Threshold of problematicity theory 1
1Running head: THRESHOLD OF PROBLEMATICITY THEORY
This is a pre-print of the article that was published as
Prins, F.J., Veenman, M.V.J., & Elshout, J.J. (2006). The impact of intellectual ability
and metacognition on learning: New support for the threshold of problematicity theory.
Learning & Instruction, 16 , 374-387.
Copyright Elsvevier; Learning & Instruction is also available
athttp://www.elsevier.com/wps/find/journaldescription.cws_home/956/description#description
Frans J. Prins
Educational Technology Expertise Centre,
Open University of the Netherlands, Heerlen, The Netherlands
Marcel V. J. Veenman
Dept. of Developmental and Educational Psychology,
Leiden University, The Netherlands
&
Graduate School of Teaching and Learning,
University of Amsterdam, The Netherlands
Jan J. Elshout
Dept. of Psychonomics,
University of Amsterdam, The Netherlands
Threshold of problematicity theory 2
Abstract
Three models representing different relations between intellectual ability,
metacognitive skills, and learning were compared. The conditions under
which each of these models holds were investigated, on the basis of the
threshold of problematicity theory (Elshout, 1987). Novice and advanced
learners (N = 44) passed through a computer-simulated inductive-learning
environment of different complexity levels. Results show that correlational
patterns between intellectual ability, metacognitive skilfulness, and learning
outcomes of novice learners at the easy level were similar to the patterns of
advanced learners at the intermediate level. Metacognitive skilfulness rather
than intellectual ability appears essential for learning when learners operate at
the boundary of their knowledge.
Threshold of problematicity theory 3
The Impact of Intellectual Ability and Metacognition on Learning:
New Support for the Threshold of Problematicity Theory
1.
Introduction
Intellectual ability and metacognition are two important determinants for
learning (Veenman, 1993). Intellectual ability is regarded here as the acquired
repertoire of general cognitive skills that is available to a person at a
particular point of time (Humphreys, 1968; Snow & Lohman, 1984). As
Anderson (1996, p. 356) phrased it, “intelligence is the simple accrual and
tuning of many small units of knowledge that in total produce complex
cognition. The whole is no more than the sum of its parts, but it has a lot of
parts.” According to this view, performance on mental ability tests can be
understood in terms of basic information-processing components (Carroll,
1993; Simon, 1976; Sternberg, 1988). This is a cognitive approach of
understanding intelligence rather than the dominant psychometric approach.
Whereas psychometric theories deal primarily with the structural aspects of
intelligence, cognitive theories deal primarily with its processing parts
(Sternberg, 1988).
The concept of metacognition, first introduced by Flavell (1976), refers to
both the knowledge about one’s own cognitive processes (i.e., metacognitive
knowledge) and the regulation of these processes (i.e., metacognitive skills).
Metacognitive knowledge concerns knowledge about the interplay between
person characteristics, task characteristics, and the available strategies in a
Threshold of problematicity theory 4
learning situation (Flavell, 1979), whereas metacognitive skills (i.e. executive
skills; see Kluwe, 1987) concern the self-regulatory activities actually being
performed by a learner in order to structure the problem solving process. The
assessment of metacognitive skills through self-reports is problematic because
it appears that learners have poor insight into their own behaviour (Nisbett &
Wilson, 1977; Prins, Busato, Elshout, & Hamaker, 1998; Veenman, Prins, &
Verheij, 2003). A valid but time-consuming method to assess metacognitive
skills is the use of think-aloud protocols (Brown, 1987; Garner & Alexander,
1989; Veenman, 1993, 2005; Veenman, Elshout, & Groen, 1993). The thinkaloud method taps processes in working memory (Ericsson & Simon, 1993),
which means that automatic problem solving processes cannot be measured
by this method. Thus, the think-aloud method is suitable to assess
metacognitive skills of novice as well as advanced learners as long as the
learning task is complex enough for learners to prevent their problem-solving
activities from being executed automatically. Scores for metacognitive skills
measured with the think-aloud method are strongly related to metacognitive
skills measured through observational data (Veenman, Kerseboom, &
Imthorn, 2000) or log-file data (Veenman, Wilhelm, & Beishuizen, 2004).
For years now educational researchers have been discussing the relations
between intellectual ability, metacognitive skills, and learning (e.g., Davidson,
Deuser, & Sternberg, 1994; Maqsud, 1997; Sternberg, 1985, 1988, 1994;
Swanson, 1990; Veenman, 1993), not in the least because knowledge
Threshold of problematicity theory 5
concerning these relations is essential for the design of adequate instructional
support. There are reasons to consider metacognitive skills and intellectual
ability as distinct concepts. First, metacognitive skills appear to be applicable
over a wide range of tasks (cf. Veenman & Verheij, 2003), while mental
abilities, such as verbal ability and inductive reasoning, apply to a smaller
range of tasks (Sternberg, 1988; Schraw, 1998). Second, evidence implies that
the frontal lobes of the brain are of critical importance for human
metacognition (Metcalfe, 1996; Shimamura, 1996, 2000), whereas cognitive
operations are also located in other areas of the brain (Kalat, 1992; Posner,
Petersen, Fox & Raichle, 1988). Consequently, a person who has lost the most
central metacognitive abilities because of brain damage “… appear to drift
about like a rudderless ship”, even given a high level of cognitive abilities as
measured by a variety of tests (Metcalfe, 1996, p. 404). Third, metacognitive
skills are teachable and supportable (Brown & Palincsar, 1989; Schraw, 1998;
Veenman, Elshout, & Busato, 1994), whereas a durable improvement of more
specific cognitive abilities through training and support is rather difficult to
achieve (Elshout, 1987).
