
Z0.66) than in
the ‘ambiguous adherence’ group (r
2
Z0.43, r
1
ÿr
2
Z0.23, zZ1.72, pZ0.04).
Anxiety was significantly more stable over time in the ‘weak’ (r
1
Z0.81) than in the
‘ambiguous adherence’ group (r
2
Z0.66, r
1
ÿr
2
Z0.15, zZ1.74, pZ0.04). The
correlation between deep-level processing at T1 and surface-level processing at T2
was significantly stronger in the ‘ambiguous adherence’ group (r
1
Z0.31) than in
the ‘strong adherence’ group (r
2
Zÿ0.03, r
1
ÿr
2
Z0.34, zZ1.81, pZ0.04), and
the ‘weak adherence’ group (r
3
Zÿ0.14, r
1
ÿr
3
Z0.45, zZ2.40, pZ0.01). The
correlation between task investment at T1 and surface-level processing at T2 was
significantly stronger in the ‘ambiguous adherence’ group (r
1
Z0.48) than in the
‘strong adherence’ group (r
2
Z0.13, r
1
ÿr
2
Z0.35, zZ2.03, pZ0.02). The
correlation between deep-level processing at T1 and task investment at T2 was
significantly stronger in the ‘strong adherence’ group (r
1
Z0.43) than in the ‘weak
adherence’ group (r
2
Z0.13, r
1
ÿr
2
Z0.30, zZ1.85, pZ0.03).
4.4. Path analysis
Subsequently, paths between the latent variables over time were modeled
according to the significant estimates in the correlation matrices. The analyses were
run, insignificant paths were removed, and the analyses were run again. Hence, the
final models show only paths with substantial and significant regression coefficients,
which is of major importance given the small sample sizes of the contrasting groups.
Table 3
Disattenuated factor correlations between latent variables over time for students who experienced strong
teacher adherence to the instructional principles (nZ65)
Strong teacher adherence T1 T2
SLP DLP A TI SLP DLP A TI
T1
SLP 1.00
DLP ÿ0.03 1.00
A 0.21 ÿ0.07 1.00
TI 0.13 0.30* ÿ0.08 1.00
T2
SLP 0.66** ÿ0.03 0.19 0.13 1.00
DLP 0.04 0.58** ÿ0.08 0.38** 0.14 1.00
A 0.19 ÿ0.21 0.74** ÿ0.14 0.08 ÿ0.20 1.00
TI 0.08 0.43** ÿ0.05 0.65** 0.11 0.47** ÿ0.22 1.00
Note: *p!0.05; **p!0.01.
SLP, surface-level processing; DLP, deep-level processing; A, anxiety; TI, task investment.
150 J.S. Rozendaal et al. / Learning and Instruction 15 (2005) 141e160
Nevertheless, caution should still be exercised when interpreting the reported results,
which are meant for the purpose of generating hypotheses only and should not be
regarded as confirmatory.
As the main indication of model fit, the ratio of chi-square to the degrees of
freedom was used. In contrast to chi-square or the p-statistic, c
2
/df-measure is less
sensitive to group sizes and departures from normality (Byrne, 1989; Marsh &
Hocevar, 1985). According to Byrne (1989),ac
2
/df-ratio equal or below 2 can be
considered a good fit. According to Marsh and Hocevar (1985), a reasonable fit is
indicated by a c
2
/df within the interval between 2 and 5. The comparative fit index
Table 4
Disattenuated factor correlations between latent variables over time for students who experienced
ambiguous teacher adherence to the instructional principles (nZ50)
Ambiguous teacher adherence T1 T2
SLP DLP A TI SLP DLP A TI
T1
SLP 1.00
DLP 0.47** 1.00
A 0.10 ÿ0.18 1.00
TI 0.38* 0.29 ÿ0.13 1.00
T2
SLP 0.43** 0.31* 0.20 0.48** 1.00
DLP 0.17 0.41** ÿ0.29 0.13 0.21 1.00
Aÿ0.04 ÿ0.31* 0.66** ÿ0.10 0.09 ÿ0.40** 1.00
TI 0.17 0.16 ÿ0.01 0.57** 0.27 0.28 0.06 1.00
Note: *p!0.05; **p!0.01.
