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ED 351 643 CG 024 644
AUTHOR Garcia, Teresa; Pintrich, Paul R.
TITLE Critical Thinking and Its Relationship to Motivation,
Learning Strategies, and Classroom Experience.
INSTITUTION National Center for Research to Improve Postsecondary
Teaching and Learning, Ann Arbor, MI.
SPONS AGENCY Office of Educational Research and Improvement (ED),
Washington, DC.
PUB DATE Aug 92
CONTRACT OERI-86-0010
NOTE 31p.; Paper presented at the Annual Meeting of the
American Psych.logical Association (100th,
Washington, DC, August 14-18, 1992).
PUB TYPE Reports Research/Technical (143)
Speeches /Conference Papers (150)
EDRS :RICE MF01/PCO2 Plus Postage.
DESCRIPTORS Biology; College Students; *Critical Thinking;
English Instruction; *Goal Orientation; Higher
Education; Metacognition; *Motivation; Self Control;
Self Motivation; Social Sciences
ABSTRACT Critical thinking has important implications for
classic learning issues such as transfer of knowledge and application
of problem-solving skills to novel situations. The goal of this study
was to identify some of the important correlates of critical
thinking, in terms of motivation, use of cognitive learning
strategies, and classroom experiences. Participants (N=758) were
college students attending three midwestern institutions (a community
college; a small private college; and a comprehensive university)
during the 1987-88 school year. Twelve classrooms were sampled,
spanning three disciplines: biology (three classes, N=219); English
(three classes, N=110); and social science (six classes, N=429). The
Motivated Strategies for Learning Questionnaires (MSLQ) was
administered to students at the beginning and at the end of the
winter 1988 school term. The results of the analyses lend further
support for the positive relationship between "deep" processing (in
this case, critical thinking) and an intrinsic goal orientation. The
relationship between critical thinking and a mastery orientation,
however is tempered by the content domain. Intrinsic goal orientation
is a significant, positive predictor of critical thinking for biology
and social science students, but not for English students, at both
the pretest and posttest. Metacognitive self-regulatory strategies
were consistently positively related to critical thinking, both
across domains and at the two time points. In summary, this study
supported the positive relationship between motivation, deep strategy
use, and critical thinking. (ABL)
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Correlates of critical thinking
Critical thinking and its relationship to motivation, learning strategies.
and classroom experience
Teresa Garcia
and
Paul R Pintrich
Combined Program in Education and Psychology
University of Michigan
Ann Arbor, Michigan
U.S. OEPARTMENT OF EDUCATION
Office of Educational Research and Improvement
EDUCATIONAL RESOURCES INFORMATION
CENTER (ERIC)
`Th.s document has been reproduced as
received from the pe.son or cxganizat.on
onginaling .1
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OE RI position or Macy
-PERMISSION TO REPRODUCE THIS
MATERIAL HAS BEEN GRANTED BY
jere50 Garcia{
TO THE EDUCATIONAL RESOURCES
INFORMATION CENTER (ERIC)
Data collection and preparation of this paper were made possible by funding to the
.. National Center for Research to Improve Postsecondary Teaching and Learning
( NCRIPTAL) at the School of Education of the University of Michigan. NCRIPTAL was
funded by Grant Number OERI-86-0010 from the Office of EducationalResearch and
0Improvement (OERI), Department of Education. The ideas expressed in this paper are
not the positions or policies of NCRIPTAL, OERI, or the Department of Education.
Paper presented in a symposium on "Learning, thinking, and problem solving: Issues
Oin teaching and transfer" at the Centennial Annual Convention of the American
Psychological Association at Washington, DC, August, 1992.
CDC.)
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Correlates of critical thinking2
Abstract
The aim of this study was to identify some of the motivational, learning
strategy, and classroom experiences that are most strongly related to critical thinking.
We focused on intrinsic goal orientation as a motivational variable that may enhance
cognitive engagement (Elliott & Dweck, 1988; Graham & Golan, 1991; Pintrich & Garcia,
1991). In terms of learning strategies, we chose rehearsal, elaboration, and
metacognitive self-regulatory strategies (regulating, monitoring, and planning) as
representative of surface- and deep-processing of information (Entwistle & Marton,
1984; Marton, Hounsell, & Entwistle, 1984; Weinstein & Mayer, 1986) which may relate
to differences in critical thinking. We used classroom perceptions of instructor
effectiveness, the difficulty of the course, and of the degree of collaboration perceived by
the students as experiences which may promote or detract from critical thinking (Ames
& Archer, 1988; McKeachie, 1986; Smith, 1977). Finally, we examined differences
between biology, English, and social science classes to identify domain differences in
the relative importance of the motivational, learning strategy, and classroom
experiences in levels of students' critical thinking (Stodolsky, 1988; Stodolsky, Salk, &
Glaessner, 1991).
