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Metacognition, Strategies, Achievement, and Demographics: Relationships Across Countries10.12738/estp.2016.5.0137

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Abstract

Learning strategies, such as memorization and elaboration strategies, have received both support and repudiation. The 2009 international PISA reading, science, and mathematics achievement test and survey of 15 year-olds in 65 countries was used. The findings indicated that self-reported use of learning strategies, which involve compensatory approaches like memorization, across a global sample was not strongly associated with higher achievement. However, metacognitive strategies which involve an awareness of thinking, as measured by the appropriate use of strategies within a context, were related to greater achievement. Although there were differences across gender and student SES, metacognitive strategies remained a significant predictor of achievement when controlling for SES and gender, and were on par with SES in predicting achievement. This study provides insight that may be particularly beneficial for males and lower SES students who underachieve in reading.
Received: February 6, 2016
Revision received: May 17, 2016
Accepted: July 4, 2016
OnlineFirst: August 10, 2016
Copyright © 2016 EDAM
www.estp.com.tr
DOI 10.12738/estp.2016.5.0137 October 2016 16(5) 14851502
Research Article
KURAM VE UYGULAMADA EĞİTİM BİLİMLERİ EDUCATIONAL SCIENCES: THEORY & PRACTICE
Citation: Callan, G. L., Marchant, G. J., Finch, W. H., & German, R. L. (2016). Metacognition, strategies, achievement,
and demographics: Relationships across countries. Educational Sciences: Theory & Practice, 16, 1485–1502.
1 Department of Educational Psychology, Ball State University, Educational Psychology Teachers College Muncie Indiana
47306-0001, United States. Email: glcallan@bsu.edu
2 Correspondence to: Gregory J. Marchant (PhD), Department of Educational Psychology, Ball State University, Educa-
tional Psychology Teachers College Muncie Indiana 47306-0001, United States. Email: gmarchant@bsu.edu
3 Department of Educational Psychology, Ball State University, Educational Psychology Teachers College Muncie Indiana
47306-0001, United States. Email: whfinch@bsu.edu
4 Department of Educational Psychology, Ball State University, Educational Psychology Teachers College Muncie Indiana
47306-0001, United States. Email: rlgerman@bsu.edu
Abstract
Learning strategies, such as memorization and elaboration strategies, have received both support and
repudiation. The 2009 international PISA reading, science, and mathematics achievement test and survey
of 15 year-olds in 65 countries was used. The findings indicated that self-reported use of learning strategies,
which involve compensatory approaches like memorization, across a global sample was not strongly
associated with higher achievement. However, metacognitive strategies which involve an awareness
of thinking, as measured by the appropriate use of strategies within a context, were related to greater
achievement. Although there were differences across gender and student SES, metacognitive strategies
remained a significant predictor of achievement when controlling for SES and gender, and were on par with
SES in predicting achievement. This study provides insight that may be particularly beneficial for males and
lower SES students who underachieve in reading.
Keywords
Learning strategies • Student achievement • International data (PISA) • Demographics • Socio-economic status
Gregory L. Callan1
Ball State University
Gregory J. Marchant2
Ball State University
W. Holmes Finch3
Ball State University
Rachel L. German4
Ball State University
Metacognition, Strategies, Achievement, and
Demographics: Relationships Across Countries
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The factors inuencing student learning is a shared concern internationally. The role
of student demographics, the nature of their schools, and the wealth and inequality of
their countries are established factors in academic achievement (Marchant & Finch,
2016). Identifying factors that impede learning is not a difcult task. Factors such
as poverty, discrimination, and inequality all undermine efforts to educate children;
however, it is more difcult to identify feasible and efcient solutions to improve
learning outcomes or overcome disadvantages. Short of one-to-one instruction, the
strategies students employ when approaching learning tasks may be one factor that
could offset some of the other universal negatives.
A large body of research has examined the use of academic strategies, which entail
a variety of tactics that may facilitate achievement (Boss & Vaughn, 2002; Ward &
Traweek, 1993; Zimmerman, 2002). Although, multiple perspectives are discussed
within the literature, and the terminology may differ slightly across these perspectives,
some of the most common types of strategies include cognitive and metacognitive
strategies (Cantrell et al., 2010; Pintrich, Smith, Garcia, & McKeachie, 1993). It should
be noted that there are differences in the terminology and classication of academic
strategies. Many have also classied various academic strategies into two broad
categories of learning strategies and metacognitive strategies (PISA, 2009; Woolfolk,
2014). In particular, this perspective is consistent with the Programme for International
Student Assessment (PISA, 2009) which served as the primary data source for this
study. From this perspective, learning strategies may entail both cognitive strategies
and control strategies that are used to optimize students’ learning of content. Cognitive
strategies include a variety of actions but some popular strategies include memorization,
elaboration, or summarization (PISA, 2009; Pintrich et al., 1993; Woolfolk, 2014). A
common theme among these strategies is that they enhance learning by compensating
for limitations on one’s cognitive abilities. For example, one may choose to use a
memorization strategy, such as rote repetition or creating an acronym, because without
the support of such a strategy, the number of pieces of information to be remembered
would exceed or strain the learner’s memory capacity. Relatedly, another cognitive
strategy, elaboration, entails creating connections between prior learning and new
information, which supports learning by capitalizing on cognitive predispositions to
remember content that is connected to prior knowledge.
On the other hand, control strategies have been dened in multiple ways within
the literature; however, PISA (2009) describes control strategies as the actions that
students take to identify the key purpose of a task or identify the main concepts. From
this perspective, control strategies are considered to be within the larger category
of learning strategies because the identication of key information should enhance
learning (Gardner, Brown, Sanders, & Menke, 1992).
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In contrast to learning strategies, another class of strategies within the PISA, 2009
measures include metacognitive strategies, which help a learner “think about his or her
thinking” (Bruning, Schraw, & Norby, 2011). For example, a metacognitive strategy
to check one’s understanding of a paragraph immediately after reading the paragraph
might increase the reader’s awareness that he or she did not understand the text.
Similarly, one may summarize a paragraph into their own words to monitor how well
they understood the text. A primary benet of metacognitive strategies is that increased
awareness, especially when a learner is struggling, provides an opportunity for the
learner to take actions, such as utilizing learning strategies, to improve learning.