Considering metacognition and intellectual ability as distinct theoretical
concepts does not imply that the two are unrelated. There are three models
that may represent the relations between intellectual ability, metacognitive
skills, and novice learning (Veenman, 1993; Veenman et al., 2004), namely the
intelligence model, the independency model, and the mixed model, each of which
Threshold of problematicity theory 6
are described below. Some researchers (e.g. De Corte & Van Pelt, 2003) tend to
focus on which of these models is the right one. In contrast, we seek to
determine the conditions under which each of these models holds. Based on
Elshout’s (1987) threshold of problematicity theory, which describes the varying
impact of intellectual ability on learning due to task complexity, we suggest
that task complexity is a key variable here. However, the theory still has two
drawbacks. First, the theory does not explicitly include the role of
metacognition. Second, the empirical evidence for the threshold of
problematicity theory is limited as far as realistic learning tasks are concerned.
Thus, the aims of the present study are to extend the threshold of
problematicity theory to the role of metacognition, and to provide empirical
evidence for the theory for learners in a realistic, ill-structured, self-directed
learning task.
1.1 The relation between intellectual ability and metacognitive skills
The first model that represents the relations between intellectual ability,
metacognitive skills, and novice learning is called the intelligence model. This
model regards metacognition as a manifestation of intellectual ability. For
instance, Sternberg (1985, 1988, 1994) conceived metacomponents as an essential
part of human intelligence. Metacomponents are used to decide what to do, to
monitor ongoing activities, and to evaluate the outcome of those activities
after they have been completed and, thus, they are similar to metacognitive
skills. In the same vein, the Planning, Attention, Simultaneous, and Successive
Threshold of problematicity theory 7
(PASS) theory of intelligence (e.g., Das, Naglieri, & Kirby, 1994) conceives
self-regulatory processes as an essential part of human intelligence. The
intelligence model predicts that metacognitive skills and intellectual ability
are highly correlated and that metacognitive skills will not have a predictive
value for learning independent of intellectual ability. Empirical support for
the intelligence model was partly found by Elshout and Veenman (1992).
The second model, referred to as the independency model, predicts the
opposite. Metacognitive skills and intellectual ability are not substantially
correlated and, thus, they are independent determinants of learning. For
instance, Allon, Gutkin, and Bruning (1994), Swanson (1990), Maqsud (1997),
and Minnaert (1996) provided evidence for this model. In their studies, they
found that metacognition and intellectual ability were unrelated predictors of
learning.
The last model, called the mixed model, predicts that metacognitive skills
and intellectual ability share some variance but that metacognitive skills have
a surplus value on top of intellectual ability for the prediction of leaning.
Evidence for the mixed model was, for instance, obtained by Berger and Reid
(1989) and in several studies by Veenman and Elshout (e.g., Veenman &
Elshout, 1995, 1999; Veenman, Elshout, & Meijer, 1997). For a detailed
overview concerning the evidence for the models, see Veenman, Wilhelm,
and Beishuizen (2004) and Veenman and Spaans (2005).
1.2 Threshold of problematicity
Threshold of problematicity theory 8
The threshold of problematicity theory (Elshout, 1987) and the mechanisms that
form the base of this theory may provide insight into when and why the
relations between intellectual ability, metacognition and learning outcomes
change. This theory describes a varying impact of intellectual ability on
learning outcomes, depending on the task complexity. At a very low and at a
very high level of task complexity, the impact of intellectual ability on
learning outcomes tends to be zero. Somewhere in between, at an
intermediate level of task complexity, intellectual ability has maximum
impact on learning outcomes. Due to the differences in domain-specific
knowledge, a particular learning task is experienced as less complex by
advanced learners relative to novice learners (Elshout, 1987; Snow, 1989).
Therefore, the curves for novices and advanced learners will likely have
different positions on the task complexity axis (see Figure 1).
---------------------------------Insert Figure 1
---------------------------------The low or zero correlations between intellectual ability and learning
outcomes at either end of the curve could be caused by a lack of variance in
learning outcomes. However, in several empirical studies variance in learning
outcomes at both ends of the curve was found, while intellectual ability still
had little impact (Raaheim, 1988, 1991; Veenman & Elshout, 1999; Veenman &
Verheij, 2003). Thus, there must be at least another determinant that causes
Threshold of problematicity theory 9
the variance of learning outcomes. Elshout (1987) argued that for every
person there is a critical point on the task complexity continuum, which he
called the threshold of problematicity. Below this threshold, that is, for easy
tasks, smooth, internalised, and fast problem-solving activities may be
observed. The flow of activity is relatively automatic and algorithmic, and
errors
mostly
come from
cognitive slips rather
than fundamental
inadequacies of the learner. Above the threshold, that is, during more
complex
tasks,
task-specific
or
domain-specific
knowledge
becomes
increasingly inadequate. When no problem-solving strategy is available from
memory, the learner must operate in a heuristic, improvisational mode (e.g.,
Anzai, 1991) and will shift to processes aimed at assembling a strategy. Snow
(1989), for that reason, labelled the threshold of problematicity the algorithmicheuristic threshold. To be able to improvise or behave heuristically, one has to
design one’s own behaviour, which is a manifestation of metacognition
(Elshout, 1987).