3
Correlates of critical thinking3
Critical thinking and its relationship to motivation. learning strategies,
and classroom experiences
Teresa Garcia and Paul R Pintrich
We define critical thinking as the degree to which students report applying
previous knowledge to new situations to solve problems, reach decisions, or make
critical evaluations with respect to standards of excellence (McKeachie, Pintrich, Lin,
Smith, & Sharma, 1990; Pintrich, Smith, Garcia. & McKeachle, 1991). As such, critical
thinking has important implications for classic learning issues such as transfer of
knowledge and application of problem-solving skills to novel situations (Halpern,
1989; Nickcrson, Perkins, & Smith, 1985). The goal of this study was to identify some of
the important correlates of critical thinking, in terms of motivation, use of cognitive
learning strategies, and classroom experiences.
There is a large and growing body of research directed at examining the interface
between motivation and cognition (e.g., Elliott & Dweck, 1988; Graham & Golan, 1991;
Pintrich & Garcia, 1991). This research has shown that students' goals are related to
their degree of cognitive engagement. Engaging in a task for reasons such as interest,
mastery, challenge -- having an intrinsic goal orientation -- is related to "deeper"
processing, whereas engaging in a task for reasons such as demonstrating one's ability,
getting a good grade, or besting others -- having an extrinsic goal orientation -- is
related to shallower levels of information processing. This line of research has
demonstrated the importance of motivation in students' cognitive engagement;
accordingly, our model includes intrinsic goal orientation as a factor that may
positively influence critical thinking. Previous studies have examined the links
between motivation and learning strategies, but there has been little research on the
links between motivation and critical thinking.
4
Correlates of critical thinking4
Researchers focusing on students' strategies for learning have also contributed
to our understanding of reasoning and thinking in the classroom. Entwistle and his
colleagues (e.g.. Entwistle & Marton, 1984; Marton, Hounsell, & Entwistle, 1984) discuss
students' information processing in terms of the use of deep and surface learning
strategies. Greater cognitive engagement is associated with using deep strategies such as
elaboration, organization, as well as metacognitive strategies involving monitoring,
regulating, and planning (cf. Branford, Sherwood. Vye, & Reiser, 1986; Weinstein &
Mayer, 1986). Surface strategies such as rote rehearsal, copying passages from the text,
or rewriting class notes indicate a lesser degree of cognitive engagement. Critical
thinking, by definition, is a form of higher-order cognitive engagement (e.g., Halpern,
1989); students who use deep strategies may then demonstrate greater levels of critical
thinking, relative to students who tend to use surface strategies. We have included
rehearsal, elaboration, metacognitive self-regulatory strategies as correlates of critical
thinking to test this proposition.
Critical thinking may not only be influenced by students' motivation and use of
learning strategies, but also by what happens in the classroom. Students' reasoning
and thinking are affected by classroom processes and task structures (Ames & Archer.
1988; Halpern, 1989; McKeachie, 1986; Nolen, 1988; Smith, 1977). Students who are
allowed to work in small, collaborative peer groups demonstrate greater cognitive
engagement. Higher levels of cognitive engagement are also reported by students who
rate their instructors as enthusiastic, effective, and responsive.
Finally, classroom processes and their relationship to students' motivation and
cognition may be traced to domain differences (Stodolsky, 1988; Stodolsky, Salk, &
Glaessner, 1991). Different domains demand different instructional practices and task
structures, and the importance of particular strategies or motivational outlookmay
vary by content area. The tasks and cognitive demands on students in the natural
sciences may be markedly different from those faced by students in the social sciences,
5
Correlates of c.,:itical thinking5
in composition classes, or mathematics, so the impact of motivation, learning
strategies, and classroom experiences on critical thinking may differ by discipline.
Therefore, the research questions that will guide the reporting of results are as
follows: 1) What are the relationships between motivation, learning strategies, and
critical thinking?: 2) What is the relationship between classroom experience and
critical thinking?: and 3) How do the relationships between motivation, learning
strategies, classroom experience, and critical thinking vary by subject domain?