Learning Strategies, Metacognitive Strategies, and Achievement
Research has been relatively consistent in showing that metacognitive strategies are
related to achievement and learning across many content areas, but especially reading,
mathematics, and science. In addition, these ndings have been found throughout the
world, rather than merely in the United States. In Vietnam, increased metacognitive
strategies from repeated-reading led to better awareness of the utility of reading uency
(Gorsuch & Taguchi, 2010). Training in metacognitive strategies resulted in better
achievement in fractional mathematics in Nigeria (Onu, Eskay, & Igbo, 2012). In Israel,
high school students who were taught to use metacognitive strategies during math
performed better than peers who did not receive this training (Mevarech & Amrany,
2008). In addition, primary school children in Great Britain who performed the best
at addition and subtraction, reportedly used more advanced metacognitive strategies
(Throndsen, 2011). High school students who were better at comprehending geometric
proofs were found to use more metacognitive strategies (Yang, 2012). When taught to
eighth graders in Israel, meta-strategic knowledge, which is described as explicit general
knowledge about thinking strategies, had dramatic short-term and long-term effects
on scientic inquiry learning (Ben-David & Zohar, 2009). In that study, the effect was
stronger for low-achieving students. In the United States, two measures of metacognitive
strategies were signicant predictors of middle school students’ achievement levels in
science (Sperling, Richmond, Ramsay, & Klapp, 2012). Measures of metacognitive
awareness during mathematical problem solving predicted achievement performance
in mathematical problem solving tasks as well as standardized test scores (Callan &
Cleary, 2014). The use of metacognitive prompts during science instruction resulted in
an increase in students’ scientic knowledge and creative strategies for solving problems
(Peters & Kitsantas, 2010). Thus, across many cultures, countries, and academic subjects,
increased use of metacognitive strategies has been consistently linked to positive learning
outcomes; however, ndings have been more variable regarding learning strategies.
In general, much of the research in the United States has indicated that more frequent
use of learning strategies is related to increased learning (Pressley & Harris, 2006) and
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greater academic achievement (Robbins et al., 2004). However, the ndings within
other countries have been less consistent. For example, Chiu, Chow, and Mcbride-
Chang (2007) studied learning and metacognitive strategies across 34 countries and
found that although metacognitive strategies resulted in higher achievement, the use
of memorization learning strategies resulted in lower scores. In contrast, another study
found that learning strategies, such as elaboration, organization, and rehearsal, were all
signicant predictors of physics achievement in Turkey (Sezgin Selcuk, 2010).
Thus, it seems that learning strategies are not always benecial for all students.
Relatedly, research suggests that the deployment of learning strategies is dependent on
contextual factors such as the academic domain, the type of tasks, or the difculty of the
task (Callan & Cleary, 2014; Cleary & Chen, 2009; Hadwin, Winne, Stockley, Nesbit,
& Woszczyna, 2001). Some research also suggests that the use of learning strategies
may depend on demographic factors such as SES or gender. For example, students
from varying socio-economic groups utilize strategies differentially with greater
SES positively relating to more frequent strategy use (Akyol, Sungur, & Tekkaya,
2010; Jensen, 2009; Lipina & Colombo, 2009). Interestingly, most of this research
has examined individual differences in SES such as how one’s family SES inuence
strategy use. Less research has examined more macro level inuences on strategy use,
such as the collective socio-economic status (SES) of one’s country. Given that some
research has shown there to be differences in academic motivation and beliefs across
countries (Chiu & Chow, 2010), it is pertinent to consider whether students from higher
or lower SES countries utilize strategies differently, and if these differences in strategy
use account for unique variation in achievement after controlling for family SES.
In a related line of research, there is some evidence to suggest that males and females
may utilize learning and metacognitive strategies differently, with females being more
strategic than their male peers (Bembenutty, 2007; Zimmerman & Martinez-Pons, 1990).
Given that there are persistent and signicant differences in academic achievement between
males and females in math and reading, and strategy use is related to achievement, it is
pertinent to examine how males or females utilize strategies. Some research has addressed
this issue. For example, Chuy and Nitulescu (2013) examined whether Canadian male and
female students utilized strategies differentially for reading tasks and found that females
tended to use learning strategies and metacognitive strategies more frequently than males.
That study, and much of the literature addressing gender differences in strategy use, has
focused on a single country, and research is needed to examine strategy use across a multi-
national sample of students. Moreover, if differences emerge in strategy use, it is important
to determine the extent to which variation in achievement is explained by such differences.
Research is needed to examine how metacognitive and particularly learning
strategies relate to academic achievement and gender internationally. Specically, it
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is important to understand if some types of strategies may be more useful than other
strategies because this could have important implications for instruction. In this study,
the authors examine the use of metacognitive and learning strategies across higher
and lower SES countries, if these strategies relate to achievement across countries,
and whether strategies account for unique variation after controlling for SES. In
addition, because the majority of research examining gender differences in strategy
use has focused on small sample sizes within the United States, we examine a cross
national sample to address whether males and females utilize different strategies.
Research Questions
The research questions to be addressed in this study are as follows:
1. Across countries, what is the relationship of metacognitive and learning strategies to
reading, math, and science achievement? Do metacognitive and learning strategies
account for unique variation in achievement while controlling for demographics,
including SES?
2. Does the SES of one’s country signicantly relate to the use of learning strategies
and metacognitive strategies? In particular, do students from the countries with the
15 highest and 15 lowest SES utilize learning strategies differently?
3. Are there gender differences in learning and metacognitive strategies that might
explain the traditional gender performance differences in reading and math?
Method
Participants
In the current study, we address the research questions above by examining
individual level data from the Program for International Student Assessment (PISA)
of 15-year-old’s achievement in reading, mathematics, and science from 63 countries.
A total of 475,460 students (50.3% female) were included in the study. Students are
weighted to be representative of their country and school.
Instrument
PISA is an international achievement test designed to determine students’ ability to
apply reading, science, and mathematics content to real-life situations. In addition to the
test, a survey is administered to collect demographic data. The 2009 administration of
PISA included two metacognitive indexes and three learning strategy use indexes (the
2009 PISA data is the most recent that contains these measures). Metacognitive strategies
was measured as knowledge of effective metacognitive strategies for text comprehension.
Students were presented with scenarios and then evaluated the quality and usefulness of
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strategies for reaching an intended goal. The ratings of the strategies were compared to
an optimal ratings developed by experts. Two metacognitive indexes were created: The
index of Understanding and Remembering and the index of Summarizing. Additionally
there were three learning strategy use indexes: The frequency of use of Memorization
Strategies, Elaboration Strategies, and Control Strategies. Student use of these strategies
was compared to ratings deemed effective by experts to create indexes. The index scores
for the two metacognitive and three learning strategy measures were entered into a principal
components analysis with Varimax rotation, and based on proportion of variance explained
and conceptual coherence, a 2-component solution was retained, accounting for 70 percent
of the variance (see Table 1). The rst component (Learning Strategies) contained the
three strategy indexes related to control, elaboration, and memorization, and the second
component (Metacognitive Strategies) contained the two metacognitive strategy indexes.
Table 1
Principal Components Analysis Results for PISA Learning Factors
Factors
Learning Metacognitive
Control strategies .85 .23
Elaboration strategies .80 -.01
Memorization strategies .80 -.09
Meta-cognitive summarizing .01 .85
Meta-understanding and remembering .05 .84
Data Analysis
The components were then entered into the subsequent analyses. In order to
ascertain the relationship of learning and metacognitive strategies to achievement
test scores, after controlling for demographic factors and SES, hierarchical
regression (HR) analysis was used. For each of the academic domains of Reading,
Math, and Science, HR was used in which the rst stage included SES, gender,
language spoken at home (language of exam or other language), and immigration
status (native born or immigrant). The second stage of the HR included the two
components described above. Of particular interest was the amount of additional
variance explained by learning and metacognitive strategies after controlling for
the demographic variables. Analyses were conducted across all 63 PISA countries,
as well as for the 15 wealthiest (based on GDP) collectively, and 15 poorest
collectively, and for each of these nations individually.