Strictly speaking, the threshold of problematicity theory just predicts the
varying relation between intellectual ability and learning outcomes, and does
not involve metacognition. Nevertheless, the role of metacognition can be
inferred from the mechanisms described in the threshold of problematicity
theory. It is hypothesised that at the right end of the curve, that is, when tasks
are very complex, the quality of metacognitive skills rather than intellectual
ability is the main determinant of learning outcomes, because learners have to
Threshold of problematicity theory 10
improvise and use heuristics rather than call upon knowledge and skill
components that are associated with intellectual ability. They need to
orientate, work systematically, and evaluate their behaviour to get the initial
learning process on the right track (Veenman, Prins, & Elshout, 2002). This
implies a low correlation between intellectual ability and metacognitive skills.
Later on during the learning process, as learners become more experienced,
learning may require the cognitive sub-tasks associated with intellectual
ability, which have to be regulated. Thus, the threshold of problematicity
theory suggests that the mixed model holds for situations of an average
complexity, that is, for a reduced but substantial zone of problematicity.
Empirical evidence for the (‘pure’) threshold of problematicity theory exists.
Raaheim (1988, 1991), for instance, found a part of the hypothesised curve by
using a longitudinal design in which students were required to solve a
particular task several times. Results showed that the impact of intellectual
ability gradually increased. Veenman et al. (2002) found that in a complex
computer-simulated learning environment, intellectual ability had hardly any
impact on learning outcomes of novices whereas metacognitive skilfulness
was the main determinant of novice learning outcomes. Finally, the general
finding that intellectual ability is either unrelated or weakly related to
performance of experts in several domains (Ericsson & Smith, 1991; Ericsson
& Lehman, 1996) is consistent with Elshout’s (1987) theory. The empirical
evidence, however, is limited and mainly focussed on the relation between
Threshold of problematicity theory 11
intellectual ability and learning outcomes. In order to provide additional
empirical evidence for the threshold of problematicity theory, with respect to
intelligence as well as metacognitive skilfulness, a computer-simulated
inductive-learning task with varying levels of complexity was developed.
1.3 Inductive learning
Inductive-learning tasks require active, self-directed learning. They fit in
current constructivist view of education and, probably for that reason,
nowadays these task types are frequently used in secondary and higher
education (De Jong & Van Joolingen, 1998). In computer-simulated inductivelearning environments, learners can design experiments by changing values
of input variables and observe the resulting changes in values of output
variables. By carrying out experiments, relations between input and output
variables can be induced (De Jong & Van Joolingen, 1998). Thus, this kind of
learning is labelled inductive learning (Holland, Holyoak, Nisbett, & Thagard,
1986). Inductive learning is a problem-solving process that can be
characterized as a search process (Klahr, 2000; Klahr & Dunbar, 1988; Klahr &
Simon, 1999). According to the framework of Klahr and Dunbar (1988), called
scientific discovery as dual search (SDDS), learners can search in two search
spaces: (1) the hypothesis space that consists of possible rules that can be
induced in the learning environment, and (2) the experiment space consisting
of all possible experiments that can be conducted in the learning environment.
Klahr (2000) suggested that advanced learners and experts tend to search the
Threshold of problematicity theory 12
hypothesis space first, and that novices tend to search the experiment space.
Indeed, in complex learning environments, novice learners have limited
knowledge about relevant variables and, therefore, they are particularly busy
conducting experiments in order to identify independent and dependent
variables before they can generate hypotheses (Van Joolingen & De Jong,
1997; Veenman et al., 2002). When task complexity increases, advanced
learners may also reach a point at which their prior knowledge is insufficient
and at which they have to rely on weak methods for problem solving. At that
point an inductive-learning process similar to that of novices may be
observed.
1.4 Research questions
To obtain support for the threshold of problematicity theory, the patterns of
correlations between metacognitive skilfulness, intellectual ability, and
learning outcomes will be examined for novices and advanced learners for
three levels of task complexity. More specifically, the unique contribution of
intellectual ability and the unique contribution of metacognitive skilfulness to
the variance in learning outcomes will be determined, as well as the
contribution shared by intellectual ability and metacognitive skilfulness. The
theory predicts that for tasks that are experienced as complex and unfamiliar,
the learning process will be a heuristic, improvisational assembly process.
Hence, learning outcomes for novices are probably associated with
metacognitive skilfulness rather than with intellectual ability, and thus, the
Threshold of problematicity theory 13
main part of the variance in learning outcomes will be accounted for by
metacognitive skilfulness.
The threshold of problematicity of advanced learners may be positioned
at a higher level of complexity (see Figure 1). When advanced learners reach
their boundary of knowledge, that is, at the most complex level of task
complexity, it is expected that metacognitive skilfulness rather than
intellectual ability will have substantial impact on learning outcomes. At the
intermediate level, we expect a pattern in which both determinants contribute
uniquely to the variance of learning outcomes. At the relative easy level of
task complexity, it is hypothesized that prior knowledge will be the main
determinant of learning outcomes because advanced learners may retrieve the
knowledge that is necessary to complete the majority of the post-test
questions directly from memory. In sum, it is expected that the pattern of
correlations between metacognitive skilfulness, intellectual ability, and
learning outcomes of novice learners at the easy level will be similar to the
pattern of correlations of advanced learners at the most complex level.
2.
Method
2.1. Participants
Three months prior to this study the intellectual ability of 496 first year
psychology students was assessed by a series of paper-and-pencil ability tests,
representing
five
primary
intelligence
factors
(Inductive
reasoning,
Quantitative reasoning, Verbal ability, Closure flexibility, and Sequential
Threshold of problematicity theory 14
reasoning) in Carroll’s re-analyses of factor-analytic studies (Carroll, 1993).