Method
Subjects
Participants of this study were 758 college students attending three midwestern
institutions (a community college; a small, private college; and a comprehensive
university) during the 1987-1988 school year. Twelve classrooms were sampled,
spanning three disciplines: biology (three classes, total n = 219); English (three classes,
total n = 110); and social science (six classes, n = 429). Males (49.1%) and females
(50.9%) are proportionately represented, and the majority of our sample is white (84%).
Over half of the respondents were in their first year of college (57%).
The Motivated Strategies for Learning Questionnaire (Pintrich, McKeachie,
Smith, Doljanac, Lin, Naveh-Benjamin, Crooks, & Karabenick, 1988) was administered
to stadents at the beginning and at the end of the Winter 1988 school term. These data
were collected on a volunteer basis, and subjects received no monetary compensation
for their participation.
Measures
The Motivated Strategies for Learns g Questionnaire (MSLQ) is a self-report,
Likert-scaled instrument (1 = not true of me, to 7 = very true of me) designed to measure
student motivational beliefs and strategy use. The 1988 version of the MSLQ consists of
6
Correlates of critical thinking6
40 motivation and 65 cognitive strategy items, which comprise a total of 20 scales. The
five pretest and posttest MSLQ scales used were: intrinsic goal orientation; rehearsal
strategies; elaboration strategies; metacognitive self-regulatory strategies; and critical
thinking. The posttest MSLQ includes an additional 30 items designed to tap into
students' course perceptions. Our three course perceptions scales, instructor
effectiveness, course difficulty, and collaborative learning are based on a factor
analysis of the classroom perception items. Subjects' mean scale scores were used for
analyses.
Intrinsic goal orientation (4 items: time 1 alpha = .73; time 2 alpha = .75) refers
to the degree to which a student engages in a learning task for reasons such as mastery,
challenge, curiosity (e.g., "I prefer coursework that arouses my curiosity, eveL if it is
difficult"). The rehearsal strategies scale (4 items: time 1 alpha = .64; time 2 alpha = .66)
is a measure of the level of memorization and repetition a student uses when studying
(e.g., "When I study I practice saying the material to myself over and over"). Elaboration
strategies (7 items: time 1 alpha = .65; time 2 alpha = .73) is an index of the degree to
which a student tries to paraphrase, summarize, or create analogies (e.g., "I write brief
summaries of the main ideas in my lecture notes"). Our measure of metacognitive self-
regulatory strategies (13 items: time 1 alpha = .78; time 2 alpha = .83) assesses three
general processes: planning, regulating, and monitoring (e.g., "I try to think through a
topic and decide what I'm supposed to learn from it rather than just read it over when
studying"). Critical thinking (5 items: time 1 alpha = .75; time 2 alpha = .78) is a
measure of the extent to which students report applying previous knowledge to new
situations tz solve problems, reach decisions, or make critical evaluations with respect
to standards of excellence (e.g., "When a theory, interpretation, or conclusion is
presented in class or in the readings, I try to decide if there is good supporting
evidence"). Our course perceptions scales tap three factors: instructor effectiveness (10
items, alpha = .90; e.g., 'The instructor explains material well"); course difficulty (4
7
Correlates of critical thinking7
items. alpha = .76; e.g.. 'This course requires too much work compared to other courses
carrying the same credit hours"); and collaborative learning (2 items, alpha = .80; e.g.,
"Students often work together to complete assignments").
Results
Descriptive statistics and bivariate relationships
Means and standard deviations for these constructs are located in Table 1. The
variables show a slight negative skew, with means hovering at about 5.0. Mean levels of
critical thinking decreased from time 1 to time 2; a paired t-test indicates that this
decrease is statistically significant. t(376) = 3.91, p = .000. Additional paired t-tests
show significant decreases in intrinsic goal orientation (0384) = 2.50, p = .013),
rehearsal (t(377) = 2.51, p = .012), and elaboration (t(375) = 2.56, p = .011). These time 1 to
time 2 decreases are consistent with many of our previous findings (e.g.. Pintrich et al.,
1991; Pintrich & Garcia, 1991).
Critical thinking, intrinsic goal orientation, rehearsal, elaboration, and
metacognitive self-regulatory strategies are positively correlated with one another. As
shown in Table 1, the correlations between the five constructs at the pretest range from
.23 to .66, and correlations between the posttest measures of these variablesrange from
.33 to .75. Note that although the five constructs are positively related toone another,
the relationship between critical thinking and rehearsal (r = .23 at time 1, and .28 at
time 2) is weaker than those between critical thinking and intrinsic goal orientation (r
= .50 at time 1; .57 at time 2). elaboration (r = .57 at time 1; .64 at time 2), and
metacognitive self-regulatory strategies (r = .59 at time 1; .64 at time 2). Test-retest
correlations between pairs of the same constructs range from .57 to .66. Correlations
between different constructs at time 1 and time 2 range from .18 to .52.