Results
Strategies Factors, Achievement, and Demographics
The Learning Strategies component demonstrated a weak correlation to achievement
(r = .02 for reading, r = -.03 for math, and r = -.01 for science). Although statistically
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signicant due to the large sample size (all p < .001), the practical implication for these
relations is near zero. The same cannot be said of the Metacognitive Strategies component,
which demonstrated a strong correlation to achievement across all subject areas even
though the measurement of Metacognitive Strategies was situated only within a reading
context (r = .50 for reading, r = .46 for math, and r = .48 for science; all p < .001).
The two components were also signicantly correlated to all of the demographic
variables (p < .001). As was true for the achievement variables, these relationships were
very weak. The Learning Strategies factor exhibited a very weak, positive relationship
with SES (r = .02), language at home (r = .02; more likely to use strategies when the
language spoken at home was the same as the achievement measure), and immigration
status (r = .02; native born more likely to use the strategies). In addition, there was
a weak, statistically signicant, negative relationship between gender and learning
strategies (r = -.07; males were slightly less likely to use the Learning Strategies). On
the other hand, Metacognitive Strategies displayed stronger correlations with most of
the demographic variables. The Metacognitive Strategies factor was related to SES
(r = .25), gender (r = -.13; males were signicantly less likely to use metacognitive
strategies), and language at home (r = -.09; less likely to use Metacognitive Strategies
when home language was the same as test). The correlation coefcient between
Metacognitive Strategies and immigration was negligible in value (r = .01).
Predicting Achievement with Learning and Metacognitive Strategy Compo-
nents and Demographics
Across all PISA nations, the demographic variables and the two strategy
components were signicant predictors (a = 0.05) of achievement for all three subject
areas (see Tables 2-4). The demographics accounted for approximately 28 percent of
the variance in the rst step of the multiple regressions (R2 = 0.28 for reading and
science, R2 = 0.29 for math), and the Learning Strategy components accounted for a
little less than half of that in the second step (R2 change = 0.14 for reading, 0.12 for
math, and 0.13 for science). The standardized β coefcients for the model across all
countries revealed a strong contribution by the Metacognitive Strategies component
(β = 0.39 for reading, β = 0.36 for math, and β = 0.38 for science), approaching that
of the SES index (β = 0.40 for reading, β = 0.43 for math, and β = 0.42 for science).
The relationship of demographics and strategies to achievement was similar for high
and low SES countries.
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Table 2
Multiple Regression Results with Demographics (Step 1) and Learning Factors (Step 2) Predicting Reading
Achievement for 15 Highest and Lowest SES Countries
Standardized Beta Coefcients
Country Demo R2
Learning
Factors R2
Change
SES Gender Home
Lang
Immi-
gration
Learning
Strategies
Meta-
cognitive
Iceland .18 .13 .17 -.12 -.09 .01* .06 .44
Qatar .26 .11 .16 -.17 -.10 .35 .09 .35
Canada .13 .15 .24 -.10 -.05 -.01 .07 .39
Norway .18 .17 .20 -.18 -.11 -.02 .10 .41
Dubai (UAE) .29 .16 .28 -.16 -.06 .21 .01* .42
Finland .21 .18 .20 -.17 -.10 -.04 .07 .45
Australia .17 .20 .25 -.09 -.01 -.01 .09 .45
Sweden .21 .19 .25 -.14 -.11 -.04 .08 .44
Denmark .20 .19 .27 -.10 -.08 -.03 -.01* .45
Netherlands .14 .27 .24 -.06 -.00* -.02 -.02 .53
United King .17 .16 .28 -.07 -.08 .01 .07 .41
Belgium .21 .24 .28 -.04 -.03 -.05 -.01 .52
Luxembourg .23 .17 .34 -.11 -.04 -.01* -.00* .43
Germany .24 .20 .30 -.14 -.08 -.03 .03 .46
United States .20 .14 .35 -.08 -.01 .02 -.03 .39
High SES .19 .16 .32 -.09 -.03 .01 -.00 .41
Azerbaijan .12 .06 .25 -.16 .06 .01 .13 .22
Kyrgyzstan .27 .09 .32 -.23 .09 .04 .06 .32
Uruguay .27 .13 .38 -.17 -.03 -.00* .04 .38
Macao-China .13 .12 .15 -.18 -.29 .01 .17 .30
Hong Kong .13 .17 .16 -.14 -.18 -.04 .13 .40
Panama .26 .14 .36 -.12 -.12 -.02 .10 .39
Albania .23 .12 .31 -.23 -.02 .01 .13 .34
Colombia .19 .17 .31 -.07 -.03 -.00 -.01 .43
Brazil .19 .13 .31 -.12 -.04 -.04 .08 .37
Turkey .27 .14 .38 -.18 -.04 -.02 .05 .38
Tunisia .14 .09 .29 -.16 -.01 -.02 .10 .28
Mexico .20 .16 .30 -.12 -.10 -.05 .07 .41
Thailand .23 .11 .33 -.23 -.01 -.01 .18 .27
Peru .34 .10 .45 -.10 -.14 -.03 -.05 .32
Indonesia .19 .12 .26 -.26 .03 -.10 .08 .35
Low SES .18 .13 .30 -.16 -.03 -.02 .05 .37
All Countries .28 .14 .40 -.12 -.03 .00 .00 .39
Note. * = not signicant.