The test battery included tests for Vocabulary (difficult word meanings, 60
items), Verbal Analogies (items taking the form of e.g. foe : hatred = friend : ...?,
40 items), Conclusions (linear syllogisms, 40 items), Number Series (requiring
the completion of numerical series, 30 items), Number Speed (arithmetic
problems of addition, subtraction, multiplication, and division, 90 items), and
Embedded Figures (discrimination of figures in complex line-patterns, 32
items). According to Sternberg (1982), verbal analogies, linear syllogisms, and
number series are tests to measure reasoning skills, which are an important
subset of intelligence. The unweighted mean of the z-scores on these six tests
may be regarded as a measure of intellectual ability or an IQ equivalent
(Veenman & Elshout, 1999). Those students, whose intellectual ability scores
deviated at least 0.80 standard deviation from the mean, were classified as
either being of high or relatively low intellectual ability.
If participants had received physics education for three or fewer years of
their six years of secondary education, they were classified as novice learners,
whereas they were classified as advanced learners if they had received four
years or more physics education. Thus, differences between novice and
advanced learners concerned their domain knowledge, not their experiences
with computer-supported learning environments. Participants in this study
were 44 first-year psychology students (10 relatively low intellectual ability
novices, 12 high intellectual ability novices, 13 relatively low intellectual
Threshold of problematicity theory 15
ability advanced learners, and 9 high intellectual ability advanced learners).
The groups did not differ in sex or age. The participants received study
credits for their participation in the study.
---------------------------------Insert Figure 2
---------------------------------2.2. Computer-simulated learning environment (the Optics Lab)
In the present study a computer-simulated learning environment was used in
the optics domain. In the Optics Lab learners could run experiments by
manipulating light rays and lenses. Figure 2 depicts an example of an
experiment in the Optics Lab. Learners could manipulate objects qualitatively
by dragging them and quantitatively by changing the input of numbers. The
distances between objects and the angles of light rays could be measured.
Light rays were not visible during the movement of an object, so each
situation after a movement of an object was considered as an observation, and
therefore, as an experiment. Experiments could only be run with one lens at a
time. The tasks in the Optics Lab consisted of three phases. Phase 1 dealt with
refraction. In this phase participants could run experiments with four
differently shaped lenses and one light source that had one light ray and
could only be moved horizontally. Phase 2 dealt with focal distances of lenses.
In this phase three thin lenses were available as well as one light source of
three parallel light rays that could be moved horizontally and vertically.
Threshold of problematicity theory 16
Phase 3 dealt with images and magnification. In this phase one light source of
three divergent light rays could be moved horizontally and vertically, and the
same three thin lenses of phase 2 were available.
In each phase learners were asked to infer rules about optics. For
instance, the assignment in phase 1 was: Try to find out, by conducting
experiments, what will happen when a light ray passes through a lens. When does the
emerging light ray intersect with the axis and what determines the place of this
intersection point? What are the differences between the four lenses? Learners could
decide how much time they would spent working on this assignment before
going to the next phase and the next assignment. Each phase started with the
presentation of prerequisite theory concerning that phase. In phases 2 and 3
the rules that could have been inferred in the previous phase were added to
the theory, in order to reduce differences in prior knowledge associated with
earlier phases.
The rules that could be inferred in phase 2 were relatively easy. The
relations between variables were linear, and half of the independent variables
had no effect on the dependent variable. Phases 1 and 3 were more complex
because of the more complex relations between system variables. These
relations were non-linear and contained constraints. An example of such a
relation is: If the light source is positioned very closely to a positive lens, then
the emerging light ray will not intersect with the optical axis. Phase 3 was
assumed to be more complex than phase 1, because in phase 3 there were
Threshold of problematicity theory 17
more independent system variables and, therefore, more rules to be inferred.
Although phase 1 is considered to be more complex than phase 2, learners
started with phase 1 because refraction and positive and negative lenses are
prerequisite concepts for understanding focal distance. Therefore, phase 1 will
be labelled as the intermediate phase, phase 2 as the easy phase, and phase 3 as
the complex phase of the Optics Lab.
2.3. Procedure
All participants passed through the Optics Lab in a single session. The session
started with a standardised twenty-minute instruction on how to operate in
the Optics Lab. Participants could familiarise themselves with the Optics Lab
by executing a set of prescribed actions (e.g. moving a lens in a prescribed
way). After the instruction participants completed an optics pre-test, covering
phases 1–3. They worked in the Optics Lab, running experiments for the next
90 minutes. Note taking was possible, using paper and pencil. Each phase of
the Optics Lab ended with a post-test, which was a parallel version of the pretest. Notes could not be consulted during completion of the tests. After a time
limit of one and a half hour, participants had to stop experimenting but they
were allowed to finish the post-test of the Optics Lab phase in which they
were working.
Participants were asked to think aloud during working in Optics Lab
and during completion of the post-tests. Thinking aloud protocols were taperecorded, transcribed, time tagged and analysed, in order to assess
Threshold of problematicity theory 18
metacognitive skills and to examine test-taking behaviour. The computer also
logged all participants’ actions in the Optics Lab in order to compare
metacognitive skilfulness with actual discovery behaviour.
2.4 Metacognitive skilfulness.
The quality of metacognitive skills was assessed by judging the think-aloud
protocols of the participants according to the criteria of Veenman and Elshout
(Veenman, 1993; Veenman & Elshout, 1991, 1995; Veenman et al., 1997).
Metacognitive skilfulness was scored on four subscales: orientation activities,
systematic orderliness, evaluation, and elaboration activities. Orientation
activities concern the preparation for the task. These activities were judged on
indications of analysing the problem statement, identifying the independent
and dependent system variables, building a mental model of the task, and
generating hypotheses and predictions. Judgments of systematic orderliness
were based on the quality of planning activities, the systematic execution of
those plans, completing an orderly sequence of actions, and the avoidance of
unsystematic events (such as varying two variables at the same time).