8
Correlates of critical thinking8
Insert Table 1 about here
Correlations between the three course perception variables and critical
thinking, intrinsic goal orientation, rehearsal, elaboration, and metacognitive self-
regulatory strategies are presented in Table 2. Instructor effectiveness is moderately
related to critical thinking (r = .13 with time 1 critical thinking, and r = .21 at time 2
critical thinking), and more strongly related to intrinsic goal orientation and learning
strategies (correlations range from .16 to .25 with time 1 measures, and from .28 to .40
with time 2 measures). Perceptions of course difficulty do not seem to be related to
critical thinking, motivation, and learning strategies in a linear fashion, with
bivariate correlations ranging from -.09 to .07. Collaborative learning is moderately
related to critical thinking (r = .10 with time 1 critical thinking, andr = .13 with time 2
critical thinking). Working with other students is also positively related to levels of
intrinsic goal orientation and use of learning strategies (f a range from .05 to .14 with
time 1 measures of motivation and strategies; r's range from .16 to .30 with time 2
measures of motivation and learning strategies).
Insert Table 2 about here
Differences between disciplines
Although the three disciplines (biology, English, and social science) show no
significant differences in mean levels of pretest critical thinking (F(2,678) = .26, n.s.),
there are significant differences in posttest levels of critical thinking, and pretest and
posttest measures of intrinsic goal orientation and learning strategies (see Table 3).
Biology, English, and social science also significantly differ in mean levels of perceived
9
Correlates of critical thinking9
instructor effectiveness, course difficulty, and collaborative learning. Post hoc Scheffe
tests show a consistent pattern that indicates biology is different from English and
social science. At both the pretest and posttest, biology students reported higherlevels
of intrinsic goal orientation, rehearsal, elaboration, and metacognitive self-regulatory
strategies, compared to English and social science students. At time 2, however,
students in English reported higher levels of critical thinking (M = 4.81) than did
biology (M = 4.60) or social science (M = 4.38) students. In terms ofcourse perceptions,
biology students perceived their classes to be of higher quality (M = 5.82) and more
difficult (M = 2.82) than English and social science students. Biology also has the
highest perceived level of collaborative learning (5.30). followed by social science (3.83)
and English (2.99).
Insert Table 3 about here
Critical Thinking at Time 1
Given that critical thinking is correlated with motivation, learning strategies.
and classroom experiences (Tables 1 & 2); and that there are significant domain
differences in mean levels of the constructs of interest (Table 3). we moved to multiple
regression as a multivariate tool for examining differences in the relative importance
of motivation and different types of learning strategies on levels of critical thinking.
Table 4 contains the results of four parallel regressions: the firstregression was done
using the entire sample, and the other three are the same regressions done separately
by discipline. These equations enter time 1 intrinsic goal orientation, rehearsal.
elaboration, and metacognitive strategies as predictors of time 1 critical thinking.
The first regression was done on the entire sample, and included two dummy
variables to test for domain differences (Biology and Englishare coded 0/1, so entering
ey
Correlates of critical thinking10
these two variables into the equation makes social science the comparison group). At
time 1, intrinsic goal orientation (beta = .33). elaboration strategies (beta = .27), and
metacognitive self-regulatory strategies (beta = .33) have comparable effects on critical
thinking, whereas rehearsal strategies are not significantly related to critical thinking
in this multivariate analysis (beta = -.03). It is interesting to note that the oneway
ANOVA showed no significant differences between the three disciplines in pretest
critical thinking: however, after controlling for motivation and learning strategies,
being in biology is associated with significantly lower levels of critical thinking,
compared to social science (beta = -.08). Forty-five percent of the variance in pretest
critical thinking can be accounted for by incoming levels of motivation and learning
strategies, as well as discipline differences.
Within-domain regressions show that rehearsal is not significantly related to
critical thinking in the three domains. Rehearsal strategies are marginally significant
in the equations for biology and for English. Rehearsal is negatively related to time 1
critical thinking In biology (-.10), but positively related to pretest levels of critical
thinking in English (.18). Biology (R2 = .46) and social science (R2 = .47) show the same
pattern of results as above, with intrinsic goal orientation, elaboration, and
metacognitive self-regulatory strategies positively related to critical thinking.
