Khine M.S., Saleh I.M. Models and Modeling: Cognitive Tools for Scientific Enquiry
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3 The Nature of Scientific Meta-Knowledge 71
patterns may be found in the data, which suggest revisions to models or lead to new
theoretical models, further investigations, and more data analyses.
Teaching Scientific Meta-knowledge
To develop and demonstrate an understanding of the four kinds of scientific meta-
knowledge that we argue are needed, students would need to engage in an extensive
inquiry curriculum, one aimed at developing and testing models of different types,
which would fit together to create a scientific theory for a given domain. For each
study they undertake, students would need to consider which type of model they
want to develop, which type of question they should ask, which type of investiga-
tion they need to carry out, and which data analysis techniques they should employ.
Being able to make such decisions presupposes that they know about different
model types, question types, investigation methods, and data analysis techniques
and, further, that they know how these work together to create and test scientific
theories.
While our Inquiry Island and Web of Inquiry software embody much of the
meta-knowledge we have described in this chapter, our own research program has
provided limited opportunities for determining how best to teach such meta-level
expertise to young students (i.e., upper elementary and middle school students).
In one attempt to develop students’ meta-modeling knowledge, for example,
we designed a version of our ThinkerTools Inquiry Curriculum that focuses on
enabling students to learn about different types of scientific models and their utility
(Schwarz & White, 2005). The results were encouraging, though the impact of the
curriculum was strongest on the higher achieving students.
In most of our recent work (Frederiksen et al., 2008; White & Frederiksen,
2005), the teachers we collaborate with only commit to having their students engage
in one or two major research projects per year. Given this limited time commit-
ment, we have focused on enabling students to develop a particular type of theory,
which usually consists of a causal model (often multifactor) linked with some pre-
liminary models of underlying mechanisms. To evaluate their theories, students
design and carry out a particular type of investigation, usually a controlled com-
parison designed to test their competing hypotheses. They then undertake a limited
range of data analyses, typically centering around a comparison of averages and
frequency distributions of data from the various experimental conditions. Thus,
although the students learn a great deal about scientific inquiry and produce some
impressive research projects (Frederiksen et al., 2008), the restricted instructional
time and number of investigations students undertake limit the development of their
meta-level expertise.
Creating curricula that adequately teach scientific meta-knowledge would require
a much more extensive curricular sequence that would give students opportunities to
learn more about different types of models, investigations, and analyses, as well as
how all of these components of inquiry work together, throughout a series of inves-
tigations, to create comprehensive scientific theories. Engaging in such a curriculum
72 B.Y. White et al.
would t hus necessitate a serious time commitment on the part of schools and teach-
ers. Teachers, along with those who develop curricular standards and accountability
measures for science education, need to be convinced of the importance of scientific
meta-knowledge.
Concluding Thoughts About the Utility of the Framework
A major benefit of the meta-knowledge framework described in this chapter is to
increase the awareness of students, teachers, and scientists as to different types of
models, questions, investigations, and analyses. If they have a toolkit that includes
a wide variety of model types, they may construct richer theories by building mul-
tiple models of different types and linking them together. Similarly if researchers
learn how to use different investigation methods, they can combine these methods
to triangulate on the phenomena they are trying to understand and hence produce
more robust results. The framework provides guidance to make informed decisions
as to what to do at different points in the inquiry process. Our basic argument is
that scientific meta-awareness provides insights for scientists and students that will
enhance their ability to learn and understand scientific inquiry.
One long-term goal of our work has been to develop computer-based tools that
support scientists and students in their work. For example, we think a modeling tool
could help scientists construct many different kinds of models, just as Stella (High
Performance Systems, 2000) helps students construct system-dynamics models and
Net Logo (Wilensky, 1999) and Agent Sheets (Repenning, Ioannidou, & Zola, 2000)
help students construct agent models. Such a tool could help scientists and students
consider different types of models they might construct and provide a structure,
which guides the development of each type of model.
Inquiry Island and the Web of Inquiry provide another kind of computer-based
tool that can support scientists and students in their r esearch. Inquiry Island has
extensive advice about the issues people s hould address as they construct models
and theories, formulate research questions and hypotheses, carry out investigations,
and analyze and synthesize the data they collect. This advice is available when
people ask for it or when they appear to need it. It can be modified, in the Web
of Inquiry, for particular types of inquiries or for particular groups of people. The
goal is to refine Inquiry Island and the Web of Inquiry as general-purpose tools that
can support scientific inquiry at many different levels.
Clearly more work remains to be done. This chapter only presents a top-level
view of the kind of meta-knowledge we think is central to scientific inquiry.
For example, there are many different kinds of model types or epistemic forms
(Collins & Ferguson, 1993) that scientists and students might learn to use. In fact,
artificial intelligence has led to a proliferation of different model types in its short
history, such as production-system models, agent models, constraint-satisfaction
models, semantic-network models, frame-system models, qualitative-process mod-
els. This proliferation of model types provides new power for making sense of
diverse phenomena in the world. Understanding the affordances and constraints in
3 The Nature of Scientific Meta-Knowledge 73
building models of these different types should become a learning goal for future
scientists.