Evaluation activities concern the control of the learning process. They were
judged on monitoring and checking, both on the local level (e.g. detecting
errors and checking calculations) as well as on the global level of keeping
track of progress being made (e.g. verifying whether the results obtained
provide an answer to the problem statement). Finally, judgments of
elaboration concern the intention of storing of findings and concepts in
Threshold of problematicity theory 19
memory. They were based on indications of recapitulating, drawing
conclusions, relating these conclusions to the subject matter, and generating
explanations. Elaboration itself may be conceived as a cognitive activity, but it
is assumed that the occurrence of such cognitive activity at an appropriate
point in time results from metacognitive activity. In order to avoid
contamination of protocol scores with learning outcomes, aspects of
metacognitive skilfulness were judged on the quality of performing
regulatory activities, not on the correctness of the information that resulted
from these activities. For instance, generating well-considered, though
incorrect predictions or incorrect conclusions may still result in high scores on
orientation or elaboration.
The four subscales of metacognitive skilfulness were rated on a fivepoint scale, ranging from 0 to 4. For each participant summed scores over the
four subscales were computed separately for the easy, the intermediate, and
the complex phase, thus obtaining three scores for metacognitive skilfulness.
The first author, who received no prior information about the participant’s
scores of intellectual ability, judged the think-aloud protocols. In an earlier
study (Veenman et al., 2002), the first two authors judged similar protocols
obtained in Optics Lab according to the same criteria. Average Cronbach’s
alphas over the four different subscales of .93 were established as inter-judge
reliabilities.
2.5 Learning outcomes.
Threshold of problematicity theory 20
Pre-tests and post-tests consisted of three types of questions: (1) qualitative
WHAT-IF questions, (2) qualitative reasoning questions, and (3) quantitative
questions. WHAT-IF questions contained three parts: conditions, action, and
predictions (Swaak & De Jong, 1996). In all questions conditions were defined
by a depicted situation with a light source, light rays and a lens. Actions and
predictions were presented in text. Actions referred to a change in the value of
an independent variable, while predictions referred to the changed value of a
dependent variable. The learner was asked to decide which of the three
predicted states would match the conditions and the action. An example of a
WHAT-IF question: The light source is moved a bit to the right. Where will the
emerging light ray intersect? (1) Closer to the lens, (2) At the same distance from the
lens, or (3) Further away from the lens. Qualitative reasoning questions were
reversed WHAT-IF questions: A prediction was presented and learners had to
choose which of the three actions would match the conditions and the
prediction. An example of a qualitative reasoning question: When will the
emerging light rays intersect above the optical axis? (1) If you rotate the light rays
upwards, (2) If you move the light source a bit downwards, (3) If you move the lens a
bit to the right. Quantitative questions also contained depicted situations, but
in this case the answer categories concerned numbers. In the easy phase the
quantitative questions of the post-test dealt with the focal distance of the
available lenses, which made them inappropriate for inclusion in a pre-test. In
the complex phase the quantitative questions concerned magnification and
Threshold of problematicity theory 21
the quantitative relation between object distance and image distance. The
intermediate phase contained no quantitative questions because with a light
source that has one light ray it is impossible to question magnification in a
quantitative way.
All independent variables in the Optics Lab were systematically
questioned with the WHAT-IF items, the qualitative reasoning questions and
the quantitative questions. The pre-test contained, in random order, 14
questions concerning the easy phase (10 WHAT-IF and 4 qualitative
reasoning), 15 questions concerning the intermediate phase (10 WHAT-IF and
5 qualitative reasoning), and 28 questions concerning the complex phase (18
WHAT-IF, 3 qualitative reasoning, and 7 quantitative questions). The post-test
questions were parallel versions of the pre-test questions, supplemented with
3 quantitative questions concerning the easy phase. In the results section, the
qualitative questions (WHAT-IF and the qualitative reasoning) and the
quantitative questions will be reported separately.
3.
Results
3.1
Analyses prerequisite
Cronbach’s alphas of the qualitative pre-test of the easy, the intermediate, the
complex phase and the total test were .65 (14 items), .04 (15 items), .30 (21
items) and .55 (50 items), respectively, whereas Cronbach’s alphas of the
qualitative post-test of the easy, the intermediate, the complex phase, and the
total test were .48 (14 items), .59 (15 items), .53 (21 items) and .62 (50 items),
Threshold of problematicity theory 22
respectively. Unfortunately, reliability coefficients of the quantitative tests
were insufficient. Cronbach’s alphas of the quantitative pre-test of the
complex phase, the post-test of the easy phase, and the complex phase were .
01 (7 items), .24 (3 items), and .21 (7 items), respectively. Thus, results
concerning the quantitative tests were excluded from further analyses.
Table 1 depicts the means and standard deviations of the Optics Lab pretest scores for relatively low and high intellectual ability novice and advanced
learners. Pre-test scores are presented for each phase separately and for the
total test. A MANOVA revealed main effects for level of expertise. Advanced
learners outperformed novices on the Optics Lab qualitative pre-test of the
easy phase, F(1,40) = 5.44, p < .05, the complex phase, F(1,40) = 11.75, p < .01,
and the total pre-test, F(1,40) = 15.63, p < .01. No main effect for intellectual
ability, F(3, 38) = 1.24, p = .31, and no interaction effect of level of expertise
with intellectual ability for pre-test scores, F(4, 37) = 0.59, p = .68, were found.