However, testing the same model with the English sample shows that the only
significant predictor of critical thinking is use of metacognitive self-regulatory
strategies. Accordingly, slightly less variance in pretest critical thinking is accounted
for in the English sample (R2 = .38).
Insert Table 4 about here
1
Correlates of critical thinking11
Critical Thinking at Time 2
The second set of regression equations enter time 2 intrinsic goal orientation,
rehearsal, elaboration, and metacognitive self-regulatory strategies as predictors of
time 2 critical thinking. In order to examine the effects of classroom experiences on
students' critical thinking, the posttest regression models also include perceptions of
instructor effectiveness, course difficulty, and collaborative learning (see Table 5).
Insert Table 5 about here
The first regression, using the aggregated sample and the two dummy variables
for discipline, show the same pattern of effects for motivation and learning strategies
at the posttest as in the pretest. That is, time 2 rehearsal strategies (beta = -.06) are not
significantly related to posttest critical thinking, but intrinsic goal orientation (beta =
.25), elaboration (beta = .28), and metacognitive self-regulatorystrategies (beta = .35) are
significant positive predictors of critical thinking at the posttest. The only significant
course perception predictor is course difficulty (beta = .10): students who perceived their
courses as difficult tended to report higher levels of time 2 critical thinking. After
adjusting for motivation, use of learning strategies, and clusroom perceptions, biology
students reported significantly lower levels of critical thinking (beta = -.08), and
English students reported significantly higher levels of critical thinking (beta = .10),
compared to their social science counterparts. Slightly more than half of the variance
(R2 = .54) in posttest critical thinking is accounted for by time 2 motivation, strategy
use, course perceptions, and discipline differences.'
1 When pretest level of critical thinking is included in this regression, variance
explained increases to 65%. The effects of motivation, strategy use, and being in
English are unchanged when time 1 critical thinking is included. However, course
difficulty drops out as a significant predictor (beta decreases to .05).
:12
Correlates of critical thinking12
Once again, the effects of motivation, strategy use, and course perceptions on
critical thinking at time 2 differ between domains. For biology classes, the effects of
perceived instructor effectiveness (beta = -.04), course difficulty (beta = .08), and
collaborative learning (beta = .10) are washed out after adjusting for posttest levels of
motivation and strategy use. At time 2, rehearsal is a significant negative predictor
(beta = -.19) of critical thinking, whereas intrinsic goal orientation (beta = .30).
elaboration (beta = .28), and metacognitive self-regulatory strategies (beta = .32) have
comparably strong, positive effects on critical thinking. All told, just over half of the
variance in posttest critical thinking in the biology classes is attributable to
motivation and strategy use (R2 = .52).2
Much like the pretest findings, the only significant predictor of posttest critical
thinking in English is metacognitive self-regulatory strategies (beta = .56). Intrinsic
goal orientation, rehearsal, elaboration, and course perceptions were not significantly
related to time 2 critical thinking. Thus the bulk of the variance explained (R2 = .52) in
posttest critical thinking is related to planning, regulating, and monitoring processes.3
Posttest critical thinking in the social science courses (as in the pretest
regression) is positively and significantly related to intrinsic goal orientation (beta =
.28), elaboration (beta = .34), and metacognitive self-regulatory strategies (beta = .30) at
time 2. Time 2 rehearsal strategies are not significantly related to posttest critical
thinking in a linear fashion. Perceptions of instructor effectiveness and collaborative
learning are unrelated to time 2 critical thinking, but course difficulty is a significant
positive predictor of critical thinking in the social sciences (beta = .14). Almost sixty
2 When pretest level of critical thinking is included in this regression, variance
explained increases to 67%. When time 1 critical thinking is accounted for, the effects
of intrinsic goal orientation and metacognitive self-regulatory strategies are
unchanged, but the effects of time 2 rehearsal and elaboration strategies on posttest
critical thinking are washed out (betas decrease to -.01 and .09, respectively).
3 Including time 1 critical thinking into the posttest model makes no difference in the
results, in terms of the magnitude of the betas or percent of variance accounted for by
the variables in the equation.
Correlates of critical thinking13
percent of the variance in critical thinking at time 2 is accounted for by motivation,
strategy use, and perceptions of course difficulty (R2 = .59).4
Finally, in order to make a more stringent test of the links between motivation,
strategy use, classroom experiences, and critical thinking, we used time / measures of
motivation and strategy use and time 2 classroom perceptions to predict time 2 critical
thinking (see Table 6). Regressing posttest critical thinking on this set of variables gave
similar results to the regressions reported above, although percents of variance
explained are lower.