Such work on meta-scientific knowledge should thus have benefits that go
beyond science education. We think that our framework provides a structure that
will enable science to refine its practices and products. Making the meta-knowledge
underlying scientific processes explicit fosters systematic reflection by scientists and
the field as a whole. This reflection enhances the possibility that a self-improving
system will take hold to develop better tools, models, and representational forms.
Hence we argue that the development of an explicit understanding of the nature of
scientific meta-knowledge will lead to a more productive s cientific enterprise.
Acknowledgments This research was funded in part by the National Science Foundation (grants
MDR-9154433, REC-0087583, and REC-0337753). We thank the members of our research team
for their contributions to this work, as well as the students and teachers who participated in our
studies. The views expressed in this chapter are those of the authors and do not necessarily reflect
those of the National Science Foundation.
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Chapter 4
From Modeling Schemata to the Profiling
Schema: Modeling Across the Curricula
for Profile Shaping Education
Ibrahim A. Halloun
Research has been undertaken for the last 4 years, in part under the auspices of
Educational Research Center, to extrapolate the work on modeling theory in sci-
ence education (Halloun, 1984, 1994, 1996, 1998a, 1998b, 2000, 2001, 2004a,
2004b, 2004/2006, 2007a, 2007b, 2008) into the broader field of education. Profile
Shaping Education (PSE) has emerged in the process as a generic educational frame-
work that may be deployed in designing and deploying various curricula. PSE
calls for any systemic effort in education to empower the target population (e.g.,
students, preservice and in-service teachers, administrators) with a particular profile
following well-established pedagogic principles and rules (or general and opera-
tional standards), and in accordance with well-defined cognitive, metaphysical, and
educational tenets. The target profile can be readily translated, using a particular pro-
filing schema, into measurable outcomes which would be reified in various course
materials, and ascertained in various forms of assessment. This chapter discusses, in
the context of PSE, the development of the profiling schema in question, beginning
with its origins in modeling theory, and its implementation in certain educational
programs, especially the International Arab Baccalaureate (IAB).
Modeling Theory in Science Education
The work of this author on modeling began in physics as part of his Ph.D. dis-
sertation at Arizona State University (Halloun, 1984). Through classroom-based
research, scientific models and modeling were originally transposed from profes-
sional tools and methodology of scientific research into pedagogical tools and
methodology for learning physics meaningfully at the college level (Halloun &
Hestenes, 1987; Halloun, 1984). Subsequently, and as an integral part of this
author’s drive for a “modeling theory in science education”, models and modeling
gradually evolved into generic pedagogical tools and methodology for meaningful
I.A. Halloun (B)
Educational Research Center, Jounieh, Lebanon
e-mail: halloun@halloun.net
77
M.S. Khine, I.M. Saleh (eds.), Models and Modeling, Models and Modeling
in Science Education 6, DOI 10.1007/978-94-007-0449-7_4,
C
Springer Science+Business Media B.V. 2011
78 I.A. Halloun
development of any scientific paradigm at any school or university level (Halloun,
2001, 2004/2006, 2007a). Unless otherwise specified, any mention in the following
of “models”, “modeling”, and “modeling theory” refers to this author’s perspec-
tive on these matters as summarized below and as reflected in his work for the
pedagogical theory in question.
Modeling theory calls for any science course to help individual students bring
their personal paradigms, often governed by naive realism as educational research
showed in the last three decades, into a s tate of relative commensurability with sci-
entific paradigms. A paradigm consists, for us, of major tenets (i.e., metaphysical or
foundational statements, often of axiomatic nature), principles and rules that gov-
ern development and deployment of certain habits of mind (cognitive processes and
dispositions) and episteme (a body of conceptions or conceptual knowledge). A sci-
entific paradigm is a paradigm accepted and shared by a community of scientists.
The corresponding episteme consists of a corroborated scientific theory or set of
interrelated theories, with each theory primarily consisting of a set of s cientific
models and appropriate principles and rules for model construction and deploy-
ment. A scientific model is a representation of a specific pattern in the real world,
and modeling involves habits of mind for model construction, corroboration, and
deployment. The pattern may be about the structure and/or the behavior of a number
of physical systems in the universe. Scientific habits of mind include generic cogni-
tive processes (skills) and dispositions (values, attitudes, and other meta-cognitive
controls) which scientists systematically invest in the construction, corroboration,
and deployment of scientific theory, models included.
Modeling theory calls on teachers to empower students to resolve, explicitly
and to the extent that is possible in a given science course, epistemic and cog-
nitive incommensurability between naive paradigms and corresponding scientific
paradigm(s), while mediating construction and deployment of scientific models.