SLP, surface-level processing; DLP, deep-level processing; A, anxiety; TI, task investment.
Table 5
Disattenuated factor correlations between latent variables over time for students who experienced weak
teacher adherence to the instructional principles (nZ67)
Weak teacher adherence T1 T2
SLP DLP A TI SLP DLP A TI
T1
SLP 1.00
DLP 0.03 1.00
A 0.04 ÿ0.22 1.00
TI 0.29* 0.09 0.11 1.00
T2
SLP 0.60** ÿ0.14 0.20 0.33* 1.00
DLP 0.18 0.51** ÿ0.11 0.17 0.21 1.00
A 0.15 ÿ0.15 0.81** 0.04 0.11 ÿ0.12 1.00
TI 0.23 0.13 0.20 0.68** 0.26* 0.13 0.22 1.00
Note: *p!0.05; **p!0.01.
SLP, surface-level processing; DLP, deep-level processing; A, anxiety; TI, task investment.
151J.S. Rozendaal et al. / Learning and Instruction 15 (2005) 141e160
(CFI; Bentler, 1988) is given as an additional indication of model fit. This index also
reflects the model fit relatively well at all sample sizes.
The model of the ‘strong adherence’ group (Fig. 1) had a good fit (c
2
Z97.50,
df Z49, c
2
/df Z1.99, CFI Z0.83). The test and retest values in the model ranged
from 0.52 to 0.74, and were all significant at the p!0.01 level (t
slp
(49) Z6.97,
r
bslp
Z0.66, t
dlp
(49) Z5.65, r
bdlp
Z0.56, t
anx
(49) Z8.92, r
banx
Z0.74, t
ti
(49) Z5.25,
r
bti
Z0.52, p!0.01). Furthermore, a significant part of the variance in task
investment at T2 could be predicted from deep-level processing at T1 (t(49) Z2.53,
r
b
Z0.25, p!0.05). Sadly, the path between task investment at T1 and deep-level
processing at T2 as indicated by the disattenuated factor correlations was only
nearly significant.
The model of the ‘ambiguous adherence’ group (Fig. 2) also had a good fit
(c
2
Z90.59, df Z49, c
2
/df Z1.85, CFI Z0.78). The test and retest values in the
model ranged from 0.31 to 0.66, and were all significant at the p!0.01 level
(t
dlp
(49) Z3.06, r
bdlp
Z0.40, t
anx
(49) Z6.13, r
banx
Z0.66, t
ti
(49) Z4.43, r
bti
Z0.53),
except for surface-level processing (t
slp
(49) Z2.44, r
bslp
Z0.31, p!0.05). Further-
more, a significant part of the variance in surface-level processing at T2 could be
predicted from task investment at T1 (t(49) Z2.69, r
b
Z0.34, p!0.01). Note that the
contribution of persistence to the task investment component at T2 is negligible.
The data of the ‘weak adherence’ group yielded only a stability model (Fig. 3)
which had a good fit (c
2
Z68.92, df Z50, c
2
/df Z1.38, CFI Z0.92). The test and
retest values in the model ranged from 0.57 to 0.80, and were all significant at the
p!0.01 level (t
slp
(50) Z5.64, r
bslp
Z0.57, t
dlp
(50) Z5.23, r
bdlp
Z0.54, t
anx
(50) Z
10.74, r
banx
Z0.80, t
ti
(50) Z6.11, r
bti
Z0.61, p!0.01).
SLP
r²=.43
.88e
.84e
.77e
.83e
1.00
1.00
1.00
1.00
1.00
.64
.56
1.00
1.00
1.00
.48
.54
.66**
.58**
.74**
.52**
DLP
r²=.33
A
r²=.55
TI
r²=.41
T1 T2
NOTE. *
p
<.05. **
p
<.01
STRONG TEACHER AHDERENCE TO THE
ILS-INSTRUCTIONAL PRINCIPLES (n=65)
.25*
DLP
SLP
TA
PA
INT
PER
DLP
SLP
TA
PA
INT
PER
SLP
DLP
A
TI
Fig. 1. Structural equation model for the estimation of the relations between latent variables at T1 and T2
for students who experienced strong teacher adherence to the instructional principles. SLP, surface-level
processing; DLP, deep-level processing; TA, test anxiety; PA, performance anxiety; INT, interest; PER,
persistence; A, anxiety; TI, task investment.
