Epistemic profiles and metacognition: support
for the consistency hypothesis
Krista R. Muis & Gina M. Franco
Received: 23 June 2008 /Accepted: 21 January 2009
# Springer Science + Business Media, LLC 2009
Abstract Relations were examined between epistemic profiles, metacognition, problem
solving, and achievement in the context of learning in an educational psychology course.
Two hundred thirty-one university students completed self-report inventories reflecting
their epistemic profiles and use of metacognitive strategies, and were epistemically profiled
as rational, empirical, or both rational and empirical in their approaches to knowing. From
the larger sample, 78 students participated in a problem-solving session using a think aloud
protocol. Results demonstrated that for self-reported metacognitive strategies, students
profiled as both rational and empirical had the highest frequency of metacognitive strategy
use compared to students profiled as empirical. Similarly, during problem solving, students
profiled as both rational and empirical had the highest frequency of regulation of cognition
compared to students profiled as empirical or rational. Finally, students profiled as both
rational and empirical attained higher levels of problem-solving achievement compared to
students profiled as empirical.
A mother asks her son, “What color is the sky?” The boy answers, “Blue.” The mother
queries, “How do you know that?” The boy responds confidently, “Because I can see that it
is blue.” In this scenario, the boy justifies his belief that the sky is blue via empirical
evidence. Justifying beliefs through observation is not the sole method by which humans
come to understand the world around them. For centuries, philosophers have attempted to
answer questions such as: What are the limits of human knowledge? What are the sources
Support for this research was provided by grants to Krista R. Muis from the Social Sciences and Humanities
Research Council of Canada (410-2007-0399), and McGill University.
K. Muis (*):G. Franco
Department of Educational and Counselling Psychology, McGill University, 3700 McTavish Street,
Montreal, QC H3A 1Y2, Canada
of human knowledge? And, how does one justify a proposition to be true? From the ancient
Greek philosophers, like Socrates, Plato, and Aristotle, to the medieval philosophers, like
Saint Augustine and Saint Thomas Aquinas, and from the early modern philosophers,
including Descartes, Hobbes, Locke, Hume, and Kant, to more contemporary views, several
epistemological positions have been developed and refined. These include empiricism,
rationalism, foundationalism, and coherentism, to name a few (Audi 1999).
Today, educational psychologists are also interested in the nature of knowledge and
knowing but have pursued epistemological questions in different ways. Beginning with
Perry’s (1970) work on the development of students’ views of knowledge, a number of
research paradigms have explored individuals’ beliefs about the nature of knowledge and
knowing, or epistemic beliefs. Within this context, researchers have examined how beliefs
develop (Belenky et al. 1986; Kitchener and King 1981; Kuhn 1991) and how they
influence various facets of learning including how individuals engage in problem solving
(Muis 2008; Schoenfeld 1985), monitor comprehension (Ryan 1984; Schommer 1990;
Schommer et al. 1992), calibrate metacognition (Pieschl et al. 2008; Stahl et al. 2006)
generate arguments (Mason and Scirica 2006), evaluate information retrieved from the
Internet for learning purposes (Mason et al. 2009; Strømsø and Bråten 2009), and how
beliefs affect motivation (Hofer 1999; Muis and Foy 2009; Richter and Schmid 2009) and
achievement (Hofer 1999; Muis 2008; Schommer et al. 1992). Moreover, various research
programs have investigated students’, teachers’, and other lay people’s beliefs about the
nature of knowledge and knowing, including what knowledge is, how knowledge is
constructed, and how it is evaluated (for reviews, see Hofer and Pintrich 1997; Muis 2004;
Schommer 1994). As such, the notion of epistemic beliefs has been incorporated into more
encompassing theories, such as metacognition (e.g., Hofer 2004; Kitchener 1983; Kuhn
1999, 2000) and theoretical models of self-regulated learning (e.g., Muis 2007; Winne and
Hadwin 1998). Schunk (2001) defines self-regulated learning as, “learning that results from
students’ self-generated thoughts and behaviors that are systematically oriented toward the
attainment of their learning goals” (p. 125).
In one integrated model, Muis (2007) proposed that epistemic beliefs are activated
during the task definition phase of self-regulated learning. Once activated, epistemic beliefs
influence the standards that individuals set for learning. Muis further proposed that
epistemic beliefs translate into epistemological standards that serve as inputs to
metacognition. Although there is some evidence to support these claims (e.g., Hofer
2004; Kuhn 1999, 2000; Muis 2008), empirical work is still needed to better understand
how and why epistemic beliefs influence metacognitive processes. In particular, Pintrich
(2002) made a call for more studies to advance theoretical specifications of relations
between epistemic beliefs and facets of self-regulation and metacognition, and further
suggested that future work move beyond one-point-in-time correlational designs. Moreover,
as Muis (2004) noted in her review, the majority of studies that have examined relations
between epistemic beliefs and cognitive and metacognitive processes have relied solely on
self-report measures of these constructs. As Winne and Perry (2000) and Muis (Muis et al.
2007; Winne et al. 2002a) demonstrated, there are several issues that limit contributions of
studies that use only self-report measures. For example, when asked what cognitive and
metacognitive processes they use during learning, students may respond to self-report items
by constructing answers grounded in a schema rather than by retrieving facts about events.
This typically results in inaccurate estimates of studying behaviors (Winne et al. 2002b). To
address this issue, Winne et al. suggested researchers use traces, data about actual studying
events recorded while learners study and solve problems. Coupled with self-report data, the
use of traces provides a method by which to triangulate results.