Table 2 depicts the means and standard deviations of the Optics Lab
post-test scores for each phase separately and for the total test for relatively
low and high intellectual ability novice and advanced learners. One-tailed ttests showed that novice learners gained qualitative knowledge from pre- to
post-test in the easy phase, t(22) = 6.05, p < .01, the intermediate phase, t(22) =
4.31, p < .01, and in the complex phase, t(21) = 2.73, p < .01. Consequently, their
total qualitative post-test score was significantly higher than their pre-test
score, t(21) = 6.31, p < .01. The advanced learners gained qualitative
Threshold of problematicity theory 23
knowledge in the easy phase, t(20) = 4.90, p < .01], the intermediate phase,
t(20) = 2.35, p < .05, and the complex phase, t(18) = 1.77, p < .05, as well. Their
total qualitative post-test score was significantly higher than their pre-test
score, t(18) = 5.02, p < .01.
As expected, the pre-test and post-test scores of the easy phase correlated
significantly for advanced learners (r = .54, p < .05, n = 21), but not for novice
learners (r = .11, p = .61, n = 23), indicating that in the easy phase prior
knowledge is an important determinant for learning for advanced learners.
The pre-test and post-test scores of the intermediate phase correlated
significantly for novice learners (r = .49, p < .05) but not for advanced learners
(r = .21, p = .37). Other correlations between pre-test and post-test scores were
not significant.
A MANOVA with repeated measures indicated that the knowledge gain
on the total qualitative test was significantly higher for novice learners than
for advanced learners, F(1, 39) = 4.43, p < .05. The two groups differed on the
pre-test, but not on the post-test. Furthermore, this MANOVA showed that
the knowledge gain for high intellectual ability learners in the intermediate
phase was larger than the knowledge gain for relatively low intellectual
ability learners (Mlowpre = 5.36, SDlowpre = 1.76; Mhighpre = 5.82, SDhighpre = 1.89; Mlowpost
= 6.36, SDlowpost = 2.08; Mhighpost = 8.55, SDhighpost = 2.82), F(1, 42) = 5.18, p < .05.
Threshold of problematicity theory 24
3.2
Metacognitive skilfulness and the relation with
intellectual ability
A measure for metacognitive skilfulness, consisting of a summed score of
orientation activities, systematic orderliness, evaluation, and elaboration
activities, was assessed for each phase separately. The correlations between
measures of metacognitive skilfulness of the easy phase and the intermediate
phase (phase 2 and 1), the intermediate phase and the complex phase (phase 1
and 3), and the easy phase and the complex phase (phase 2 and 3) were
respectively .40 (p < .01), .19 (p = .12), and .54 (p < .01). A principal component
analysis on the measures of metacognitive skilfulness for the three phases of
the Optics Lab extracted one component with an eigenvalue greater than 1.0.
This component had an eigenvalue of 1.76, with 58.8% of the variance
accounted for. The eigenvalue of the second component was 0.83,
representing an additional 27.5% of the variance. In Table 3 the unrotated
component matrix is depicted. All scores loaded high and positive on the first
component. Therefore, the results concerning the overall score for
metacognitive skilfulness, that is, the summed score over the measures for
metacognitive skilfulness of the three phases of the Optics Lab, will be
reported below. The results concerning the overall score for metacognitive
skilfulness were very similar to the results concerning the measures of
metacognitive skilfulness of the three phase of the Optics Lab.
Threshold of problematicity theory 25
Novice and advanced learners did not differ on overall metacognitive
skilfulness (Msumnov = 23.77, SDnov = 9.73; Msumadv = 19.89, SDadv = 10.03), t(39)
= 1.26, p = .22. Furthermore, no difference between novice and advanced
learners was found for intellectual ability scores (Mnov = 0.03, SDnov = 0.95; Madv
= – 0.06, SDadv = 1.00, respectively). The correlations between overall
metacognitive skilfulness and intellectual ability were .54 (p < .01) for novice
learners, and .60 (p < .01) for advanced learners. After correction for selection
of extreme groups on intellectual ability (Gulliksen, 1961), these correlations
became .39 and .44, respectively.
3.3
Determinants of learning
Table 4 shows the correlations between intellectual ability and learning
outcomes, and the correlations between overall metacognitive skilfulness and
learning outcomes. Correlations are presented for novice and advanced
learners separately for qualitative post-tests of each phase and for the total
tests. The uncorrected correlations as well as corrected correlations for
selection of extreme groups on intellectual ability (Gulliksen, 1961) are shown.
Inspection of the bivariate plots did not reveal a violation of the requirement
of homoskedasticity. It should be pointed out that correlations for indirect
selection (here the corrected correlation between metacognitive skilfulness
and learning outcomes, see last column of Table 4) lack a statistical method
for establishing a significance level. The significance of correlations for direct
Threshold of problematicity theory 26
selection (correlations with intellectual ability) is identical to the significance
of the uncorrected correlations (Elshout, Overbeek, Roe, & Vijn, 1979).
Table 5 depicts the distributions of the variance in learning outcomes of the
three phases of the Optics Lab. Semipartial correlations (Nunnally, 1967) were
calculated by partialing intellectual ability from the correlations between
metacognitive skilfulness and learning outcomes, and vice versa. They
represent the unique contribution of intellectual ability and the unique
contribution of metacognitive skilfulness to the variance of learning
outcomes. The correlations in table 4 and the distributions in Table 5 show
that metacognitive skilfulness is the main determinant of learning outcomes
in the easy phase for novices, as expected. Moreover, for advanced learners,
metacognitive skilfulness is the main determinant of learning outcomes in the
intermediate phase. In the other phases the unique contributions of both
intellectual ability and metacognitive skilfulness to the variance in learning
outcomes is modest for both novice and advanced learners.