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Table 3
Multiple Regression Results with Demographics (Step 1) and Learning Factors (Step 2) Predicting Math
Achievement for 15 Highest and Lowest SES Countries
Standardized Beta Coefcients
Country Demo R2
Learning
Factors R2
Change
SES Gender Home
Lang
Immi-
gration
Learning
Strategies
Meta-
cognitive
Iceland .10 .16 .22 .14 -.05 .01* .08 .41
Qatar .29 .13 .18 .06 .09 .34 .07 .37
Canada .12 .14 .28 .17 .01 -.04 .03 .38
Norway .13 .14 .24 .12 -.11 -.02 .07 .38
Dubai (UAE) .26 .16 .29 .09 .10* .20 -.03 .41
Finland .09 .17 .21 .18 -.06 -.03 .03 .43
Australia .16 .17 .29 .16 .06 -.03 .09 .41
Sweden .17 .16 .29 .10 -.10 -.02 .07 .41
Denmark .16 .15 .27 .18 -.07 -.04 -.06 .41
Netherlands .16 .23 .26 .18 -.02 -.03 -.04 .50
United King .19 .13 .32 .19 -.06 .00* .04 .36
Belgium .22 .20 .30 .21 -.01 -.08 -.04 .47
Luxembourg .21 .14 .35 .20 -.03 -.02 -.02 .39
Germany .22 .19 .32 .17 -.07 -.01 .01 .45
United States .21 .12 .38 .17 -.02 .04 -.06 .36
High SES .19 .15 .34 .17 -.03 .02 -.03 .40
Azerbaijan .02 .06 .06 .10 -.06 -.02 .12 .24
Kyrgyzstan .22 .13 .33 .03 .08 .06 .06 .38
Uruguay .25 .14 .38 .12 -.02 .00* .01* .39
Macao-China .03 .12 .11 .12 -.09 -.02 .18 .29
Hong Kong .11 .17 .20 .14 -.14 -.07 .11 .40
Panama .21 .17 .35 .08 -.03 .00* .11 .43
Albania .12 .14 .28 .04 -.04 .01* .10 .34
Colombia .25 .18 .33 .23 -.02 .01 .01 .45
Brazil .19 .12 .33 .13 -.03 -.05 .04 .36
Turkey .24 .12 .43 .15 -.01 -.00 -.00* .36
Tunisia .18 .09 .38 .12 -.06 .23 .03 .29
Mexico .18 .18 .29 .14 -.07 -.05 .07 .43
Thailand .14 .10 .32 .07 .03 .02 .20 .24
Peru .34 .12 .46 .14 -.11 -.02 -.05 .34
Indonesia .14 .18 .29 .04 .01 -.07 .09 .43
Low SES .17 .13 .32 .10 -.05 .01 .03 .37
All Countries .29 .12 .43 .11 -.04 .01 -.04 .36
Note. * = not signicant.
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EDUCATIONAL SCIENCES: THEORY & PRACTICE
Table 4
Multiple Regression Results with Demographics (Step 1) and Learning Factors (Step 2) Predicting Science
Achievement for 15 Highest and Lowest SES Countries
Standardized Beta Coefcients
Country Demo R2
Learning
Factors R2
Change
SES Gender Home
Lang
Immi-
gration
Learning
Strategies
Meta-
cognitive
Iceland .08 .18 .19 .14 -.07 .02* .05 .43
Qatar .26 .12 .16 -.06 -.02 .36 .08 .36
Canada .11 .15 .26 .13 -.06 -.02 .04 .39
Norway .14 .15 .23 .07 -.15 -.02 .08 -.38
Dubai (UAE) .26 .17 .27 -.05 -.06 .23 .01 .43
Finland .10 .19 .20 .08 -.10 -.04 .03 .46
Australia .15 .19 .27 .11 -.01 -.03 .08 .44
Sweden .17 .18 .26 .10 -.12 -.04 .05 .44
Denmark .17 .16 .27 .15 -.11 -.03 -.01 .42
Netherlands .15 .26 .26 .11 -.03 -.05 -.02 .53
United King .17 .16 .31 .12 -.09 .03 .06 .40
Belgium .20 .22 .28 .14 -.02 -.08 -.01 .49
Luxembourg .23 .16 .37 .14 -.03 -.02 .01* .41
Germany .23 .19 .31 .13 -.14 -.01 .04 .45
United States .19 .13 .36 .12 -.03 .02 -.04 .37
High SES .18 .15 .32 .12 -.05 .00 -.01 .41
Azerbaijan .07 .07 .18 -.04 .11 .01 .09 .27
Kyrgyzstan .18 .11 .28 -.06 .07 .08 .10 .35
Uruguay .25 .14 .38 .05 -.01 .02 .04 .39
Macao-China .06 .14 .12 .05 -.25 -.01* .18 .32
Hong Kong .09 .18 .16 .08 -.17 -.05 .13 .41
Panama .20 .18 .33 .05 -.09 .00* .08 .44
Albania .15 .15 .29 -.07 -.02 .02 .11 .38
Colombia .20 .19 .30 .14 -.01 -.01 .01 .45
Brazil .18 .13 .33 .05 -.03 -.03 .06 .37
Turkey .20 .17 .38 .02 -.04 -.01 .06 .43
Tunisia .12 .09 .32 .03 -.02 -.01 .07 .30
Mexico .18 .17 .31 .09 -.08 -.04 .07 .41
Thailand .14 .10 .30 -.04 -.00* -.01 .17 .27
Peru .20 .10 .43 .05 -.13 -.02 -.02 .33
Indonesia .11 .17 .26 -.03 .06 -.06 .12 .41
Low SES .15 .14 .30 .03 -.05 .00 .05 .38
All Countries .28 .13 .42 .03 .05 .09 -.02 .38
Note. * = not signicant.
Strategy Factors and Achievement in High and Low SES Countries
Demographics and the Metacognitive Strategy components signicantly predicted
achievement for students in both the high and low SES countries across the three
subject areas (see Tables 2-4). The Standardized β weights for both the SES index
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and for the Metacognitive Strategies component were slightly higher for students in
the higher rather than lower SES countries.
The students from the high SES countries scored an average of 84 to 104 points
higher than students from the lower SES countries across subject areas (see Tables
5 and 6). Students in low SES countries used Learning Strategies more (by 0.23
points), and students from high SES countries scored higher on the Metacognitive
Strategies factor (by 0.34 points). In other words, students from low SES countries
were more likely to use strategies that were not strongly related to achievement,
and less likely to use the strategies that are more strongly related to achievement.
The gender difference in Metacognitive Strategies was striking, with females scoring
signicantly higher on the metacognitive strategies component (p < .001), especially
in the higher SES countries (see Table 7).
