A Proposal For A Problem-Driven Mathematics Curriculum Framework

The Mathematics Enthusiast
The Mathematics Enthusiast
Volume 10
Number 1 Numbers 1 & 2 Article 20
1-2013
A Proposal for a Problem-Driven Mathematics Curriculum
A Proposal for a Problem-Driven Mathematics Curriculum
Framework
Framework
Judith S. Zawojewski
Marta T. Magiera
Richard Lesh
Follow this and additional works at: https://scholarworks.umt.edu/tme
Part of the Mathematics Commons
Let us know how access to this document benefits you.
Recommended Citation
Recommended Citation
Zawojewski, Judith S.; Magiera, Marta T.; and Lesh, Richard (2013) "A Proposal for a Problem-Driven
Mathematics Curriculum Framework," The Mathematics Enthusiast: Vol. 10 : No. 1 , Article 20.
Available at: https://scholarworks.umt.edu/tme/vol10/iss1/20
This Article is brought to you for free and open access by ScholarWorks at University of Montana. It has been
accepted for inclusion in The Mathematics Enthusiast by an authorized editor of ScholarWorks at University of
Montana. For more information, please contact scholarworks@mso.umt.edu.

TME, vol10, nos.1&2, p.469
The Mathematics Enthusiast, ISSN 1551-3440, Vol. 10, nos.1&2, pp.469-506
2013©The Author(s) &Dept of Mathematical Sciences-The University of Montana
A Proposal for a Problem-Driven Mathematics Curriculum Framework
Judith S. Zawojewski
Illinois Institute of Technology, Chicago, IL
Marta T. Magiera
Marquette University, Milwaukee, WI
Richard Lesh
Indiana University, Bloomington, IN
Abstract: A framework for a problem-driven mathematics curriculum is proposed,
grounded in the assumption that students learn mathematics while engaged in complex
problem-solving activity. The framework is envisioned as a dynamic technologically-
driven multi-dimensional representation that can highlight the nature of the curriculum
(e.g., revealing the relationship among modeling, conceptual, and procedural knowledge),
can be used for programmatic, classroom and individual assessment, and can be easily
revised to reflect ongoing changes in disciplinary knowledge development and important
applications of mathematics. The discussion prompts ideas and questions for future
development of the envisioned software needed to enact such a framework.
Keywords: Problem-based Mathematics, Curriculum frameworks, Mathematical
Modeling, Model-Eliciting Activities.
Introduction
Curriculum frameworks are commonly organized around categories of
mathematical topics (e.g., number, geometry), such as in the new Common Core School
Mathematics Standards (NGA & CCSSO, 2011) and the National Council of Teachers of
Mathematics (NCTM) standards documents (1989, 2000) for the United States (U.S.).
Oftentimes, to convey the nature of mathematics teaching and learning, the content topics
are cross-referenced with other types of mathematical behaviors, such as the “process
standards” (e.g., problem solving, reasoning and proof) of the NCTM documents, and the
“practices” (e.g., model with mathematics, attend to precision) of the CCSSM document.

Zawojewski, Magiera & Lesh
Another approach is to formulate mathematics curriculum frameworks based on
assumptions about learning mathematics, such as the Dutch curriculum framework
described by van den Heuvel-Panhuizen (2003) (e.g., informal to formal, situated to
generalized, individual to social). The developers of mathematics curriculum frameworks
choose their organization and structure in order to communicate a mathematics
curriculum to broad audiences (e.g., teachers, administrators, parents, students). The
choices for content and the representation of curricula made by the framework
developers, in turn, convey a distinctive perspective on mathematics curriculum,
accompanied by inevitable (some intended, some unintended) consequences when users
of the framework transform the represented curriculum into prescriptions for classroom
experiences and assessment. A proposal for framing and representing a problem-driven
mathematics curriculum is described in this article. The proposal envisions a framework
that grows out of Lesh and colleague’s work on models-and-modeling, which has focused
on using modeling problems as sites for revealing and assessing students’ thinking (e.g.,
Lesh, Cramer, Doerr, Post & Zawojewski, 2003), and more recently by Richard Lesh to
teach data modeling (personal conversation, Dec. 21, 2012). The proposal also envisions
a representational system that builds on a one originally posed by Lesh, Lamon, Gong
and Post (1992), and is particularly poignant today because technology is now available
that could carry out the proposal.
Why an Alternative Framework?
Assumptions about Curriculum Frameworks