When students are guided in this direction in the framework of modeling theory,
comparative research shows that they become more effective problem solvers
(Taconis, Ferguson-Hessler, & Broekkamp, 2001) and that their overall course
achievement becomes significantly better under the prescribed modeling approach
than under any other form of instruction, especially traditional instruction of lecture
and demonstration (Hake, 1998; Halloun, 1984, 1994, 1996, 1998a, 1998b, 2000,
2004a, 2007a, 2007b; Halloun & Hestenes, 1987).
The advocated approach owes its relative success to the fact that it focuses on
both course content and instructional methodology, without compromising one for
the other, and in ways that bring the foundations of scientific paradigms into conso-
nance, even resonance, with the foundations of human cognition. This is achieved,
in part, in the manner outlined in the following six sections.
Paradigmatic Perspective
Under modeling instruction, students are guided to develop any conception (con-
cept, law, or any other theoretical entity or statement) or habit of mind (process, skill,
4 From Modeling Schemata to the Profiling Schema 79
or disposition about knowing or learning science) from a paradigmatic perspective,
and not in an episodic, piecemeal approach. In such perspective, any learning activ-
ity is conducted in the context of the big picture defined by the scientific theory that
conception and habit are about, and in accordance with epistemological, method-
ological, and axiological tenets associated with the paradigm which the theory
belongs to.
Epistemological tenets, to which we subscribe, postulate that there are patterns in
scientific knowledge, just like there are patterns in the physical world. In this world,
patterns manifest themselves in the structure and behavior of physical systems, but
especially in the conservation or change of state of such systems while interact-
ing with each other. Patterns in the scientific realm are best manifested through
scientific models, with each model representing a specific pattern in the structure
and/or behavior of physical systems, and making part of a particular scientific the-
ory that sets the scope and structure of the model in accordance with well-defined
paradigmatic principles and rules.
Methodological and axiological tenets we subscribe to are primarily those that
govern model construction and deployment (corroboration included), and that allow
students, in the process, to efficiently develop scientific habits of mind, and ingrain
them constructively and productively in their individual mental profiles.
Under modeling instruction, students are constantly engaged in the development
of scientific models of increasing level of complexity in accordance with the tenets
above. Furthermore, a balance is maintained in the process between the epistemic
dimension (conceptions) and the cognitive dimension (habits of mind), with special
attention to the latter since habits allow meaningful development of conceptions
more than the other way around. Special attention is also devoted to reflect the
generic aspect of all habits of mind, as well as of some basic conceptions in scien-
tific paradigms, in ways to allow students realize and appreciate cross-disciplinarity
among various scientific fields, as well as between those fields and nonscientific
fields. As a consequence, students begin to think coherently and efficiently, and learn
course materials meaningfully and productively, especially when they are empow-
ered to take advantage of knowledge (conceptions and habits of mind) developed in
one course in other courses and in everyday life.
Critical Thresholds
A number of thresholds are defined within every scientific theory that set (a) a
paradigmatic hierarchy in the structure of the theory, and especially (b) an effi-
cient cognitive and pedagogical sequence in the scope of any scientific course which
the theory is about. The most critical of these thresholds are the “basic threshold”
and the “mastery threshold” (Fig. 4.1). The basic threshold is set between the core
body of knowledge and the fundamental body of knowledge. Core knowledge cor-
responds to a limited number of the most basic models in the context of which,
from a pedagogical perspective, can be developed the most fundamental and critical
conceptions and habits of mind of the corresponding scientific theory. Fundamental
80 I.A. Halloun
Fig. 4.1 Critical thresholds in the structure of scientific theory, as set from paradigmatic and
pedagogic perspectives
knowledge embodies somewhat more complex basic models in the context of which
students reinforce, and widen the scope of, core conceptions and habits, and derive
from them new conceptions and habits. Emergent knowledge typically involves
models that may emerge from the composition of two or more basic models, as
well as original, more complex models.
A student needs to meaningfully develop the entire core knowledge before s/he
can proceed to fundamental knowledge. Any flaw in developing any conception
or habit of mind in t he core knowledge prevents the student from crossing the basic
threshold, and thus from developing fundamental knowledge meaningfully. Students
normally require significant teacher mediation, in the form described below under
modeling instruction, in order to reach such threshold. Once they cross it, teacher
mediation can be gradually reduced throughout the fundamental body of knowledge
until students cross the mastery threshold. Beyond that threshold, students should be
capable of developing the more complex emergent knowledge with the least teacher
mediation ever.
For example, in Newtonian theory of mechanics, core knowledge corresponds
to the most elementary two basic particle models in the context of which students
need to develop all basic concepts and laws of Newtonian kinematics and dynamics,
as well as basic habits of mind necessary for model construction and deployment.
These models are the free particle model representing physical objects in translation
with constant velocity under no net force, and the uniformly accelerated particle
model representing physical objects in translation with constant acceleration under
a net constant force. Fundamental knowledge, in a typical senior secondary school
course or freshman college (university) course, involves either or both types of
basic particle models: bound particle models (circular or simple harmonic motion),
and particles interacting with impulsive forces (collision). Emergent knowledge
may encompass, in such courses, other types of bound particle models, as well as