4.
Discussion
The aims of the present study were to extend the threshold of problematicity
theory (Elshout, 1987) to the role of metacognition, and to provide empirical
evidence for this theory for learners in a realistic, ill-structured, self-directed
learning task. We examined whether the task complexity would cause
changes in the patterns of correlation between intellectual ability,
metacognitive skilfulness, and learning outcomes, as the threshold of
Threshold of problematicity theory 27
problematicity theory suggests. It was hypothesized that the pattern of
correlations between intellectual, ability, metacognitive skilfulness, and
learning outcomes would differ for novice and advanced learners. The pattern
for novice learners in the relatively easy phase of the Optics Lab was expected
to be similar to the pattern for advanced learners in the complex phase.
As was expected, the pattern of correlations for novice and advanced
learners differed substantially. For novice learners, metacognitive skilfulness
was the main determinant for learning outcomes in the relative easy phase of
the Optics Lab, which is shown by the size of unique contribution of
metacognitive skilfulness to the post-test scores of the easy phase and the
small unique contribution of intellectual ability. In the intermediate and
complex phase, the impact of metacognitive skilfulness and intellectual ability
on learning outcomes was limited. Considering the relatively low impact of
intellectual ability on learning outcomes for novice learners, even the easy
phase of the Optics Lab may have been be positioned beyond their threshold
of problematicity.
For advanced learners, in contrast, metacognitive skilfulness was the
main determinant in the intermediate phase, and not, as we expected, in the
complex phase. It is likely that, because of the complexity of the Optics Lab
(see also Veenman et al., 2002), the advanced learners already reached their
boundary of knowledge in the intermediate phase. Thus, the pattern of
Threshold of problematicity theory 28
correlations found for advanced learners in the intermediate phase resembled
the pattern of correlations found for novice learners in the easy phase.
These findings about the varying impact of metacognitive skills are in
line with ideas of Weinert (1987), who stated that only for tasks of medium
difficulty, where strategic solutions are possible, learning behaviour and
performance could be positively influenced by metacognitive skills. On
extremely difficult tasks, the use of metacognition may lead to the realisation
that further effort will not be productive. In spite of the rather high
complexity of the Optics Lab, novice learners were indeed able to acquire
knowledge in the Optics Lab, which suggests that the task itself was not
extremely difficult. In fact, the knowledge gain of novice learners was larger
than the knowledge gain of the advanced learners, without the risk of a
ceiling effect for advanced learners on the pre-test. The knowledge gains of
novice learners were even large enough to let initial differences on the pretests disappear on the post-tests.
Furthermore, as expected, for advanced learners prior knowledge was
the main determinant for learning outcomes in the easy phase, shown by a
high positive correlation between pre-test and post-test scores in that phase
and the modest correlations between intellectual ability and metacognitive
skilfulness at the one hand, and post-test scores on the other. Apparently,
advanced learners could retrieve the knowledge that is necessary to complete
the post-test questions directly from memory. In the complex phase, a
Threshold of problematicity theory 29
marginal significant correlation between intellectual ability and learning
outcomes was found for advanced learners. These results are in accordance
with the findings of Veenman and Elshout (1999).
In sum, the varying impact of intellectual ability on learning outcomes
found in the present study is in accordance with the threshold theory, which
states that the threshold of problematicity of advanced learners is positioned
at a higher level of the objective task complexity than the threshold of
problematicity of novice learners. Also the role of metacognition is in
accordance with our theoretical view. In general, the patterns of correlations
found in the present study resemble the mixed model (Veenman, 1993):
metacognitive skilfulness and intellectual ability were related to some extent,
while they both had an independent impact on qualitative learning outcomes
at an adequate level of task complexity.
Reliability scores of the qualitative test were sufficient, except for some
reliability scores of the qualitative pre-tests. This could cause problems
interpreting the correlations concerning these scores. Probably, low reliability
was partly due to the complexity of the test items. When learners have limited
knowledge, as is the case with novice learners, or when misconceptions are
developed, inter-item correlations and, therefore, reliability will decrease. It
is well known that the internal consistency of novices in a pre-test is often
low. Also the rather limited size of the tests and the fact that questions were
multiple-choice items decreased test reliability. However, it should be noted
Threshold of problematicity theory 30
that Cronbach’s alpha is a lower bound measure for reliability and, thus,
likely underestimates reliability. For this reason, correlations concerning
scales with moderate reliability could be higher than one could expect on
account of Cronbach’s alpha. Moreover, low internal consistency is a big
problem when results are not significant, because low internal consistency
would be the alternative explanation for a lack of significant results. When
significant results are found, moderately low internal consistency is not a
pressing issue. Reliability scores of the quantitative tests, however, were too
low, probably for the above-mentioned reasons, and results concerning the
quantitative tests had to be excluded from further analyses.
When educators design learning environments and learning tasks, the
threshold of problematicity of the students should be taken into account.
Operating slightly above one’s threshold is the better way to learn (Elshout,
1987; Snow, 1989) because learners are then challenged to extend their
heuristic and improvisational abilities. When learners operate too far beyond
their threshold, the heuristic behaviour will be ineffective. Problem solving as
well as learning will then become almost an impossible venture (see Weinert,
1987). On the other hand, learners working far below their thresholds may
only strengthen habitual automatic performance. Indeed, successful assembly
processes will be strengthened and automatised by continued practice. They
become, in Veenman’s (1993) terms, domain-specific task schemata, which are
stored in memory, independently of the general task schema from which they
Threshold of problematicity theory 31
were derived. Instructional support, however, may be necessary to adapt the
task complexity to the threshold of the learner. Eventually, continued practice
will gradually raise a learner’s threshold of problematicity for a particular
task: More and more difficult instances of a task type become nonproblematical and automatic (Elshout, 1987).