K.R. Muis, G.M. Franco
Within the metacognition literature, Veenman et al. (2006) note that much more research
is necessary to advance theoretical understanding of how individual differences, like
epistemic beliefs, interact with metacognition and its various components. Accordingly, the
purpose of this study was to address these gaps in the literature. In particular, we examined
relations between epistemic profiles (beliefs about how knowledge is derived and justified),
metacognition, problem solving, and achievement in the context of learning in an
educational psychology course. We add to the current literature by exploring relations
using both self-reported beliefs and actual behaviors in a learning context. If results are
consistent across the two types of methods used to trace learning behaviors, then validity of
the results is bolstered. We first present relevant theoretical frameworks followed by
empirical evidence of relations between epistemic beliefs and metacognitive processes. We
end our literature review with research questions and hypotheses.
Metacognition and self-regulation
In general, metacognition refers to knowledge of one’s own cognitive processes, that is,
knowledge of how one monitors cognitive processes and how one regulates those processes
(Flavell 1976). Over the decades, various definitions of metacognition have developed that
include consideration of both cognitive and metacognitive processes, and numerous labels
have been used like metacognitive awareness, metacognitive beliefs, metacognitive
knowledge, and metacognitive skills, to name a few (Veenman et al. 2006). Moreover,
when metacognition is located within broader theoretical frameworks, variations and
inconsistencies are found with regard to its role. For example, as Veenman et al. (2006)
noted, some theorists view self-regulation to be a subordinate component of metacognition
(Brown and DeLoache 1978), whereas others regard self-regulation as a superordinate to
metacognition (Muis 2007; Winne and Hadwin 1998). For the purposes of this paper, we
explore metacogniton from a regulation of cognition perspective, situated as a subordinate
According to Brown et al. (1983) metacognition can be divided into two components:
knowledge of cognition, and regulation of cognition. Knowledge of cognition refers to the
relatively stable information that learners have about their own cognitive processes
including knowledge of how they store and retrieve information (Brown et al. 1983).
Regulation of cognition refers to processes of planning activities prior to engaging in a task,
monitoring activities during learning, and checking outcomes against set goals. These
processes are assumed to be unstable, task and situation dependent (Brown et al. 1983). The
current study focuses on the processes of regulation of cognition.
Within the self-regulated learning framework, several theorists view metacognition as
the hub of self-regulation (e.g., Borkowski et al. 2000; Lefebvre-Pinard and Pinard 1985;
Muis 2007; Paris and Winograd 1990; Winne and Hadwin 1998). They described how
metacognition can facilitate or constrain facets of self-regulated learning, and propose that
metacognition is one key moderator of performance (Lefebvre-Pinard and Pinard 1985;
Paris and Winograd 1990). How might epistemic beliefs influence metacognitive processes
during self-regulation? We present one model that describes this relationship.
Epistemic beliefs, self-regulated learning, and metacognition
To help establish why epistemic beliefs may be related to various facets of self-regulated
learning, Muis (2007) proposed an extension of previous models by incorporating epistemic
beliefs within the self-regulated learning framework. Similar to most models of self-regulated
Support for the consistency hypothesis
learning, but particularly Winne and Hadwin’s (1998) and Pintrich’s (2000), Muis proposed
four phases of self-regulated learning and four areas for regulation. The four phases include 1)
task definition, 2) planning and goal setting, 3) enactment, and, 4) evaluation. The four areas
for regulation include a) cognition (e.g., knowledge activation, knowledge of strategies), b)
motivation and affect (e.g., achievement goals, achievement attributions, self-efficacy), c)
behavior (e.g., time, effort), and d) context (resources, social context).
In the first phase, an individual constructs a perception of the task, which is influenced
by external conditions, such as context, and internal conditions, such as prior knowledge,
motivation, and an individual’s epistemic beliefs (Muis 2007). During the second phase,
components from the first phase influence the types of goals an individual sets for learning
and the plans made for carrying out the task. Planning includes selecting the types of
learning and metacognitive strategies an individual may use to carry out the task,
identifying the type of information on which to focus during learning, and determining the
extent to which the veracity of information should be evaluated (Muis 2007). The third
phase begins when an individual carries out the task by enacting the chosen learning and
metacognitive strategies. Finally, in the last phase of self-regulated learning, individuals
engage in reflection and reaction to evaluate the successes or failures of each phase or
products created for the task, or perceptions about the self or context (Muis 2007).
According to Muis (2007) and Hofer (2004), it is during the second phase of self-
regulation that epistemic beliefs may translate into epistemological standards, which may
serve as a key source of information during epistemic metacognition. As Hofer (2004)
described, Kitchener (1983) and Kuhn (1999, 2000) developed theoretical frameworks that
located epistemic beliefs at the metacognitive level. Within the broader context of
cognition, both Kuhn (1999, 2000) and Kitchener (1983) have proposed that epistemolog-
ical thinking or epistemic understanding can be best understood in terms of its relation to
metacognition. Kuhn, for example, describes metacognition as “meta-knowing” that
includes any form of cognition, which is comprised of three levels: metacognitive knowing
(knowing about declarative knowledge), metastrategic knowing (knowing about procedural
knowledge), and epistemological meta-knowing (knowing about knowledge and knowing).
It is epistemological meta-knowing that ties directly to cognitive processes, particularly that
of monitoring. As Kuhn suggests, epistemological meta-knowing entails monitoring one’s
understanding of the complexity of problems, the certainty and limits of knowledge, and the
evaluation of evidence.
Empirical evidence of relations between epistemic beliefs and metacognition
A few empirical studies support predicted relations between epistemic beliefs and
metacognitive processes (Hofer 2004; Muis 2008; Pieschl et al. 2008; Stahl et al. 2006).
For example, Pieschl et al. (2008) explored relations between epistemic beliefs, prior
knowledge, and metacognitive calibration. Students from biology and the humanities
learned about genetic fingerprinting using a hierarchical hypertext on the topic. From one
level to the next, information presented to students in each node was increasingly complex.