Insert Table 3 about here
For the model we used to test the entire sample, being in English is positively
related to time 2 critical thinking (beta = .14), as is having an intrinsic goal orientation
(beta = .17), using elaboration strategies (beta = .13) and using metacognitive self-
regulatory strategies (beta = .38). Time 1 use of rehearsal strategies is negatively related
to time 2 reports of critical thinking. After adjusting for pretest levels of motivation
and strategy use, the only measure of classroom perceptions that is statistically
significant is collaborative learning (beta = .13). Just over a third of the variation in
posttest critical thinking can be accounted f ^r by motivation, strategy use, and
classroom experiences (R2 = .35).5
4 An additional 10% of variance is accounted for by including time 1 critical thinking
in the regression equation; doing so washes out the effect of perceptions ofcourse
difficulty, but the relationship between motivation, strategy use, and critical thinking
at time 2 remains unchanged.
5 Including time 1 critical thinking as a predictor in this equation explains an
additional 12% of the variance in time 2 critical thinking. However, the effects of time
1 intrinsic goal orientation and rehearsal are washed out, and the effect of
metacognitive self-regulatory strategies is decreased (beta decreases from .38 to .22).
Perceived instructor effectiveness becomes a significant predictor (beta= .10).
i4
Correlates of critical thinking14
The models tested separately by domain show the same patterns of differences.
Time 1 use of rehearsal strategies is significantly negatively related to time 2 critical
thinking (beta = -.18) in biology, but not in English or social science. Metacognitive
self-regulatory strategies at the pretest are positively related to posttest critical
thinking in all three domains (betas are .40, .38, and .38 for biology. English, and
social science, respectively): again, use of these metacognitive self-regulatory strategies
is the only significant predictor in the English model.
Discussion
Previous research has shown how an orientation towards mastery learning has
positive effects on depth of information-processing (e.g., Graham & Golan, 1991;
Pintrich & Garcia, 1991); the results of our analyses here lend further support for the
positive relationship between "deep" processing (in this case, critical thinking) and an
intrinsic goal orientation. The relationship between critical thinking and a mastery
orientation, however, is tempered by the content domain. Intrinsic goal orientation is
a significant, positive predictor of critical thinking for biology and social science
students, but not for English students, at both the pretest and the posttest. Although the
bivariate correlations between intrinsic goal orientation and cr:tical thinking at the
two time points are positive (r's range from .11 to .50) for students in English,
multivariate analyses showed that the most powerful predictor of critical thinking in
composition classes was the use of metacognitive self-regulatory strategies. The key
difference between English and the two other domains may be that our English courses
were composition classes. Critical thinking in English may mean critically evaluating
one's own and other's writing. Planning, regulating, and monitoring are processes
which are crucial to effective composition; therefore, metacognitive awareness, rather
than motivation, becomes paramount in critically evaluating text.
Correlates of critical thinking15
Metacognitive self-regulatory strategies were consistently positively related to
critical thinking, both across domains and at the two time points. The three processes
that this scale taps, planning, regulating, and monitoring, define an awareness that
may be one of the most important factors in reaching critical evaluations with respect
to standards of excellence and applying previous knowledge to new situations to solve
problems and reach decisions. Elaboration strategies (attempts to paraphrase,
summarize, or draw analogies between different aspects of coursework) are also
positively related to critical thinking (although not significantly so for English). It
seems that "deep" strategy use fosters critical thinking: not unexpectedly, cognitive
engagement in trying to understand the material (use of elaboration and metacognitive
self-regulatory strategies) appears to beget further cognitive engagement that implies
going beyond the material to think critically about it.
Rote rehearsal strategies were not consistently related to critical thinking.
Zero-order correlations between rehearsal and critical thinking were all positive;
however, after adjusting for other strategies, motivation, course perceptions, and
domain differences, the relationship between rehearsal and critical thinking was
either nonexistent or slightly to moderately negative. We had expected that deep
strategy use would be positively related to critical thinking, and surface strategy use
would be negatively related to critical thinking. However, rehearsal in and of itself
does not appear to enhance nor attenuate critical thinking. It may be the case that
although rehearsal is considered to be a surface strategy, it is a form of cognitive
engagement (albeit a shallower form); cognitive involvement, in any manifestation,
may simply be a necessary, but not sufficient precursor of critical thinking.
Students' evaluations of instructor effectiveness were not significantly related
to critical thinking. Course difficulty anu collaborative learning were significantly
related to critical thinking, but these effects varied, depending on the regression model.