It will be a challenge for further research to try to move the threshold of
problematicity of novice learners upwards by providing specific instructional
support aimed at supporting their metacognitive skilfulness. Good examples
of this kind of support are structuring the environment by providing subassignments and so-called telling experiments (Veenman & Elshout, 1995), in
which learners are explicitly told which experiments they have to conduct,
leading to better learning performances. This kind of adaptive support may
allow both high and low intelligent novice learners to turn more rapidly from
generating hypotheses to data interpretation and testing hypotheses.
Threshold of problematicity theory 32
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Threshold of problematicity theory 41
Acknowledgements
This research was financially supported by the Foundation for
Behavioural Sciences (SGW), part of the Netherlands Organisation for
Scientific Research, with grant 575-22-003. This research project was hosted by
the Department of Developmental and Educational Psychology at Leiden
University from 1996 to 2000, to which department the first author was
affiliated to during this period.
The authors thank the colleagues of the Research Department of the
Educational Technology Expertise Center, especially Jan-Willem Strijbos, for
their valuable comments on an earlier draft.
Threshold of problematicity theory 42
Table 1
Means and standard deviations of the qualitative pre-test scores in the different phases of the Optics Lab
for novice and advanced learners with relatively low and high intellectual ability (IA)
PRE-TEST
Low IA
M
High IA
SD
n
M
SD
Total
n
M
SD
n
Novice
Easy phase
6.50
2.01
10
7.23
2.62
13
6.91
2.35
23
Intermediate phase
5.00
1.76
10
5.46
2.22
13
5.26
2.00
23
Complex phase
6.80
1.55
10
8.85
2.34
13
7.96
2.25
23
18.30
3.40
10
21.54
3.84
13
20.13
3.93
23
Total
Advanced
Easy phase
8.92
3.03
12
8.67
3.04
9
8.81
2.96
21
Intermediate phase
5.67
1.78
12
6.33
1.22
9
5.95
1.56
21
Complex phase
9.92
2.15
12
10.33
2.65
9
10.10
2.32
21
24.50
4.98
12
25.33
4.09
9
24.86
4.53
21
Total
Threshold of problematicity theory 43
Table 2
Means and standard deviations of the qualitative post-test scores in the different phases of the Optics
Lab for novice and advanced learners with relatively low and high intellectual ability (IA)
POST-TEST
Low IA
M
High IA
SD
n
M
Total
SD
n
M
SD
n
Novice
Easy phase
9.80
1.62
10
11.38
2.29
13
10.70
2.14
23
Intermediate phase
6.40
2.07
10
8.46
3.15
13
7.57
2.87
23
Complex phase
10.44
3.32
9
10.92
4.03
13
10.73
3.68
22
Total
26.89
3.98
9
30.77
6.66
13
29.18
5.93
22
Advanced
Easy phase
11.63
1.30
12
11.22
1.48
9
11.48
1.36
21
6.33
2.19
12
8.67
2.45
9
7.33
2.54
21
Complex phase
10.20
1.48
10
11.89
2.37
9
11.00
2.08
19
Total
28.50
3.44
10
31.78
3.23
9
30.05
3.66
19
Intermediate phase
Threshold of problematicity theory 44
Table 3
Unrotated component matrix for measures of metacognitive skilfulness for three different phases of the
Optics Lab
Easy phase
Intermediate phase
Complex phase
Component 1
.87
.64
.77
Component 2
–.09
.74
–.51
Threshold of problematicity theory 45
Table 4
Correlations between intellectual ability (IA), overall metacognitive skilfulness (MS) and post-test scores
in the different phases of the Optics Lab for novice and advanced learners
Uncorrected
IA
Corrected
MS
IA
MS
Novice (n = 22)
Easy phase
.36
.49*
.24
.43
Intermediate phase
.36
.37
.25
.29
Complex phase
.08
.15
.05
.13
Total
.35
.45*
.24
.38
–.11
.19
Advanced (n = 19)
Easy phase
–.17
.11
Intermediate phase
.49*
.72**
.34*
.67
Complex phase
.45*
.26
.31*
.13
Total
.52*
.68**
.37*
.61
Note. Corrected = corrected for selection of extreme groups of intellectual ability.
* p < .05. ** p < .01.
Threshold of problematicity theory 46
Table 5
Proportion of the variance of post-test scores in the different phases of the Optics Lab accounted for by
intellectual ability (IA) and metacognitive skilfulness (MS) for novice and advanced learners
IA unique
MS unique
IA and MS
Total
Novice (n = 22)
Easy phase
.01
.13
.05
.19
Intermediate phase
.02
.04
.04
.11
Complex phase
.00
.01
.00
.02
Total
.01
.10
.05
.15
Advanced (n = 19)
Easy phase
.05
.07
.03
.15
Intermediate phase
.00
.34
.11
.45
Complex phase
.08
.00
.02
.10
Total
.01
.25
.12
.38
Threshold of problematicity theory 47
Impact of
intellectual
ability on
learning
Advanced
learners
Novice
learners
Increasing task complexity
Figure 1. The relation between task complexity and the impact of intellectual
ability on learning outcomes.
Threshold of problematicity theory 48
Figure 2. An example of an experiment in the Optics Lab.