Pieschl et al. found that the more students believed in the complexity of knowledge, the
more information they processed and the more they judged that content to be less
comprehensible than students who believed that knowledge is more simply structured. This
effect was more pronounced as the complexity of the information increased; students who
believed knowledge is complex adjusted processing of information based on the complexity
of the information. Moreover, students who believed knowledge is complex performed
better on a test of learning outcomes than students who believed knowledge is simple.
K.R. Muis, G.M. Franco
In another study, Muis (2008) examined relations between epistemic profiles and self-
reported and actual metacognitive strategy use in the context of mathematics problem
solving. She used Schoenfeld’s (1985) model of self-regulated learning in mathematics and
Royce’s (1983) psycho-epistemological profile model to predict differences in regulation of
cognition as a function of individuals’ epistemic profiles. Similar to Schommer’s (1990)
position that epistemic beliefs are multidimensional, Royce (1959) proposed that beliefs
about knowing involve two dimensions—beliefs about how knowledge is derived, and
beliefs about how knowledge is justified. Moreover, like Schommer, Royce hypothesized
these general beliefs may vary along several continua. For example, similar to Schommer’s
belief in authority dimension, Royce proposed that some individuals might believe that
knowledge is derived via logic and reason, whereas others may believe that knowledge is
derived via observation, or via universality of awareness and insight. In contrast to
Schommer, however, Royce hypothesized these dimensions as distinct, whereas Schommer
suggested these same beliefs vary along the same dimension. That is, Schommer proposed
that at one end of the continuum, individuals may believe in authority as the source of
knowledge, whereas at the other end of the continuum, individuals may believe that
knowledge is derived empirically or via logic and reason. Royce (1978) further argued that
if it can be shown there are different beliefs about knowing then it is reasonable to
anticipate that people will combine these different beliefs in a particular preference order
which can be described as a hierarchical structure.
Following a decade’s worth of theoretical and empirical work (e.g., Royce 1967, 1974;
Royce and Rozeboom 1972), Royce (1978) concluded there are three basic beliefs about
knowing: rationalism, empiricism, and metaphorism. He argued that although there have
been several theories of knowledge proposed in the history of philosophical thought, these
three systems of knowing are basic because of their direct dependence on cognitive
processes on one hand, and their empirical testability on the other hand. Accordingly,
people can know only in terms of the three cognitive processes that underlie these three
systems of knowing (Royce 1978).
To test his model, Royce and Mos (1980) developed the Psycho-Epistemological Profile
(PEP) to profile an individual’s epistemological hierarchy. The epistemological dimensions
measured by the PEP reflect three basic approaches to knowing: rationalism, whereby
individuals believe knowledge is derived and justified through reason and logic;
empiricism, whereby individuals believe knowledge is derived and justified through direct
observation; and metaphorism, whereby individuals believe knowledge is derived via
intuition and justified via universality. These three approaches to knowing are considered to
be three different epistemic profiles that depend on a particular sub-hierarchy of cognitive
processes: conceptualizing (focusing on logic); perceiving (focusing on observables); and,
symbolizing (analogical thinking), respectively. Although Royce acknowledged these
cognitive processes do not function independently and that, for a comprehensive
understanding of the world, all three ways of knowing should be invoked, a person is
partial to one of the cognitive processes that reflects his or her predominant epistemology.
These hypotheses are supported by research examining relations between epistemic profiles
and cognitive processes used while learning (see Royce and Mos 1980).
Using Royce’s (1978) theoretical framework, Muis (2008) epistemically profiled
individuals using three different profile types: rationalism, empiricism, and a combined
group that espoused both rational and empirical approaches to knowing. After they
completed inventories to assess their epistemic profiles and self-reported metacognitive
strategy use, students in Muis’ (2008) study participated in two problem-solving sessions.
Episodes were coded for planning, monitoring, control, use of empirical and rational
Support for the consistency hypothesis
argumentation, and justification for solutions. Based on Schoenfeld’s (1982) model of self-
regulated learning for mathematics problem solving, Muis predicted that students profiled
as rational would self-report and engage in more regulation of cognition than students
profiled as empirical. Moreover, she predicted that students profiled as rational would
justify their solutions based on valid proofs and theorems underlying the problems, whereas
students profiled as empirical would justify their solutions based on observable information.
Results supported her hypotheses. For both self-reported metacognitive self-regulation and
regulation of cognition during problem solving, students profiled as rational had the highest
self-reported mean and actual frequency of regulation of cognition compared to students
profiled as empirical. Moreover, students profiled as rational justified their solutions as
correct using the logical information (e.g., proofs and theorems) they derived to solve the
problems. In contrast, students profiled as empirical justified their solutions as correct based
on empirical information, such as physically measuring lines and circles created during
The current study
We extend Muis’ (2007, 2008) work by exploring relations between epistemic profiles,
regulation of cognition, and achievement within the context of learning in an educational
psychology course. In Muis’ (2008) study, she examined whether individuals’ beliefs about
how knowledge is justified and how knowledge is derived predicted the extent to which
they regulated cognitive processes and whether those beliefs predicted the types of
information individuals used to solve problems and justify solutions. Consistent with
Hofer’s (2004) and Muis’ (2007) theoretical propositions, results from Muis’ (2008) study
support the notion that individuals’ epistemic beliefs translate into epistemological
standards that influence metacognitive strategies chosen for a particular task.