When looking at the entire sample, and using pretest measures of motivation and
Correlates of critical thinking16
strategies use. collaborative learning is a significant, positive predictor of critical
thinking at the posttest. When looking at the entire sample and using posttest measures
of motivation and strategy use, v find that posttest critical thinking is positively
related to perceptions of course difficulty. The conclusion we can draw from these data
is that in general, collaboration and discussion of class material with other students
seems to promote critical thinking, and interestingly, course v-irk students perceive as
challenging may "force" students to think more critically.
With respect to domain differences, we found that biology students had the
lowest level of critical thinking, after adjusting for motivation, cognitive strategy use,
and course perceptions. This may be due to the nature of the material presented in a
science class: students may have taken what they were presented to learn simply as
factual, and did not seek to actively challenge what they may have interpreted as laws
of nature. The higher level of critical thinking reported by students in English may
also reflect the nature of the discipline. The questions which comprise our critical
thinking scale (e.g., "Whenever I read an assertion or conclusion, I think about possible
alternatives," "I often find myself questioning things I hear or read in this course to
decide if I find them convincing," " I try to develop my own understanding of most
topics, rather than only rely on the instructor's ideas") certainly tap into the processes
involved in constructing and deconstructing text.
In summary, this study lends support to the positive relationship between
motivation, deep strategy use, and critical thinking. These relationships held true
across different regression models: when predicting pretest critical thinking with
pretest motivation and cognition; when predicting posttest critical thinking with
posttest motivation and cognition; and when predicting posttest critical thinking with
pretest motivation and cognition. Collaborative learning and challenging course work
are also positively related to critical thinking, but these classroom experiences are
much weaker predictors of critical thinking, compared to individual differences in
Correlates of critical thinking17
motivation and deep-processing strategies. The domain differences we found provide
evidence supporting Stodolsky and her colleagues' claims of motivation and cognition
varying by subject matter (Stodolsky, 1988; Stodolsky, Salk, & Glaessner, 1991),
although a within-subject, repeated measures design would be a more powerful and
stringent test of this assertion. The consistent effects of domain, and the varying
effects of classroom experiences we found here indicate that the nature of the domain
(e.g., the tasks students are given, the type of material involved), rather than actual
classroom experience may be more closely linked to students' critical thinking. These
data highlight the importance of motivation, cognitive engagement, and the subject
domain in students' critical thinking.
Correlates of critical thinking18
References
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BEST COPY AVAILABLE
Table 1. Means, standard deviations, and correlations among pretest and posttest MSLQ scales.
Correlations with absolute values greater than or equal to .08 are significant at alpha = .05.
Note: intrins = intrinsic goal orientation; rehears = rehearsal strategies; elabora = elaboration
metacognitive self-regulatory strategies; critthk = critical thinking.
Minimum pairwise n = 447.
strategies; metacog =
intrinsl rehearsl
Time 1
elabora 1 metaco :1 critthkl intrins2 rehears2
Time 2
elabora2 metaco critthk2
intrinsl 1.0
rehears 1 .23 1.0
elabora 1 .49 .42 1.0
metacogl .50 .45 .66 1.0
critthkl .50 .27 .57 .59 1.0
intrins2 .59 .13 .35 .39 .37 1.0
rehears2 .18 .57 .40 .33 .13 .33 1.0
elabora2 .42 .32 .61 .59 .43 .60 .46 1.0
metacog2 .36 .31 .50 .66 .35 .56 .49 .75 1.0
critthk2 .41 .16 .44 .52 .63 .57 .28 .64 .64 1.0
Mean 5.31 5.14 4.88 4.86 4.71 5.16 5.04 4.81 4.89 4.54
SD .97 1.03 .88 .80 1.02 .98 1.11 .98 .86 1.07
Table 2. Means, standard deviations and correlations among pretest and posttest MSLQ scales and course perception
variables. Minimum pairwise n = 447. Correlations with absolute values greater than or equal to .08 are significant at
alpha = .05.
Instructor
Effectiveness Course
Difficulty Collaborative
Learning
Time 1 measures
Intrinsic Goal Orientation .20 .03 .14
Rehearsal Strategies .16 .06 .10
Elaboration Strategies .21 .00 .14
Metacognitive Self-Regulatory Strategies .25 .00 .05
Critical Thinking .13 .07 .10
Time 2 measures
Intrinsic Goal Orientation .40 -.07 .18
Rehearsal Strategies .28 .08 .30
Elaboration Strategies .36 -.07 .22
Metacognitive Self-Regulatory Strategies .32 -.09 .16
Critical Thinking .21 .01 .13
Mean 5.50 2.42 4.17
SD 1.00 1.11 1.71
22
Table 3. Discipline differences. Means with different subscripts are significantly different from one another at alpha = .05
(post hoc Scheffe tests).