Table 5
Means, Standard Deviations, Frequencies for High SES Countries
Scores Demographic % Factors
Country Math Reading Science SES Male Lang Cntry Learn Meta
Iceland 507 (86) 500 (93) 495 (92) .72 (.89) 50 97 94 -.30 (1.07) .02 (.99)
Qatar 368 (93) 372 (112) 379 (99) .51 (.91) 51 61 72 .66 (1.15) -.36 (1.04)
Canada 527 (83) 524 (87) 529 (86) .50 (.83) 50 85 88 -.10 (1.08) .23 (.99)
Norway 498 (80) 503 (88) 500 (85) .47 (.74) 49 93 95 -.45 (1.03) .11 (.97)
Dubai (UAE) 453 (94) 459 (104) 466 (101) .42 (.79) 51 50 55 .57 (.92) .12 (.99)
Finland 541 (77) 536 (83) 554 (85) .37 (.78) 50 96 97 -.37 (.93) .26 (.99)
Australia 514 (89) 515 (96) 527 (98) .34 (.75) 49 91 87 -.10 (1.07) .19 (1.02)
Sweden 494 (89) 497 (96) 495 (96) .33 (.81) 49 92 94 -.03 (.96) .01 (1.03)
Denmark 503 (82) 495 (81) 499 (88) .30 (.87) 50 96 95 -.21 (.90) .39 (.94)
Netherlands 526 (86) 508 (86) 522 (93) .27 (.86) 50 94 95 -.29 (.88) .21 (1.03)
United King 492 (83) 494 (92) 514 (95) .20 (.79) 49 94 93 -.01 (.93) .23 (.96)
Belgium 515 (101) 506 (99) 507 (102) .20 (.93) 51 78 91 -.23 (.90) .47 (1.00)
Luxembourg 489 (92) 472 (101) 484 (100) .19 (1.10) 49 11 81 .09 (.98) .08 (1.03)
Germany 513 (95) 497 (92) 520 (97) .18 (.90) 51 90 93 .14 (.90) .45 (1.01)
United States 487 (86) 500 (94) 502 (91) .17 (.93) 51 87 93 -.12 (1.15) -.01 (1.00)
High SES
Countries 497 (89) 501 (93) 509 (95) .22 (.90) 51 88 92 -.09 (1.00) .12 (1.00)
All Countries 454 (101) 461 (99) 463 (101) -.51 (1.21) 50 86 96 .00* (1.00) .00* (1.00)
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Table 6
Means, Standard Deviations, Frequencies for Low SES Countries
Scores Demographic % Factors
Country Math Reading Science SES Male Lang Cntry Learn Meta
Azerbaijan 431 (58) 362 (71) 373 (67) -.64 (.99) 51 93 98 .72 (1.16) -.77 (.95)
Kyrgyzstan 331 (75) 314 (95) 330 (84) -.65 (.93) 49 81 98 .52 (.99) -.74 (.92)
Uruguay 427 (86) 426 (96) 427 (91) -.70 (1.22) 47 98 98 -.00 (1.03) -.03 (.99)
Macao-China 525 (79) 487 (73) 511 (71) -.70 (.87) 51 89 81 -.37 (.89) -.05 (.92)
Hong Kong 555 (90) 533 (81) 549 (83) -.80 (1.02) 53 93 77 -.04 (.92) -.24 (1.01)
Panama 360 (77) 370 (96) 376 (85) -.81 (1.33) 50 94 95 .58 (1.02) -.34 (1.01)
Albania 377 (85) 385 (96) 391 (84) -.95 (1.04) 51 99 99 .65 (.80) .20 (.93)
Colombia 381 (71) 413 (83) 402 (76) -1.15 (1.27) 48 100 99 .38 (.99) -.18 (.99)
Brazil 386 (78) 412 (91) 405 (80) -1.15 (1.21) 47 99 99 .09 (.95) -.19 (.94)
Turkey 445 (89) 464 (79) 454 (76) -1.17 (1.22) 52 96 99 .22 (.84) -.12 (.94)
Tunisia 371 (72) 404 (81) 401 (76) -1.20 (1.31) 48 100 99 .35 (.95) -.23 (.88)
Mexico 419 (75) 425 (81) 416 (73) -1.22 (1.30) 49 97 98 .05 (1.00) .02 (.98)
Thailand 419 (74) 421 (69) 425 (74) -1.31 (1.19) 43 51 100 -.07 (.79) -.41 (.93)
Peru 365 (85) 370 (95) 369 (83) -1.31 (1.25) 51 95 99 .32 (.93) -.23 (.95)
Indonesia 371 (65) 402 (63) 383 (63) -1.55 (1.10) 50 36 99 .15 (.71) -.35 (.95)
Poor Countries 395 (80) 414 (83) 405 (79) -1.28 (1.21) 49 77 99 .14 (0.9) -.22 (1.0)
All Countries 454 (101) 461 (99) 463 (101) -.51 (1.21) 50 86 96 .00* (1.00) .00* (1.00)
Table 7
Metacognitive Component Means and Standard Deviations by SES and Gender
SES
Gender High Low
Male -.05 (1.01) -.32 (.95)
Female .28 (.99) -.14 (.97)
Discussion
In this study, we examined three primary research questions. First, we examined
the relationship of Metacognitive Strategies and Learning Strategies to reading,
math, and science achievement and whether Metacognitive Strategies and Learning
Strategies predicted achievement after controlling for SES. Second, we examined
how the use of Learning Strategies and Metacognitive Strategies compare across
countries with the highest and the lowest SES. Finally, we examined if there are
gender differences in Learning and Metacognitive Strategies that might explain the
traditional gender performance differences in reading and math.
Although researchers differ regarding whether they conceptualize metacognitive
strategies and learning strategies as distinct or inseparable categories of learning tactics,
in this study, we conceptualized them as unique categories. This was consistent with
general procedures for examining PISA data and was also further supported by factor
analytic results that indicated a two factor structure. In the current study, Metacognitive
Strategies entailed tactics that aid a learner’s “thinking about thinking,” such as checking
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one’s understanding of content (i.e., understanding and remembering) and summarizing
information into one’s own words (i.e., summarizing). In contrast, Learning Strategies
were conceptualized as both cognitive strategies (i.e., memorization & elaboration)
and control strategies. As opposed to Metacognitive Strategies, Learning Strategies are
useful for managing the cognitive demands of learning new information.
In regard to the relationship of Metacognitive and Learning Strategies with achievement
in reading, math, and science, we found that the self-reported use of Metacognitive
Strategies was signicantly related to achievement for all three academic subjects and
remained a signicant predictor of achievement for all three academic domains even
after controlling for SES. These ndings are consistent with the prior literature that has
shown metacognitive strategies to be strongly related to achievement for a variety of
academic subjects and across various countries (Gorsuch & Taguchi, 2010; Mevarech
& Amrany, 2008; Onu et al., 2012); however, our ndings contribute to the literature
because we explored the relations of Metacognitive Strategies and achievement across
a global population. Thus, our results, in conjunction with the prior literature, support
the notion that teaching students how to effectively utilize Metacognitive Strategies
should support their academic achievement in all core academic areas regardless of
their nation of origin or their SES.
In contrast, our results showed that Learning Strategies, such as memory
strategies, elaboration strategies, or control strategies, were not strongly associated
with higher achievement after controlling for SES. These ndings contrast a vast
body of research that has supported the use of Learning Strategies for students
within the United States (Cho & Ahn, 2003; Robbins et al., 2004; Tait & Entwhistle,
1996; Vrugt & Oort, 2008). Some prior research examining multi-national samples
has also indicated similar ndings that Learning Strategies may not be universally
effective for students from all countries (Chiu et al., 2007; Ghiasvand, 2010). Thus,
our ndings support this prior research but also contribute by examining a more
globally representative sample.
It is interesting that Metacognitive Strategies strongly predicted achievement but
Learning Strategies did not. It seems plausible that some recent research could shed light
on these ndings. In particular, research suggests that the use of learning strategies is
inuenced signicantly by contextual variables (Hadwin et al., 2001). That is, the learning
strategies that a student will employ depend greatly on factors such as the academic
domain (e.g., reading, math, science), the type of task within the domain (e.g., completing
math homework problems compared to studying for a math test), or even the difculty
level of that task (Callan & Cleary, 2014; Cleary & Chen, 2009). Interestingly, some
initial, albeit limited research suggests that contextualized measures of learning strategies
emerge as stronger predictors of achievement compared to decontextualized measures of
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EDUCATIONAL SCIENCES: THEORY & PRACTICE
learning strategies (Callan & Cleary, 2014; Cleary, Callan, Malatesta, & Adams, 2015).