To explain why epistemic beliefs are related to metacognitive processes, Muis (2008)
proposed a consistency hypothesis. That is, based on Schoenfeld’s (1987) and Royce’s
(1978) work, Muis hypothesized that in the context of a learning situation, when the
epistemic nature of the domain is consistent with an individual’s epistemic profile, more
regulation of cognition should result. For example, in Muis’ study, given that the underling
epistemology of mathematics is rationalism, individuals profiled as predominantly rational
should plan strategies that are based on rational approaches to problem solving, should
monitor problem solving based on logical and coherent standards, and should evaluate the
veracity of their solutions based on logical and coherent information. In contrast,
individuals profiled as empirical should engage in less regulation of cognition as their
standards focus on empirical information. Given that empirical data and evidence is not as
common in mathematics, the frequency with which regulation of cognition occurs should
also be less. Muis (2008) suggested that future work is needed to further explore how
beliefs translate into epistemological standards. In particular, she recommended a domain
that entails a different underlying epistemology than mathematics should be explored to
assess whether the consistency hypothesis generalizes across other contexts. Our study
responds to this call.
Following Muis’ (2008) work, and to advance theoretical understanding of relations
between epistemic beliefs and metacognition, we focus on a different context to examine
whether individuals’ epistemic profiles predict the extent to which they engage in
metacognitive processes. Specifically, we focused on the domain of educational
psychology. We chose educational psychology given that this domain is considered both
K.R. Muis, G.M. Franco
a rational (developed from theoretical considerations) and empirical (theories are
empirically tested) domain (MacKay 1988; Royce 1978). As MacKay (1988) argued, the
empirical epistemology underlying educational psychology focuses on developing a reliable
body of knowledge that can be applied to real-world situations, such as how teachers can
motivate students to learn. To develop this reliable body of knowledge, researchers
operationally define constructs that can be empirically measured and/or observed.
Relationships between variables or differences between groups are then empirically
examined. In contrast, the rational epistemology underlying educational psychology is
used to develop theories to help explain relationships between variables and differences
between groups, and is used to predict outcomes when empirical studies are conducted.
Importantly, in educational psychology, the two underlying epistemologies work hand-in-hand
to advance knowledge in the field; empirical evidence is not meaningful unless there is a
plausible mechanism for explaining outcomes (MacKay 1988).
Accordingly, students enrolled in an educational psychology course completed a self-
report questionnaire designed to measure regulation of cognition. Students also completed a
self-report instrument that profiles individuals with regard to their beliefs about how
knowledge is derived and how it is justified. Three profiles were used for this study:
predominantly rational, predominantly empirical, or a combination of both rational and
empirical. All participants from the larger sample were invited to participate in a second
study. For the second component, students completed a prior knowledge test on motivation,
read a short chapter on how teachers can motivate students, and then completed two
problems using a think aloud protocol. Episodes were coded for planning, monitoring, and
control during problem solving, and solutions were coded for response quality. Two
research questions were examined in this study: Are there mean differences in self-reported
and actual regulation of cognition as a function of epistemic profile? Are there mean
differences in problem solving performance as a function of epistemic profile?
Based on theoretical (Hofer 2004; Muis 2007; Royce 1978) and empirical (Hofer 2004;
Muis 2008) considerations, and given that educational psychology is considered both a
rational and empirical domain (Royce 1978), we predicted that individuals profiled as both
rational and empirical in their approaches to knowing would engage in the highest self-
reported and actual frequency of regulation of cognition compared to the other two groups.
Moreover, given that we expected individuals profiled as both rational and empirical to
engage in more regulation of cognition, we also expected these individuals to obtain higher
levels of performance compared to the other two groups. No differences were expected
between individuals profiled as rational and those profiled as empirical on regulation of
cognition or performance.
Two hundred thirty-four students volunteered to participate in the first study (N=189
females, 80.8%). At the time of the study, all participating students were enrolled in an
educational psychology course. Two students were enrolled in their first year of university,
46 were enrolled in their second year, 125 were enrolled in their third year, 50 were
enrolled in their fourth year, and 11 were students that had graduated but were taking extra
Support for the consistency hypothesis
courses for credit. The mean age was 24 (SD=6.63, range=18−54 years old). Of the 234
participants, three failed to complete a significant number of items and thus were dropped
from the analyses.
Epistemic profiles questionnaire Students’ epistemic profiles were assessed using the
Psycho-Epistemological Profile (PEP; Royce and Mos 1980). This 90-item, self-report
measure is composed of three scales that reflect distinct belief systems: empiricism,
rationalism, and metaphorism. Each scale contains 30 items, and students rate each item on
a five-point Likert scale ranging from “completely disagree” (a rating of 1) to “completely
agree” (a rating of 5). Example items include: “When people are arguing a question from
two different points of view, I would say that the argument should be resolved by actual
observation of the debated situation” (empiricism); “When people are arguing a question
from two different points of view, I would say that each argument should be evaluated
based on its logical coherence” (rationalism); and “When people are arguing a question
from two different points of view, I would say that each should endeavor to assess honestly
his or her own attitude and bias before arguing further” (metaphorism). The highest score of
the three subscales represents an individual’s predominant epistemology. Factor analyses
indicate that the PEP is comprised of three independent scales containing content aligned
with the proposed belief systems (e.g., Muis 2008; Schopflocher and Royce 1978).
Moreover, previous research using the PEP has demonstrated high internal consistency
(Muis 2008) and test–retest reliability coefficients ranging from 0.80 to 0.90 (e.g., Royce
and Smith 1964; Smith et al. 1967).
Metacognitive self-regulation To measure the frequency with which students self-
reportedly use planning, monitoring, and control strategies to study for their educational
psychology course, students completed the Motivated Strategies for Learning Questionnaire
(MSLQ; Pintrich et al. 1991). The MSLQ is an 81-item measure that is widely used to
assess undergraduate students’ self-reported use of learning strategies and motivational
orientations. For the purpose of this study, only the metacognitive self-regulation subscale
was used. This subscale includes 12 items that refer to metacognitive processes of planning,
monitoring, and regulating cognitive activities. An example item is “I ask myself questions
to make sure I understand the material I have been studying in class.” Students rate each
item on a seven-point Likert scale ranging from “not at all true of me” (a rating of 1) to
“very true of me” (a rating of 7).