Biolo y En lish Social Science
Time 1 measures
Intrinsic Goal Orientation 5.55, 5.19b 5.23b F(2,681) = 7.86 .000
Rehearsal Strategies 5.32a 4.92b 5.12,b F(2,677) = 5.07 .007
Elaboration Strategies 5.04a 4.80,b 4.83b F(2,678) = 4.31 .014
Metacognitive Self-Regulatory Strategies 5.01a 4.98a 4.76b F(2,678) = 7.31 .001
Critical Thinking 4.74 4.74 4.68 F(2,678) = .26 .774
Time 2 measures
Intrinsic Goal Orientation 5.36a 5.17ab 5.02b F(2,456) = 5.69 .004
Rehearsal Strategies 5.46, 4.45b 4.98, F(2,448) = 25.26 .000
Elaboration Strategies 5.00, 4.78b 4.68b F(2,447) = 4.56 .011
Metacognitive Self-Regulatory Strategies 5.08, 4.90b 4.76b F(2,450) = 4.64 .002
Critical Thinking 4.60,b 4.81, 4.38b F(2,447) = 5.98 .005
Classroom perceptions
Instructor Effectiveness 5.32, 5.15b 5.42ab F(2,452) = 14.73 .000
Course Difficulty 2.82, 2.31b 2.18b F(2,453) = 16.36 .000
Collaborative Learning 5.30, 2.99b 3.83, F(2,454) = 78.99 .000
Table 4. Multiple regression results, with pretest critical thinking as the outcome variable. Standardized regression
coefficients are presented. Minimum pairwise ns are as follows: entire sample, 679; biology, 180; English, 94; social
science, 405.
Note: + = p < .10 * = p < 05 ** = p < .01 *** = p < .001
Entire sample Biology English Social Science
Time 1 measures
Intrinsic Goal Orientation .33*** .23*** .19+ .22***
Rehearsal Strategies -.03 -.10+ .18+ -.02
Elaboration Strategies .27*** .34*** .12 .25***
Metacognitive Self-Regulatory Strategies .33*** .27** .28* .35***
Discipline variables
Biology (0 = no, 1 = yes) -.08**
English (0 = no, 1 =yes) -.01
R-squared .45*** .46*** .38*** .47***
Table 5. Multiple regression results, with posttest critical thinking as the outcome variable. Standardized regression
coefficients are presented. Minimum pairwise ns are as follows: entire sample, 447; biology, 147; English, 84; social
science, 213.
Note: + = p < .10 * = p < .05 ** = p< .01 *" = p < .001
Entire sample Biology English Social Science
Time 2 measures
Intrinsic Goal Orientation .25*** .30*** .04 .28***
Rehearsal Strategies -.06+ -.19** -.02 .01
Elaboration Strategies .28*** .27** .15 .34***
Metacognitive Self-Regulatory Strategies .35*** .32*** .56*** .30***
Classroom Perceptions
Instructor Effectiveness .05 -.04 .08 -.07
Course Difficulty .10** .08 .02 .14**
Collaborative Learning .05 .10 .01 .01
Discipline variables
Biology (0 = no, 1 = yes) -.08*
English (0 = no, 1 =yes) .10"
R-squared .54*** .51*** .52*** .59***
4.: 8
a
Table 6. Multiple regression results, with posttest critical thinki4 as the outcome variable. Standardized regression
coefficients are presented. Minimum pairwise ns are as follows: entire sample, 337; biology, 111; English, 71; social
science, 193.
Note: + = p < .10 * = p < .05 ** = p < .01 *** = p < .001
.17**
-.11*
.13*
.38***
.09+
.04
.13**
-.08+
.14**
sample
I.35****
Biology
A5*"
English Social Science
.37*** -.10 .11+
-.18* .00 -.10
-.05 .00 .27*"
.40*" .38* .38***
.12 .24+ .02
-.02 -.04 .09
.18* -.05 .10+
.23* .42***
Entire
Time 1 measures
Intrinsic Goal Orientation
Rehearsal Strategies
Elaboration Strategies
Metacognitive Self-Regulatory Strategies
Classroom Perceptions
Instructor Effectiveness
Course Difficulty
Collaborative Learning
Discipline variables
Biology (0 = no, 1 = yes)
English (0 = no, 1 =yes)
R-squared
0