Finally, it appears that metacognitive strategies may not be as contextually sensitive as
learning strategies (Van Der Stel & Veenman, 2008).
In the current study, the Learning Strategies were measured in relation to a broad
task, test-taking. Thus, it could be the case that students’ self-reported use of Learning
Strategies for test-taking in general was too broad, or generalized, to be meaningful to
their achievement within the domains of reading, mathematics, and science. In contrast,
Metacognitive Strategies were measured in relation to the context of reading. First, the
task of reading is essential to success in all three domains of reading, math, and science
compared to test-taking strategies, and therefore, it might be expected that Metacognitive
Strategies better related to achievement. Second, if metacognitive strategies are more
global in nature, the context in which metacognitive strategies are measured may not be
as important as the context in which learning strategies are measured. Thus, the authors
caution against an interpretation that learning strategies are unimportant or that they should
be ignored. Instead, further research is needed to better understand the most appropriate
ways to measure metacognitive and learning strategies; however, it is clear from our
ndings that metacognitive strategies are a signicant factor in student achievement.
Regarding our second research question to compare strategy use in high and low SES
countries, we found that use of Learning Strategies and Metacognitive Strategies did differ.
In particular, students in high SES countries tended to use Metacognitive Strategies more
frequently than their peers in low SES countries while students from low SES countries
tended to use Learning Strategies more frequently than their peers from high SES
countries. It is interesting that students from low SES countries tended to utilize strategies
that were not strongly related to success more often than their high SES peers. Moreover,
students from high SES countries not only utilized Metacognitive Strategies more often,
our data indicates that, even after controlling for individual SES, Metacognitive Strategies
were more strongly related to higher achievement in high SES countries. Although some
prior research has shown that family SES relates to the number and type of strategies that
students use while learning (Akyol et al., 2010; Lipina & Colombo, 2009), to the authors’
knowledge, no prior study has shown that the SES of one’s country relates to frequency of
learning strategy and metacognitive strategy use. Thus, our ndings contribute uniquely
to the literature in this regard. Although we did not address particular political or societal
ideologies, our ndings seem related to prior research indicating that academic motivation
may differ due to the beliefs and philosophies of one’s country (Chiu & Chow, 2010).
Furthermore, the authors speculate that it is also possible that countries of varying SES
levels may employ different curricula and pedagogical practices and these differences
may also impact how students are taught to learn. Further research to better understand
particular beliefs among high and low SES countries and how these beliefs may impact
the use of strategies would be particularly benecial.
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Our third objective was to determine if there were gender differences in learning
and metacognitive strategies. We found that females were signicantly more likely
to use both Learning Strategies and Metacognitive Strategies. Interestingly, this
difference was much more pronounced for Metacognitive Strategies than for Learning
Strategies. Moreover, the difference in Metacognitive Strategies interacted with the
SES of one’s country in that there was a larger difference between males and females
use of Metacognitive Strategies from high SES countries than in low SES countries.
The prior literature examining the use of learning strategies between males and
females has been mixed. That is, some of the prior literature has suggested that,
within the United States, females utilize a greater number of learning strategies
than males (Bembenutty, 2007), but other research has suggested that there is no
difference between males and females in the use of memorization, elaboration, and
control strategies (Ablard & Lipschultz; Zimmerman & Martinez-Pons, 1990). On
the other hand, the literature regarding metacognitive strategies and gender has been
more consistent within both the United States and international samples. Contrary to
our ndings, much of this literature has suggested there is no signicant difference
between males’ and females’ use of metacognitive strategies (Bembenutty, 2007;
Tang & Neber, 2008). Our ndings contrast this prior literature by showing that
within a global sample of students, females use signicantly more Metacognitive
Strategies than males. Given that metacognitive strategies are so strongly related to
achievement, our ndings suggest that under-achieving males, especially in poor
countries may benet from training in metacognitive strategies.
Our ndings are important and raise several questions for further research. First, more
research is needed to examine gender differences in both learning and metacognitive
strategies, especially to better understand why these differences may exist. Although
our ndings indicate that one possible factor that could inuence the use of strategies
is the collective SES of a country, more research is needed to better understand other
factors that could further explain this difference between genders. Doing so could have
important implications for underachieving males in low SES countries and low SES
families given that Metacognitive Strategies are so strongly related to achievement.
Limitations
There are some limitations regarding the current study that should be noted.
In particular, the current study does not address all of the potential learning and
metacognitive strategies that are available for students to engage or consider.
Moreover, the authors acknowledge the inherent limitations of self-report
questionnaire methodologies for measuring the types and frequency with which
students use learning and metacognitive strategies. Although other methodologies are
available, such as think-alouds, observations, microanalysis, or teacher ratings, the
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EDUCATIONAL SCIENCES: THEORY & PRACTICE
use of self-report questionnaires may be the only feasible measurement methodology
to examine massive sample sizes as was the case in the current study. Further research
that can collect more ne grained data regarding strategy use with other measurement
methodologies may be particularly important. In addition, further research should
also examine similar research questions regarding individual types of learning and
metacognitive strategies to determine the relationships of specic strategies with
achievement and gender.
Conclusions
Although SES and gender were strongly related to achievement and the use of
metacognitive strategies; “demography is not destiny” (Cavanagh, 2007). One role of
educational psychology in public policy is to point the way for possible improvements
in education. Our ndings indicate that the relations between metacognitive strategies
and achievement were as large as the relations between SES and achievement.
Although the directionality in regression analyses is always in question, and we also
do not suggest that metacognitive strategies alone can ameliorate all of the negative
effects of low SES, our ndings are encouraging because students can be taught to use
metacognitive strategies effectively (Perry, VandeKamp, & Mercer, 2000). Moreover,
there was a signicant difference between how males and females utilize learning
and metacognitive strategies. In light of a large achievement gap between males and
females in reading that has continued to widen in the last decade (Organisation for
Economic Co-operation and Development, 2010), these results prompt the need for
further research to examine the role of metacognitive strategies as a means of closing
the reading achievement gap for males. Thus, our ndings are particularly important
for lower SES students and males underachieving in reading who less frequently
utilize metacognitive strategies appropriately.
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... Studies on this topic emphasize that metacognitive strategies are particularly related to learning in mathematics and science classes. In addition, it is emphasized in the relevant literature that this variable is an important predictor of academic achievement (Callan et al., 2016;Coutinho, 2008;Okçuoğlu and Kahyaoğlu, 2007). ...
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The aim of this study was to determine the relationship between students' metacognitive learning, critical thinking, scientific process skills and academic achievements after the ‘Cell and Divisions’ and ‘Force and Energy’ units have been processed according to different teaching methods. Also, in this study, it is aimed to determine the variables that predict academic achievement. In the study, five different groups were selected from 7th grade students. In these groups, lessons were taught according to the Multiple Intelligence Approach, Problem Based Learning, Peer Instruction, Combined Method and the method recommended by the Ministry of National Education (2017). The sample of the study, in which the correlational survey method, one of the quantitative research approaches, was used, consists of 185 seventh grade students studying in two secondary schools in the Yakutiye district of Erzurum. As a data collection tool in research; Metacognitive Learning Strategies Scale, Critical Thinking Tendency Scale, Scientific Process Skills Test and Academic Achievement Tests were used. As a result of the analyzes made on the data of the students in different groups and the whole study group, significant relationships between variables were determined in terms of group specific and all data. In addition, in the hierarchical regression analysis, it was determined that scientific process skills were the most predictive skills for academic achievement for each group.