152 J.S. Rozendaal et al. / Learning and Instruction 15 (2005) 141e160
5. Conclusion and discussion
In this study, the influence of teacher perceived administration of self-regulated
learning on students’ motivation and information-processing was explored. This was
done in the context of the Interactive Learning group System (ILS), a Dutch
NOTE. *
p
<.05. **
p
<.01
AMBIGUOUS TEACHER ADHERENCE TO THE
ILS-INSTRUCTIONAL PRINCIPLES (n=50)
SLP
r²=.29
.74e
.94e
.71e
.99e
1.00
1.00
1.00
1.00
1.00
.70
.05
1.00
1.00
1.00
.67
.34
.34**
DLP
r²=.16
A
r²=.43
TI
r²=.28
T1 T2
DLP
SLP
TA
PA
INT
PER
DLP
SLP
TA
PA
INT
PER
SLP
DLP
A
TI
.31*
.40**
.66**
.53**
Fig. 2. Structural equation model for the estimation of the relations between latent variables at T1 and T2
for students who experienced ambiguous teacher adherence to the instructional principles.
NOTE. *
p
<.05. **
p
<.01
.81e
.93e
.73e
.89e
1.00
1.00
1.00
1.00
1.00
.69
.46
1.00
1.00
1.00
.59
.36
.57**
.54**
.80**
.61**
WEAK TEACHER ADHERENCE TO THE
ILS-INSTRUCTIONAL PRINCIPLES (n=67)
T1 T2
DLP
SLP
TA
PA
INT
PER
DLP
SLP
TA
PA
INT
PER
SLP
DLP
A
TI
SLP
r²=.33
DLP
r²=.29
A
r²=.64
TI
r²=.37
Fig. 3. Structural equation model for the estimation of the relations between latent variables at T1 and T2
for students who experienced weak teacher adherence to the instructional principles.
153J.S. Rozendaal et al. / Learning and Instruction 15 (2005) 141e160
self-regulated-learning-based innovation program in secondary vocational educa-
tion. Contrasting groups of students were created using self-reports of the teachers’
adherence to the ILS-program. In the first section, between-group differences at the
teacher level are discussed to provide insight in the meaning of the contrasts between
the three groups (strong, ambiguous, and weak teacher adherence to ILS).
Subsequently, mean differences and relational differences between students in the
contrasting groups are discussed. It was expected that strong teacher adherence to
ILS is associated with an increase in students’ deep-level processing and motivation,
on one hand, and a decrease in students’ surface-level processing and anxiety, on the
other. Furthermore, it was expected that under the influence of strong adherence to
ILS, students’ motivation becomes positively associated with deep-level processing
over time, rather than with surface-level processing.
5.1. Between-group differences at the teacher-level
Three contrasting groups were formed with the teachers’ reported adherence to all
ILS instructional principles as differentiating criterion. Inspection of the differences
between the contrasting groups on the teacher level revealed that besides the
differentiating criterion, the contrasting groups differed only in teacher adherence to
collaborative learning. The teachers reported relatively frequent use of coaching
activities during collaborative learning, infrequent use of whole class feedback, and
average adherence to the other principles (prepare group assignments, activate prior
knowledge, and provide resources) across all contrasting groups. In our sample, the
variance in adherence to ILS equals more or less the variance in implementation of
collaborative learning by teachers. This is understandable, since the implementation
of collaborative learning is the most ‘visible’ and ‘deepest’ change our teachers had
to make. The relatively strong adherence to coaching may be explained by the fact
that teachers in general are very concerned with their pupils, independent of the
instructional arrangement. Giving feedback at the end of the lesson was relatively
neglected by most teachers. As mentioned, the provision of feedback was not worked
out elaborately in ILS (Witteman, 1996, 1997) and in the accompanying training
program at the time. Nevertheless, it is of great importance that teachers and change
agents realize that feedback is inherent in and a prime determinant of processes that
constitute self-regulated learning (Butler & Winne, 1995; Rozendaal, 2002).