At the beginning of the semester, and prior to covering content on self-regulated learning,
motivation, and epistemic beliefs, participants spent approximately 1 h completing all
components of the PEP and the MSLQ. The PEP was used to categorize students as
predominantly empirical, rational, or a combination of both rational and empirical in their
approaches to knowing.1Scores for the empirical, rational, and metaphorical scales were
1Like Muis (2008), we did not include individuals profiled as predominantly metaphorical. There were only
18 individuals that fit this profile. Given the small sample size, it was not possible to conduct statistical
analyses with these individuals.
K.R. Muis, G.M. Franco
computed by summing all 30 items for a total subscale score for each dimension (the
minimum possible score was 30 and the maximum possible score was 150). To create each
of the profile groups, we used Muis’ (2008) methodology wherein “perfect” subscale scores
were used as a basis from which to calculate two standard errors around each of the scores
that could be used to categorize students as low, moderate, or high along each of the
dimensions. Specifically, since the lowest and highest scores on the rationalism,
empiricism, and metaphorism subscales were 68 and 138, 65 and 140, and 60 and 138,
respectively, “perfect” subscale scores of 70 were considered low, subscale scores of 95
were considered moderate, and subscale scores of 120 were considered high.
Two standard errors were then computed for each subscale to create a range around each
of the “perfect” scores that could be used to categorize students as low, moderate, or high
along the three dimensions. Specifically, scores that fell between 58 and 82, 83 and 107,
and 108 and 132 on the each of the scales were considered low, medium, and high,
respectively. Using these criteria, individuals were labeled as predominantly rational,
predominantly empirical, predominantly metaphorical, or a combination of both rational
and empirical. For example, if an individual had a high score on the rationalism scale,
followed by a moderate or low score on empiricism and metaphorism, this individual would
be considered predominantly rational. When scores from rationalism and empiricism fell in
the same range, for example, both high, both moderate, or both low, then those individuals
were profiled as both rational and empirical. In total, 52 students were profiled as
predominantly empirical, 55 students were profiled as predominantly rational, and 124
students were profiled as both rational and empirical.
Data were screened for normality. All scales’ scores were normally distributed with
skewness and kurtosis values within acceptable ranges; skewness ranged from −.12 to .33,
and kurtosis ranged from .25 to .85. Means, standard deviations, and reliability coefficients
are presented in Table 1 for relevant subscales for the PEP and the MSLQ. Consistent with
previous research (Muis 2008; Pintrich et al. 1991; Royce and Mos 1980), reliability
estimates for the subscales on the PEP and MSLQ were considered acceptable with internal
consistency α coefficients ranging from .70 to .77.
Prior to examining variations across profiles as a function of regulation of cognition,
differences were examined within groups to ensure there were no differences in regulation
of cognition as a function of degree of epistemic profile. That is, we first assessed whether
individuals profiled as predominantly rational, for example, differed as a function of
whether they were “high” on rationalism or “moderate” on rationalism. No differences were
Table 1 Descriptive statistics and reliability coefficients for each profile and metacognitive self-regulation
Inventories’ Subscales MeanSD
SD standard deviation
b1–7 point scale
Support for the consistency hypothesis
found across levels within profiles (all p>.05). Accordingly, moderate and high profile
groups were merged for subsequent analyses (e.g., the rate at which individuals engage in
regulation of cognition is not a matter of epistemic profile score). Analyses were then
conducted to examine whether there were differences in self-reported metacognitive self-
regulation between students profiled as rational, both rational and empirical, and empirical
in their approaches to knowing. Means and standard deviations for each group for self-
reported metacognitive self-regulation are presented in Table 2.
A univariate analysis of variance revealed statistically detectable differences among the
three groups, F (2, 228)=3.13, p=.04, η2=.03. As predicted, participants profiled as both
rational and empirical had the highest mean score on self-reported metacognitive self-
regulation, followed by individuals profiled as rational, with those profiled as empirical
having the lowest mean. Post hoc analyses, using the LSD procedure, revealed that
individuals profiled as both rational and empirical had a higher self-reported mean score on
metacognitive self-regulation than individuals profiled as predominantly empirical (p<.01,
d=.38). No other differences were found.
From the larger sample in Study 12, a sub-sample of 78 students volunteered to participate
in the second study (N=64 females, 82.1%). Students completed the second component of
the study 1 week following completion of the first study. Five students were enrolled in
their first year of university, 17 were enrolled in their second year, 32 were enrolled in their
third year, 16 were enrolled in their fourth year, and 8 were students who had graduated but
were taking extra courses for credit. The mean age of the sample was 23.09 (SD=5.92). Of
the 78 students, 21 were profiled as predominantly rational, 24 were profiled as
predominantly empirical, and 33 were profiled as both rational and empirical.
Materials and procedure
To ensure differences between groups were not a function of differences in prior
knowledge, students were first given a test that asked them to define 10 motivation
Table 2 Means and standard deviations for metacognitive self-regulation as a function of epistemic profile
Profile Metacognitive self-regulation (MSR)a
Both rational and empirical
a1–7 point scale
2All students in the first sample were given the opportunity to participate in the second study. No differences
in self-reported scores were found between the sub-sample and larger sample.