... In this way, metacognitive knowledge about reading strategies could be a common resource for performance in reading and in mathematics and explain part of the strong relation between these two domains. Callan, Marchant, Finch, and German (2016) used Question 3: The Gotemba walking trail up Mount Fuji is about 9 kilometers (km) long. Walkers need to return from the 18 km walk by 8 pm. ...
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Reading literacy and metacognitive strategies are pivotal factors for solving mathematical word problems. However, it has not been previously investigated whether metacognitive reading strategies are uniquely related to word problem solving when controlling for reading literacy. In the present study, we reanalyzed international data from the 2018 PISA study. Linear regressions with n = 237390 students showed that the moderate relation between metacognitive knowledge about reading strategies and mathematical literacy almost completely disappeared when they were considered simultaneously with reading literacy. In contrast, the relationship between reading and mathematics literacy remained unaffected when controlled for metacognitive reading strategies. We therefore argue that reading literacy should be taken into account when considering metacognitive reading strategies for solving mathematical word problems.
... PISA direktörü Andreas Schleicher, verdiği bir röportajda, Türkiye'nin eğitim sistemi ile ilgili değerlendirmelerde bulunmuş ve ezbercilik gibi yaklaşımlar içeren eğitim ve öğretim sisteminin çağın gerisinde kaldığını ifade etmiştir (Koçak, 2017). Uluslararası yazın incelendiğinde ezberleme gibi ödünleyici yaklaşımların (compensatory approaches) yüksek eğitim başarısı ile ilişkisi bulunmadığı, bunun yerine düşünme farkındalığı gibi üst-bilişsel stratejilerle (metacognitive strategies) verilen eğitimlerin yüksek başarıyı getirdiği ileri sürülmüştür (Callan, Marchant, Finch ve German, 2016). Montt (2011), 50 ülkeyi kıyaslayarak yaptığı çalışmasında öğrencilerin eğitim süreçlerini takip eden, okuldaki öğrencilerin sınıf dağılımlarını iyi yapan ve iyi öğretmenlere sahip okulların ve eğitim sistemlerinin diğerlerine göre daha başarılı olduğunu ortaya koymuştur. ...
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Humankind always has an effort to produce information and code (write) it in order to keep the information permanent. While information has been produced before the invention of computer was subjected to the process of reading on the basis of tactual materials (such as clay, rock, papyrus, paper); whereas together with the spread of computers and internet, information has begun to be produced in the electronic environment and offered to readers in this environment. The purpose of this study was to investigate high school students’ habit of reading printed and e-books comparatively within the context of certain variables. In line with this purpose, semi-structured interview technique which is among qualitative data collection techniques, was used in the study. Target population of the study consisted of fourth-grade high school students having education in a city center of Central Anatolia in the 2019-2020 academic year. In this context 51 students were included in the study survey. In order to collect the study data, face-to-face interviews were conducted with students using a semi-structured interview form which was developed by the researchers and the data acquired were recorded in both written and audial forms. At the end of interviews, the data were analyzed via content analysis method and various concepts and correlations were analyzed in line with the purpose of study. According to the results of the research, it was concluded that while most of high school students preferred to read from the printed book, they generally have low-level book reading and also the readers.
... Its basic premise is that students' should learn non-cognitive skills that relate to more effective learning strategies so that they may be able to overcome difficulties (Hacker et al., 2009). Different studies have also shown a positive correlation between using metacognitive practices, that is, having students think about the best way to learn and adopt certain habits, and academic achievement (Akama, 2006;Hacker et al., 2009;Karpicke et al., 2009;Dunlosky et al., 2013;Gutman & Schoon, 2013;Callan et al., 2016). ...
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Mind, Brain, and Education (MBE) is a transdisciplinary area that joins neuroscience, psychology, and education to inform teaching practices and educational policy with research that can be translated into applicable and reflective tenets and principles of how students learn more effectively. It is well established in the MBE literature that what leads students to success are not only cognitive abilities but also beliefs and attitudes towards learning, which forms a complex and multifaceted universe with different levels of influence. This study has conducted a literature review on the contributions of MBE concerning these beliefs and attitudes and attempted to summarise them into a useful guide that might help students reflect on their academic achievement throughout life. Four essential elements were analysed and discussed, namely: growth mindset, metacognition, self-efficacy, and neuroplasticity. It is argued that these concepts are of paramount importance to anyone who wishes to accomplish both academic and career goals and they are aligned with the notion of lifelong learning.
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Bu araştırmanın amacı, 15 yaş grubu öğrencilerinin meta-biliş stratejileri (güvenilirliği değerlendirme, özetleme, anlama ve hatırlama), genel başarısızlık korkuları, öz-yeterlik inançları, öznel iyi oluşları ve yeteneğin geliştirilebilir olduğuna ilişkin inançlarının fen, matematik ve okuma başarılarının yordayıcıları olarak incelenmesidir. Araştırmada, PISA 2018 öğrenci anketi (Türkiye) verilerinden hareketle (N = 5938), yordayıcı ilişkisel desen kullanılmıştır. Veriler, çok değişkenli uyarlanabilir regresyon eğrileri (MARSplines) ve yol analizleri aracılığıyla çözümlenmiştir. MARSplines analizi sonuçları; biri dışında (güvenilirliği değerlendirme) meta-biliş stratejilerinin, öz-yeterlik inançlarının, öznel iyi oluşun, genel başarısızlık korkusunun ve yeteneğin geliştirilebilir olduğuna yönelik inançların fen, matematik ve okuma başarısını doğrusal olmayan biçimde yordadıklarını göstermiştir. MARSplines analizi sonuçları; söz konusu değişkenlerin fen, matematik ve okuma başarısının yordanmasındaki önem düzeylerine göre sıralanabildiğini de göstermiştir. Araştırma değişkenleri arasındaki ilişkilerin doğrusal olarak incelendiği yol analizi aracılığıyla elde edilen bulgular, yol modelinin hatalı tanımlandığını göstermiştir. Ancak MARSplines analizi sonuçları, bu durumun, doğrusal olmayan ilişkilerin doğrusal bir çerçevede incelenmesine bağlı olarak ortaya çıktığına işaret etmiştir.