5.2. Between-group differences at the student level
5.2.1. Information-processing
Students who were taught by a teacher population with a relatively strong
adherence to the ILS instructional principles reported a significantly larger increase
of deep-level processing over time in comparison to students in the ‘weak adherence’
group. This finding is in line with expectations of the impact of self-regulated
learning as articulated by Boekaerts (1996), and the findings on the effect of ILS by
Witteman (1997). Contrary to the findings of Witteman (1997), surface-level
154 J.S. Rozendaal et al. / Learning and Instruction 15 (2005) 141e160
processing tended to increase with deep-level processing in the ‘strong adherence’
group over time. This finding is in line with the assertion of Rozendaal et al. (2001)
that surface and deep-level processing are not ends of the same continuum and can
be used alternately. Surface-level processing may function as a precondition for
deep-level understanding, as in science courses, for instance, which sometimes ‘‘have
to be taken on trust and memorized as you go along until you get to the point where
they make more sense’’ (Palmer, 1998, p. 4), or as a finalization of the learning
process when one has studied the subject matter in-depth and memorizes the
outcomes (Lindblom-Yla
¨nne, 2003). In line with our expectations, path analyses
indicated that in the ‘strong adherence’ group, students’ deep-level processing at T1
predicted a part of the variance in task investment at T2. The disattenuated factor
correlations also indicated a significant relation between task investment at T1 and
deep-level processing at T2, which was nearly significant in the model. By contrast, in
the ‘weak adherence’ group, only a stability model was found without any cross-
lagged paths between the latent variables. The disattenuated factor correlations also
indicated a significant relation between task investment at T1 and surface-level
processing at T2, which was nearly significant in the measurement model. In the
‘ambiguous adherence’ group, a part of the variance of surface-level processing at T2
was predicted from task investment at T1. According to the disattenuated factor
correlations, deep-level processing at T1 and surface-level processing at T2 were
significantly correlated. This path, however, was nearly significant in the model. In
this group, surface- and deep-level processing were less stable over time in
comparison to the other groups. It is possible that the ambiguity of the teachers
towards ILS in this group was reflected by relative instability of students’
information-processing.
Although the present design does not allow unequivocal causal inferences, given
these findings, strong teacher adherence to ILS as interpreted by ‘our’ teachers,
seems to yield an increase of deep-level processing in students, and may yield the
possibility of a reciprocal and positive relation between deep-level processing and
task investment over time. In contrast, task investment is rather more likely to be
a predictor of surface-level processing in students who experience relatively
ambiguous or weak adherence to ILS. An important question is whether the
promotion of the predominant use of deep-level processing strategies should be
considered an important goal for students in secondary vocational education. At the
beginning of the implementation of ILS we thought it was, given national and
international reports indicating that students focused too narrowly on memoriza-
tion, instead of critical thinking and in-depth knowledge of subject matter
(Boekaerts et al., 1997; National Research Council [NRC], 1999). Over the years,
however, we have gained a more elaborate understanding of self-regulated learning
and the importance of ‘perception of choice’ (Winne & Perry, 2000). Therefore, in
establishing self-regulated-learning-based learning environments, it is of great
importance that we try to provoke the use of a broad variety of information-
processing strategies in students and teach them how to activate these strategies
appropriately, rather than to impose the use of deep-level processing at the expense
of surface-level processing.
155J.S. Rozendaal et al. / Learning and Instruction 15 (2005) 141e160
5.2.2. Motivation
The changes in interest and persistence over time did not differ significantly
between the contrasting groups. These results indicated that self-regulated-
learning-based environments may not necessarily be more motivating to all
secondary vocational students than direct instruction. However, the disattenuated
factor correlations and cross-lagged paths between task investment and in-
formation-processing over time in the three models indicated that strong adherence
to ILS is associated with a reciprocal relation between deep-level processing and
student task investment. Likely, student motivation may function as an important
precondition for higher order learning processes to occur. Thus, the results
presented in this paper underline the importance of motivational variables as well
as cognitive variables for explaining and changing student learning behavior over
time. For many secondary vocational schools, this means that solving motivational
problems may present a more urgent problem rather than the lack of deep-level
processing in students. On the other hand, if the prerequisite of student
motivation is met, the ILS instructional principles form an interesting instructional
format in which students are provoked to interact more deeply with the subject
matter.