K.R. Muis, G.M. Franco
constructs (e.g., intrinsic motivation, self-efficacy). Students were given 1 point for
definitions that were accurate reflections of the constructs, .5 points for partially correct
definitions, and 0 for definitions that were unrelated to the constructs. After students
completed the prior knowledge test, they were asked to study a short chapter on motivation,
excerpted from an educational psychology textbook (Woolfolk et al. 2003). Once they
completed the chapter, participants were then given two problem scenarios and a series of
questions related to the study material (presented in “Appendix A”). Problem scenarios
were distributed in a counterbalanced order, and students were told that they could refer
back to the chapter during their problem-solving attempts. For both the study session and
the problem-solving attempts, students were asked to think aloud following Ericsson and
Simon’s (1993) Type I protocol. Participants were given as much time as they needed to
complete the study session and problem-solving attempts.
Problem-solving sessions were then transcribed. Transcripts were coded for evidence of
planning, metacognitive monitoring, and metacognitive control. A rubric was also
developed to evaluate the quality of students’ solutions. Students’ scores on the rubric
served as an indicator of achievement.
Protocol coding schemes
In line with previous research by Schoenfeld (1982, 1985) and Muis (2008), protocol coding
schemes were used to identify the sequences of overt actions that participants took in the
process of solving problems. Three overt actions were analyzed as indicators of metacognitive
self-regulation: planning, metacognitive monitoring, and metacognitive control.
Planning A plan is a course of action an individual decides to implement prior to solving a
problem. For the current study, two kinds of plans were observed: local plans and global
plans. These plans were distinguished by the different contexts to which they referred. Local
plans addressed the problem-solving task that participants confronted in the here-and-now.
Evidence of a local plan was noted if participants overtly stated a specific heuristic, strategy,
or tactic (Winne and Hadwin 1998) as a plan for approaching the problem, prior to actually
implementing the tactic or strategy. Examples of local plans include: “I’m gonna refer back
to the chapter 11” and “I guess I might wanna use that utility value…” In contrast to local
plans, which specifically referenced the task at hand, global plans referenced the
hypothetical situation presented in the problem-solving scenarios. For example, one of
the problems required students to describe a plan they would create in an attempt to
motivate an unmotivated 7th grade class. Some students responded to this problem by
projecting themselves into the future and describing their intended course of action. These
hypothetical strategies or tactics were coded as global plans. Examples of global plans
include: “First of all, before thinking of a strategy, I would have to go in with a certain
attitude” and “First I would look at the classroom environment, ensure that it’s not too
distracting or there aren’t too many interruptions.” Tallies of local plans and global plans
were collapsed into an overall score for planning.
Metacognitive monitoring and control Recall that metacognition refers to knowledge about
one’s own thinking processes, or awareness of one’s own cognitive processes and how they
work (Flavell 1976). This knowledge is used to monitor and regulate cognitive processes.
For example, if the products created during learning or problem solving fall short of the
standards one sets for a task, one can change the tactics or strategies for working on that
task (Winne and Hadwin 1998). Metacognitive monitoring describes this process of
Support for the consistency hypothesis
comparing learning products to standards, whereas metacognitive control refers to the
regulating or fine-tuning and adjusting activities, such as changing a course of action by
implementing a new tactic or strategy or enacting a previous one. Metacognitive monitoring
activities include, for example, examining whether progress is being made toward a goal,
checking whether errors are being made, and self-testing and questioning. Examples of overt
metacognitive monitoring included: “Is this a completed plan? What else can the teacher do to
present the topic?” “I’m just reading it through one more time.” “Ah, I think I got it.”
Examples of metacognitive control included both overt statements and actions taken during
problem solving. These included: actions made to abandon an unsuccessful approach or
change a course of action when a previous course appeared unfruitful, and statements such as
“Okay, forget what I said before. I would say have a direct communication between parents
and teachers” and “I’m going to go on to question three and go back to that.”
Achievement As a measure of achievement, students’ solutions to the two problem
scenarios were evaluated according to a rubric developed by the second author. The rubric
was designed to assess the quality of students’ responses, taking into account five di-
mensions: clarity, completeness, relevance of content, justification, and validity. For each
dimension, students’ responses received a score ranging from 0 to 4. Along the clarity
dimension, a score of zero indicated the student’s response was not clearly articulated,
whereas a score of four indicated the response was very clear and that all the main points of
the answer could be easily identified. For completeness, a score of zero indicated that the
student failed to provide a response to the scenario; a score of four indicated that all salient
parts of the scenario had been addressed in the student’s response. The content dimension
assessed whether the student’s response incorporated relevant material from the assigned
chapter. If no relevant material was included in the student’s response, s/he received a score
of zero for content; a score of four was assigned if the student consistently drew on relevant
material from the chapter to develop the response. For the justification dimension, a score
of zero indicated that the student failed to justify his/her response, whereas a score of four
indicated that the student supported his/her argument with rational and/or empirical
evidence from the assigned reading. Finally, the validity dimension assessed whether or not
the student’s response offered a valid (i.e., plausible) solution to the hypothetical problem
presented in the scenario. For this dimension, a score of zero indicated that the solution was
not at all plausible, whereas a score of four indicated that the response was highly plausible
and could likely provide a successful resolution to the problem. For each scenario, a
composite score (out of 20) was calculated by totaling the scores along each of the five
dimensions of the rubric. Finally, for each participant, the two composite scores (one for
each scenario) were combined for an overall achievement score (out of 40). The mean
achievement score was 21.62 (SD=8.61) out of a possible 40 points.
Instances of planning, metacognitive monitoring, and metacognitive control were blindly
coded by two pairs of trained raters, none of whom had knowledge of participants’ epistemic
profiles while coding. As a measure of inter-rater reliability, agreement percentages were
the first pair of raters, percentages ranged from 70 to 100% for planning, 82 to 100% for
metacognitive monitoring, and 75 to 100% for metacognitive control. For the second pair of
raters, percentages ranged from 81 to 100% for planning, 82 to 100% for metacognitive
monitoring, and 80 to 100% for metacognitive control. Similarly, two independent raters
evaluated the quality of students’solutions along the five achievement dimensions. Agreement
percentages ranged from 89 to 97%. Disagreements were resolved through discussion.