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Organizations deploy a team of dedicated security professionals and spend significant resources safeguarding their digital assets. Despite best efforts, security incidents are on the rise and remain a key challenge. The literature has focused inadequately on the lack of professionals’ awareness of security, system, or situational aspects. Extant literature on the impact of awareness on threat management tasks is disjointed and does not adequately consider the metacognitive awareness and self-efficacy of security professionals. To this effect, we propose and empirically validate a model to study the relationship between security, system, situational awareness, and security professionals’ ability to detect, assess, and mitigate threats. We also investigate the effects of metacognitive awareness and self-efficacy on the relationship between awareness and threat management tasks. We validate the model using a survey of 100 information security professionals. Results indicate a significant relationship between awareness, metacognitive awareness, self-efficacy, and threat management task performance. The analysis also demonstrates that metacognitive awareness and self-efficacy mediated the impact of awareness on threat management task performance. We discuss the effects and implications of this study for practice and research.
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Poor math and numeracy skills are associated with a range of adverse outcomes, including reduced employability and poorer physical and mental health. Research has increasingly focused on understanding factors associated with the improvement of math skills in school. This systematic literature review and meta-analysis investigated the association between metacognition and math performance in adolescence (11-16-year-olds). A systematic search of electronic databases and grey literature (to 04.01.2020) highlighted 31 studies. The quantitative synthesis of 74 effect sizes from 29 of these studies (30 independent populations) indicated a significantly positive correlation between metacognition and math performance in adolescence (r = .37, 95% CI = [.29, .44], p < .001). There was significant heterogeneity between studies. Consideration of online (versus offline) measures of metacognition and more complex (versus simple) measures of math performance, and their combination, were associated with larger effect sizes; however heterogeneity remained high for all analyses.
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The current dissertation examined the validity of a context-specific assessment tool, called Self-regulated learning (SRL) microanalysis, for measuring self-regulated learning (SRL) during mathematical problem solving. SRL microanalysis is a structured interview that entails assessing respondents’ regulatory processes as they engage with a task of interest. Participants for this dissertation consisted of 83 eighth grade students attending a large urban school district in Midwestern USA. Students were administered the SRL microanalytic interview while completing a set of mathematical word problems to provide a measure of their real-time thoughts and regulatory behaviors. The SRL microanalytic interview targeted the SRL processes of goal-setting, strategic planning, strategy use, metacognitive monitoring, attributions, and adaptive inferences. In addition, students completed two questionnaires measuring SRL strategy use, and one questionnaire measuring self-esteem. The participant’s mathematics teacher completed a teacher rating scale of SRL for each participant. Mathematical skill was measured with three measures including a three item measure of mathematical problem solving skill completed during the SRL microanalytic interview, a fifteen item posttest of mathematical problem solving skill completed two weeks after the SRL microanalytic interview, and a standardized test of mathematics skill. The primary objectives of this dissertation were to compare the newly developed SRL microanalytic interview to more traditional measures of SRL including two self-report questionnaires measuring adaptive and maladaptive SRL and a teacher rating scale of SRL. In addition, the current dissertation examined whether SRL microanalysis would diverge from a theoretically unrelated construct such as self-esteem. Finally, the primary interest of the current dissertation was to examine the relative predictive validity of SRL microanalysis and SRL questionnaires. The predictive validity was compared across three related but distinct mathematics outcomes including a short set of mathematical problem solving items, a more comprehensive posttest of MPS problem solving skill, and performance on a standardized mathematics test. The results of this study revealed that SRL microanalysis did not relate to self-report questionnaires measuring adaptive or maladaptive SRL or teacher ratings of SRL. The SRL microanalytic interview diverged from the theoretically unrelated measure of self-esteem. Finally, after controlling for prior achievement and SRL questionnaires, the SRL microanalytic interview explained a significant amount of unique variation for all three mathematics outcomes. Furthermore, the SRL microanalytic protocol emerged as a superior predictor of all three mathematics outcomes compared to SRL questionnaires.
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The relationship of socioeconomic status (SES) and SES inequality to student achievement was explored using the 2009 PISA data for 65 countries. Student, school, and country level data were analyzed. School level SES emerged as the strongest predictor of student achievement (even more important than the students' own SES). When controlling for student SES, school SES, and school SES inequality, the SES inequality of the countries was more related to achievement than the mean SES of the countries. Among the higher SES countries, the relatively low SES and high SES inequality of the United States was more related to achievement. The educational policy implications from the findings were clear. Economics, in terms of SES and inequality, are related to student achievement. The SES of students and schools, and the SES inequality of a country provide a context for learning that needs further exploration and consideration by policymakers.
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The authors examined the impact of a supplemental reading course on 462 sixth-grade students’ reading engagement and performance as compared with 389 students in a control group. They further explored students’ cognitive strategy use through think aloud processes with a subset of students who participated in the intervention. Participating students reported significantly higher levels of strategy use, intrinsic motivation, extrinsic motivation, and self-efficacy as compared with the control group. Think aloud measures indicated students who participated in supplemental instruction exhibited higher levels of cognitive engagement at the end of the intervention than they exhibited at the start of the intervention. There was no significant impact on students’ reading performance as measured by a standardized test.
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Young children's strategy acquisition and maintenance were examined by comparing the recall, clustering, and study behaviors of children of different ages and intelligences. Three groups were included in the study: 5-year-old gifted children, 5-year-old nongifted children, and 7-year-old nongifted children. All were observed and measured on 5 consecutive days, with training on strategy use provided on the third day. Several differences among groups were found, generally favoring the gifted children in terms of performance and maintenance of strategies. In addition, the 5-year-old gifted children seemed to spontaneously use categorization strategies and clustered items in recall before training, while the 7-year-old children used categorization and clustering in recall after training. Implications for instruction for gifted students are discussed.
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This study examined the convergent and predictive validity of self-regulated learning (SRL) microanalytic measures. Specifically, theoretically based relations among a set of self-reflection processes, self-efficacy, and achievement were examined as was the level of convergence between a microanalytic strategy measure and a SRL self-report questionnaire targeting similar strategic behaviors. Using a sample of 49 college students, we found that SRL microanalytic self-reflection measures evidenced high inter-correlations and demonstrated medium to large relations with self-efficacy and achievement, respectively. Although non-significant relations were observed between a microanalytic strategy measure and a SRL self-report questionnaire, the microanalytic measure was shown to be a more robust predictor of future performance in the college course. Consideration for the types of scoring procedures used with microanalysis and the implications and limitations of our results are also discussed.
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The relation between achievement and self-regulated learning (SRL) is more complex than originally believed. In this study, 222 seventh-grade students (53% boys) described their use of SRL strategies and rated their achievement goals (mastery and performance). Students were high achievers, performing at or above the 97th percentile on an achievement test. However, they ranged widely in their use of SRL strategies, suggesting that SRL strategies are not necessary for high achievement. Reasons for variation in SRL were examined. Advanced reasoning was not related to SRL. Performance goal orientation was related to SRL only in conjunction with mastery goal orientation. Mastery goal orientation and gender were significantly related to SRL. As mastery goals increased, so did the use of SRL strategies. Girls reported greater use of SRL strategies (a) involving personal regulation or optimizing the environment and (b) when completing difficult homework or engaged in reading and writing. (PsycINFO Database Record (c) 2012 APA, all rights reserved)