5.2.3. Anxiety
Students who experienced ambiguous teacher adherence to the ILS instructional
principles reported a larger increase of performance anxiety than students in the
‘weak adherence’ group. This indicates that ambivalent teacher adherence to ILS
was associated with feelings of discomfort in students. The changes in test anxiety
over time did not differ significantly between the contrasting groups. Path analysis
indicated that anxiety appeared relatively unimpaired by the various conditions over
time. Thus contrary to our expectations, self-regulated-learning-based innovation
programs may not be able to solve problems of anxiety and insecurity (formerly)
associated with direct teaching. Even in self-regulated-learning-based innovation
programs, various forms of anxiety are detrimental to the development of self-
regulatory skills and social competencies over time. Most of the time, the anxiety was
positively correlated with surface-level processing and negatively with deep-level
processing. One explanation could be that surface-level processors are more anxious
about the exact nature of tests or other tasks than their deep-level processing
counterparts because of their lack of understanding of the subject matter (Palmer,
1998). Another explanation could be that a mild form of anxiety may boost surface-
level processing: students are aroused and under influence of this arousal they are
able to concentrate and memorize the subject matter better (Benjamin et al., 1981).
When the latter hypothesis is true, we expect that comparable mild anxiety may
block the use of deep-level processing: students are afraid to show initiative because
they are concerned with making mistakes. Likely, deep-level processing is only apt to
thrive in a relatively safe school climate in which student initiative is encouraged and
valued.
156 J.S. Rozendaal et al. / Learning and Instruction 15 (2005) 141e160
5.3. Limitations and future directions
This study has a number of limitations and consequently, the results should be
regarded as exploratory and for the purpose of generating hypotheses. All results
were based on self-ratings. On the teacher level, this may have led to an exaggeration
of the teachers’ reports of their adherence to ILS. And since the teachers’
implementation of ILS was assessed retrospectively, teachers could have included
perceptions of the results of their implementation. Yet, the compared contrasting
groups were created on the basis of relative differences between teachers from the
same ‘biased’ population. Therefore, the results in this study are still meaningful.
However, future research on this topic would gain validity by using groups that are
formed on the basis of observations of teacher behavior by researchers. Then it is also
possible to differentiate students over contrasting groups that differ in the quality of
instruction, rather than the frequency of the teachers’ use of the ILS instructional
principles. Furthermore, the use of multi-level techniques should be considered. Such
techniques would also demand larger sample sizes at the various measurement levels.
This also magnifies the statistical power of between-group differences that could only
be mentioned cautiously in this study. As explained in Section 3, we had little
influence on the kind and quality of variables that were used on the student level. It is
important for future research to examine whether the results presented in this study
are also found when scales from internationally acknowledged instruments like the
ASSIST (Entwistle & Tait, 1996), LASSI (Weinstein, Zimmerman, & Palmer, 1988),
MSLQ (Pintrich, Smith, Garcia, & McKeachie, 1993), and the Inventory of Learning
Styles (Vermunt & Verloop, 1999) are used. Furthermore, the design could be
enriched with vital constructs like self-efficacy, task value, goal orientation, and meta-
cognition. Finally, it would be interesting to investigate whether the current results
pertain only to the heterogeneous groups as composed in ILS or that they pertain to
other group compositions as well, and whether self-regulated-learning-based
environments yield improvements in student achievement.
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... In addition, when such a schoolwide program is missing, primary school teachers feel like they are left on their own with regard to differentiation (Gheyssens et al., 2023). Furthermore, within pre-vocational education, Rozendaal et al. (2005) found that only when teachers strongly adhere to instruction and differentiation, students experience long-term positive impacts on motivation and learning. Conversely, weak or moderate adherence results in detrimental effects. ...