K.R. Muis, G.M. Franco
Given the technical and conceptual issues that researchers have identified with the use of
self-report measures (Muis et al. 2007; Winne et al. 2002a; Winne and Perry 2000), the
purpose of the second study was to examine whether there were differences in the use of
metacognitive self-regulation during actual problem solving as a function of epistemic
profile. Participants’ prior knowledge of motivation constructs, duration of problem-solving
attempts, and performance on problem scenarios were measured. Transcriptions of
participants’ problem-solving attempts were coded for evidence of planning, metacognitive
monitoring, and metacognitive control. Means and standard deviations for total regulation
of cognition, prior knowledge, time on task, and achievement are presented in Table 3.
To ensure differences between groups on regulation of cognition and achievement were not a
function of time on task or prior knowledge, two one-way ANOVAs were conducted. No
differences were found (both ps>.16. Overall prior knowledge score was 1.98 out of a possible
revealed statistically detectable differences between groups, F(2, 74)=3.77, p=.02, η2=.09. As
predicted, participants profiled as both rational and empirical engaged in more regulation of
cognition than those in the other two groups. Participants profiled as rational had the lowest
occurrence of these behaviors. Using the LSD procedure, post hoc analyses were conducted to
examine which groups statistically differed. Individuals profiled as both rational and empirical
had a statistically detectable higher mean for regulation of cognition than individuals profiled
as empirical (p=.02, d=2.72) and rational (p=.02, d=2.75). No other differences were found.
To examine whether individuals profiled as both rational and empirical had higher
quality answers on the problems than individuals in the other two groups, an analysis of
variance was conducted. Results revealed statistically detectable differences between
groups, F(2, 74)=5.09, p=.008, η2=.12. As predicted, individuals profiled as both rational
and empirical had higher levels of achievement than individuals profiled as rational or
empirical. Post hoc analyses, using the LSD procedure, revealed that individuals profiled as
both rational and empirical had a higher mean performance than individuals profiled as
empirical (p=.002, d=2.50). No other statistically detectable differences were found.
Table 3 Means and standard deviations for planning (P), metacognitive monitoring (M), metacognitive
control (C), prior knowledge, time on task, and achievement as a function of epistemic profile
Profile Total frequency
of P, M, C strategies
Prior knowledgeaTime on taskbAchievementcN
Both Rational and Empirical33
SD is presented in parentheses
SD standard deviation
aOut of 15 possible points
cOut of 40 possible points
Support for the consistency hypothesis
Contemporary models (Muis 2007; Winne and Hadwin 1998) of self-regulated learning and
theoretical frameworks within the epistemic beliefs literature (e.g., Hofer 2004; Schommer-
Aikins 2004) have proposed that individuals’ beliefs about knowledge and knowing are
influential in the learning process. Although researchers have explored how epistemic
beliefs influence various cognitive (Schommer 1990), metacognitive (Pieschl et al. 2008;
Stahl et al. 2006), and motivational factors (Hofer 1999; Muis and Foy 2009), fewer studies
have examined relations between epistemic beliefs and metacognitive processes (Hofer
2004; Muis 2008) and, to date, only one study has assessed how epistemic profiles translate
into epistemological resources that influence metacognitive processes (Muis 2008). Based
on Muis’ (2007) integrated model, we investigated how individuals’ epistemic profiles
influence regulation of cognition. In line with this framework, we empirically evaluated
Muis’ (2008) consistency hypothesis that predicts when the predominant epistemology of a
domain of study is consonant with an individual’s epistemic profile, more regulation of
cognition should occur. Moreover, we responded to Pintrich’s (2002) and Hofer’s (2004)
calls for more research to explore relations in the context of actual learning, and to
Veenman et al.’s (2006) request for further research to assess how individual difference
variables, like epistemic profiles, interact with metacognitive processes.
Results from both studies support Muis’ (2008) consistency hypothesis. Students profiled
as both rational and empirical in their approaches to knowing engaged in more regulation of
cognition in terms of self-reports of their study habits for their educational psychology
course as well as in the context of actual problem solving compared to students profiled as
predominantly rational and predominantly empirical. We believe that replicating results
across two different contexts using varying methodologies adds to the validity of Muis’
hypothesis and to the nature of relations between epistemic profiles and metacognition.
Consistent with several theorists (Hofer 2004; King and Kitchener 2004; Kuhn 1999,
2000; Muis 2007), we interpret that students’ beliefs about how knowledge is derived and
justified exert an important influence over the types of information students monitor and
evaluate during learning. As Royce (1978) proposed, individuals profiled as rational
acquire knowledge via logic and reason and evaluate information for logical consistency.
Individuals profiled as empirical, on the other hand, acquire knowledge via perceptual
processes (like observation) and evaluate information for validity and reliability (Royce
1978). We posit that if an individual is profiled as both rational and empirical, he or she
may rely on both types of acquisition processes and evaluate information for logical
coherence as well as validity and reliability. In this regard, when a domain, such as
educational psychology, has an underlying epistemology that is both rational and empirical,
information is available that is both rational in nature as well as empirical in nature. When
sources of information entail both rational and empirical elements, there are greater
amounts of information to coordinate and evaluate. Accordingly, because individuals’
epistemic profiles influence the types of information that are monitored and evaluated (from
an epistemological sense), more regulation of cognition should occur when profiles are
In line with this notion, Kitchener (1983) developed a three-level model of cognitive
processing that distinguishes between cognition, metacognition, and epistemic cognition
whereby each level builds from the previous level. At the cognitive level, processes such as
sensing, decoding, and reasoning occur. At the second level, metacognitive processes
include planning strategies, monitoring progress, and control. The last level, epistemic
cognition, functions in synchrony with the other two levels and includes the monitoring of
K.R. Muis, G.M. Franco
the epistemic nature of learning and problem solving. This entails an awareness of the limits
and certainty of knowing, and the standards that entail the process of knowing.