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... Vor diesem Hintergrund konnte van Velzen (van Velzen, 2004;van Velzen/ Tillema, 2004) allerdings zeigen, dass Berufsschüler:innen im Unterricht selten Gelegenheit zur Selbstreflexion durch Lehrpersonen erhalten. Dies führt zu einer geringeren Nutzung metakognitiver Regulationsstrategien seitens der Lernenden und schlägt sich in der Folge in unterschiedlichen Zielsetzungen von Berufsschüler:innen und Lehrpersonen nieder (Rozendaal et al., 2005). Dabei zeigt sich, dass bereits die bloße Anregung zur Selbstreflexion der Berufsschüler:innen durch die Lehrkräfte zur Förderung der Selbstreflexion selbst führt, wobei die Wahrnehmung der Lehrperson durch die Lernenden besonders relevant ist (van Velzen/Tillema, 2004). ...
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... In addition to fulfilling this need for social connection, teachers who consistently foster student collaboration for reading activities may also encourage deeper reading in addition to shared reading. Students are inspired to read more carefully and reflect more deeply on the text's meaning when they have a genuine reading purpose of sharing information and opinions about a text with others (Rozendaal et al., 2005). This indicates that students are more driven to devote cognitive effort to critical reading comprehension-related tasks like (a) identifying new vocabulary; (b) drawing local inferences that link information within the text; and (c) drawing global inferences that relate the text to prior knowledge and experience (Cain & Oakhill, 2014). ...
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This study explored the reading comprehension level of the grade 7 students at the University of La Salette, Incorporated High School. It utilized a quantitative, descriptive research design to analyze the data from the CEM Reading Test Level 3 of three hundred sixty-eight (368) grade 7 students who served as the sample population of this study. The result revealed the students performed better in the content areas of scanning, point of view, and literal compared to the norm group. However, students were rated low average in the following content areas: synonyms, literal, inferential, reorganization, summary cloze, references, labels, and tables. The CEM Reading Test Level 3 revealed that the students’ sub-test scores, percent correct scores, and standard score only ranged from low average to average. It also revealed that there are 32.88% of students who were classified in the range of low average to very poor level of reading comprehension. The study recommends that teachers focus on developing skills in vocabulary development and reading study aids and utilize the reading intervention program proposed by the researcher to determine its effectiveness in improving the reading comprehension level of the students.
... In this sense, it is found that the studies in [20] describe interventions that improved student self-regulation by combining training in self-regulation with the teaching of problem-solving, being especially effective in improving performance. In the case of [21], teachers who practised interactive and collaborative teaching strategies promoted deep cognitive processing in their students. Therefore, we can establish these determining factors to favour good self-regulation and, consequently, an improvement in student performance in the STEAM project "Machining in Ancient Egypt": problem-solving, self-assessment, metacognition, and collaborative learning. ...
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... Next to the effect sizes, Gaspard (2023) recommends more in-depth analyses of the fidelity of implementation, that is to what extent is the intervention implemented according to plan and which are the facilitating and debilitating factors in the implementation efficacy. This is undeniably of added value as teachers' level and quality of adherence to the intervention have strong positive effects on motivation in case of a strong adherence, but rather negative effects on motivation in case of a weak adherence (Rozendaal et al., 2005). So, checking for the fidelity of implementation is of utmost importance to estimate the true level of heterogeneity in intervention effects. ...
Chapter
The author considers all chapters of Section II, highlighting that the most underlying and unequivocal message is the well-determined shift in emotion and motivation research from static, variable-centred, highly controlled, single-method studies (like surveys, randomised-control trials, or laboratory experimental designs) to a holistic, multi-method approach, tapping the dynamics of emotions and motivation and capturing situated within-person next to between-person change in the real habitat of the person. Demonstrating how readers can strive towards this epistemological shift, the author reflects on the chapters first by addressing the major insights and by reasoning on the opportunities and challenges in the methodological zone of proximal research in motivation and emotion.
... Numerous researchers have agreed that classroom interventions should ideally be designed and implemented by teachers (Callan et al., 2022;Dignath & Veenman, 2021;Perry & VandeKamp, 2000;Rozendaal et al., 2005). It is because teachers are the most knowledgeable about their students and well-positioned to freely adjust details of interventions to their classroom contexts in an ecologically valid manner (De Corte, 2000). ...
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Background: Self-regulated learning has been deemed an essential skill that must be explicitly learned and repeatedly practiced for young students. The need for research on teacher-led self-regulated learning interventions embedded in regular classroom instructions has escalated steadily. Aims: We aimed to investigate self-regulated learning interventions' effectiveness led by teachers applicable to three primary subjects (i.e., writing, mathematics, reading) based on Zimmerman's cyclical model of self-regulated learning. Samples: 214 Korean upper elementary school students participated in a series of three intervention studies (n Study1 = 70, n Study2 = 69, n Study3 = 75). Methods: Trained homeroom teachers implemented the interventions-incorporating explicit instructions about domain-specific strategies in writing (Study 1), mathematics (Study 2), and reading (Study 3)-in Korean elementary school classrooms. Participants were assigned to one of the three groups: regular classroom instruction (REG), domain-specific strategy instruction (STR), and strategy instruction within the framework of eight-phase self-regulated learning instruction (STR + SRL). Results: Synthesized results revealed that the STR + SRL group used more self-regulated strategies, performed better in achievement tests, and was less distracted by task-irrelevant thoughts than the STR and REG groups. Conclusions: Our interventions are compatible with domain-specific instructions in multiple subjects and can guide and prompt self-regulatory learning processes in elementary classrooms. Current findings also reiterate the importance of the teachers' role in research-based interventions to increase ecological validity and applicability. We shed light on the potential mechanism that underlies the relationship between enhanced self-regulated learning and motivational and cognitive outcomes.
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It has been suggested that culturally relevant literature can be beneficial to elementary school students' learning. Yet, less research has focused on African American students' perspectives of that literature, including aspects of that engagement that may benefit their learning. Therefore, the main goal centred on US elementary school students' perspectives of African American children's literature in an after-school book club. There were 15 second- and third-grade African American students from a low-income area who participated in the 6-week book club. The book club sessions were recorded, student artefacts were collected and a focus group was held with students. Following the book club, there were two classroom teachers interviewed along with an after-school teacher facilitator. Based on the analysis, four themes were found. These focused on increased reading motivation, the role of cultural and personal associations with literature for comprehending, engagement in communal learning and improved access to culturally relevant texts. The results extend previous research on the importance of social collaboration and culturally relevant books to promote motivation and reading comprehension among learners and highlight the value of collaborative and culturally based learning for Black children in the American context.
Chapter
From the mid- to late 1960s, Brian Lewis, Bernard Scott, and I conjectured that learning strategies, teaching strategies, and even plans of action have characteristic types which can be differentiated (Lewis & Pask, 1964, 1965; Pask, 1961, 1970, 1972; Pask & Lewis, 1968; Pask & Scott, 1971, 1972, 1973). Individual difference psychologies have maintained a similar stance and with greater precision regarding the nature of strategies. An overview of the approach taken by my own group in the 1960s is described in the remainder of this section. Learning and teaching strategies can, under appropriate circumstances, be substantially exteriorized or externalized for observation. Protocols can serve this purpose, but we used maplike representations of what may be known or learned. These representations were open to continuous evolution as further topics and relations between them were added by learners.
Article
A correlational study examined relationships between motivational orientation, self-regulated learning, and classroom academic performance for 173 seventh graders from eight science and seven English classes. A self-report measure of student self-efficacy, intrinsic value, test anxiety, self-regulation, and use of learning strategies was administered, and performance data were obtained from work on classroom assignments. Self-efficacy and intrinsic value were positively related to cognitive engagement and performance. Regression analyses revealed that, depending on the outcome measure, self-regulation, self-efficacy, and test anxiety emerged as the best predictors of performance. Intrinsic value did not have a direct influence on performance but was strongly related to self-regulation and cognitive strategy use, regardless of prior achievement level. The implications of individual differences in motivational orientation for cognitive engagement and self-regulation in the classroom are discussed.