Accordingly, it could be that in the context of a domain that requires the coordination of
rational and empirical information, individuals’ profiled as both rational and empirical set
standards for learning that also require them to coordinate both rational and empirical
information. This coordination of information is similar to processes that occur in later
stages of developmental models of epistemological knowing (Belenky et al. 1986;
Kitchener and King 1981; Kuhn 1991; Perry 1970). Conceptually, empiricism as an
epistemic profile within Royce’s (1978) framework is similar to King and Kitchener’s
(2004) prereflective thinking stage, whereby individuals believe knowledge is objective and
derived via the senses (e.g., direct observation). The justification process, in turn, occurs
through reliance on correspondence between beliefs and perceptual experience. From
Descartes’ dualistic Cartesianism, whereby mind and world are two separate entities,
empiricism may be construed as viewing knowledge from the world. In contrast,
rationalism with Royce’s (1978) framework is similar to King and Kitchener’s (2004)
quasireflective thinking stage, whereby individuals believe knowledge is derived via reason
and evidence, and justified based on rules specific to the context. This could also be
construed as the MIND component. Finally, a balance between rationalism and empiricism
may be similar to King and Kitchener’s last stage, reflective thinking, wherein individuals
must coordinate competing evidence, evaluate both sense data and logical data, and weigh
the evidence prior to judging the veracity of information. Whether Royce’s (1978) model
can be interpreted within a developmental framework requires empirical scrutiny.
We also suggest that domain-specificity is an important consideration, as do others (e.g.,
Buehl and Alexander 2006; Hofer 2006; Muis 2008; Muis et al. 2006). To explore the
extent to which underlying epistemologies of various domains interact with an individual’s
epistemic profile is an important area for future research. For example, research is needed
wherein students engage in learning and problem solving in three different contexts: one
that requires more rationalizing, one that requires more empirical considerations, and one
that combines both rationalism and empiricism. Using this true experimental design, we
hypothesize an aptitude (profile)—treatment (domain context) effect. This type of design
may also provide further evidence of the validity of Muis’ (2008) consistency hypothesis.
their metacognitive behaviors—were replicated in the second study wherein traces of students’
actual learning behaviors were measured. This replication across measurement tools was also
found in Muis’ (2008) study, which suggests that there is some value in using self-report
measures of students’ typical learning behaviors. Although self-reports have been criticized
for a lack of accurate reflections of learning behaviors (Muis et al. 2007; Winne et al. 2002a),
as Muis et al. (2007) suggest, they do provide an estimate of what students have the tendency
to do for learning in a particular course. Like others (Pintrich 2002), however, we still
recommend that researchers use multiple approaches to measuring students’ learning
behaviors to paint a more accurate picture of learning.
Finally, results from our study also support previous work (e.g., Lefebvre-Pinard and
Pinard 1985; Muis 2008; Paris and Winograd 1990; Schoenfeld 1985) that demonstrates the
more individuals engage in regulatory processes, the higher their level of achievement.
Indeed, metacognition has long been considered an important moderator of performance. If
an individual’s beliefs about knowledge and knowing influence the extent to which he or
she engages in regulation of cognition and influences the types of information on which to
focus, then it is pertinent that educators teach students how to regulate learning as well as
the underlying epistemological foundations of the domains they study. One pressing
Support for the consistency hypothesis
question for us is whether Muis’ (2008) consistency hypothesis generalizes to a domain that
is more empirically grounded. Do individuals profiled as predominantly empirical engage
in more regulation of cognition compared to others when the domain of study is also
empirical? Is the consistency hypothesis a rational hypothesis? We believe it is; but, of
course, its justification awaits further empirical evidence.
Problem scenario: grades and motivation
A parent was in a teacher education program where she learned about the motivation
strategies of building confidence and positive expectations. Her sixth-grade daughter
indicated a desire to improve her reading grade and her mother said she would assist her.
She taught her daughter how to use self-management strategies and gave her specific
feedback about her accomplishment. Her grades began to improve. When the daughter was
given her midterm report, she saw that her grade was lower than she expected and was very
discouraged. The parent talked with the teacher, who explained that he had given lower
grades than students were actually making on all mid-term reports. The teacher reasoned
that students slacked off the last weeks of school and the lower grade would motivate them
to work harder. After the parent explained that this had actually undermined the strategy she
had been using with her daughter, the student was given her correct average.
1.A. What does this situation tell you about the strategies for enhancing motivation in the
classroom? Explain your answer in detail.
2. A. As the principal of this school, develop guidelines for teachers about using
motivation strategies in the classroom.
B. Justify your guidelines.
3. A. If you were to recommend things the teacher could do to improve working with
families to enhance student motivation, what would they be?
B. Justify your answer.
Problem scenario: the unmotivated class
You have just been hired to teach a 7th grade class, which is famously known by all
teachers as, “unmotivated.” You have been briefed that most, if not all of the students, do
not pay attention, do not participate in class, sleep during lectures, and are only motivated
the last 2 min before lunch. Despite all of this, you are convinced that you can turn the
tables around to make this 7th grade class a “high achieving class.” You thus decided to
create a motivation plan.
1. A. What motivational strategy would be first on your plan?
B. Justify your answer.
2. A. Outline all the content of your plan.
B. Justify your plan.
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Support for the consistency hypothesis