TME, vol10, nos.1&2, p .471
Curriculum frameworks convey a view of mathematics learning to stake holders
in education, influencing the full range of mathematics education activity—from
implementation to assessment. For example, the two foundational NCTM curriculum
documents (1989, 2000) contributed to a huge shift in views of mathematics curriculum
in the U. S. Prior to the publication of these documents, schools, districts and state
curriculum guides predominantly listed expected mathematical competencies by grade
level, commonly referred to as scope and sequence documents. The NCTM standards
documents introduced a process dimension (problem solving, reasoning, connections,
communication) in addition to the common practice of describing mathematics
competencies and performance expectations. Further, discussions about the mathematical
processes and expected mathematical performances were embedded in the context of
illustrative problems, teaching and learning scenarios, and ways of thinking about
mathematics. These standards documents impacted not only state curriculum standards,
but also resulted in the development of the now-famous NSF curricula (described in
Hirsch, 2007a). Research on the standard-based curricula suggests that students using
these curricula demonstrate enhanced learning of mathematical reasoning and problem
solving (Hirsch, 2007b).
The new Common Core State Standards in Mathematics (NGA & CCSSO, 2011),
adopted by 45 of the United States and 3 territories, lists mathematical learning
objectives, or standards, organized by grade level, and is accompanied by a completely
separate discussion of eight mathematical practices. There is no discussion in the
document to help the practitioner envision what the implementation of the intended
curriculum will look like—leaving the accomplished curriculum more dependent on

professional development and local school culture to fill in the picture. One advantage to
the separation of mathematics competencies from the mathematical practices may be to
avoid representing the mathematics curriculum as an array, which can inadvertently
convey a view of mathematics curriculum as disaggregated into bits and pieces
represented by each cell.
Consider, for example, the Surveys of Enacted Curriculum (SEC) (Porter, 2002),
which are intended to drive assessment of student performance. The SEC is organized in
a two-dimensional framework of cognitive demand (memorize, perform procedures,
demonstrate understanding, conjecture/generalize/prove, and solve non-routine problems)
vs. disciplinary topics (e.g., functions, data analysis, rational expressions). It divides the
(K-12) mathematics topics dimension into 19 general categories, each of which is then
divided into 4 to 19 smaller mathematical topics. “Thus, for mathematics, there are 1,085
distinct types of content contained in categories represented by the cells” (Porter,
McMaken, Hwant, & Yang, 2011, p. 104). Porter’s fine-grained representation of
curriculum is intended to ensure coverage of mathematical topics and types of cognitive
demand while minimizing gaps and overlaps. However, such a representation may lead to
an enacted curriculum prescribed by the “pieces” (i.e., the cells), and if educators are
prompted to “teach to the test” an unintended emphasis on disconnected mathematics
education may result. Further, once a framework like this is codified by formal external
assessments, the mathematics content becomes more difficult to revise in response to the
needs of evolving fields of science, engineering and technology.
An alternative may be found in the Dutch mathematics curriculum, rooted in
Realistic Mathematics Education (RME) learning theory, initially developed by the well-

TME, vol10, nos.1&2, p .473
respected Dutch mathematics educator, Freudenthal (1991), and continued at the
Freudenthal institute today. The work in RME portrays a vision of mathematics as a
human activity that combines learning and problem solving as a simultaneous activity.
Smith & Smith (2007) describe the three dimensions around which the RME-based
mathematics curriculum framework is organized: informal to formal; situated to
generalized; and individual to social. In practice, RME emphasizes curriculum designed
to encourage students’ development via progressive mathematization. van den Heuvel-
Panhuizen (2003) describes progressive mathematization as the growth of an individual’s
mathematical knowledge from informal and connected to the local context, to an
increasing understanding of solutions designed to reach some level of schematization
(making shortcuts, discovering connections between concepts and strategies, making use
of these new findings in a new way), and finally to an increasing understanding of formal
mathematical systems.
The work on such progressive mathematization is growing (e.g., hypothetical
learning trajectories as described by Clements & Sarama, 2004a; 2004b). But, questions
have been raised by Lesh and Doerr (in press): Do all students optimally learn along a
particular normalized path (learning line, learning trajectory)? Do all students learn the
“end product” in the same way? Likely not. Rather than describing a particular learning
objective or standard as a goal for learning, they use Vygotsky’s (1978) “zone of
proximal development” to describe particular goals for students’ learning as regions
around those goals that are individualistic and dependent on a variety of interacting
factors. Such might include the scaffolding provided by the teacher, the language that the
student has and the teacher uses, and the technology or manipulatives that may or may

not be available during the learning episode. Further, Lesh and Doerr, using Piaget’s
(1928, 1950) notion of decalage, describe how apparent learning of an objective may
mask the partial development of an idea when “operational thinking” for one concept
may occur years earlier or later than comparable levels of “operational thinking” for
another closely related concept (Lesh & Sriraman, 2005). Lesh and Doerr emphasize that
individuals learn in different ways and develop their understandings along different paths.
They argue that intended “final products” (i.e., identified as standards or learning
objectives) are likely to be in intermediate stages of development in most students, and
open to revision and modification as they encounter new situations for which they need to
form a mathematical interpretation.
Assumptions about Mathematics Learning
Lesh and Zawojewski (2007) refer to the work of various theorists and researchers
(e.g., Lester & Charles, 2003; Lester & Kehle, 2003; Schoen & Charles, 2003; Silver,
1985; Stein, Boaler, & Silver, 2003) to establish a close relationship between the
development of mathematical understandings and mathematical problem-solving. Their
perspective on learning “treats problem solving as important to developing an
understanding of any given mathematical concept or process . . . . [and]. . . the study of
problem solving needs to happen in the context of learning mathematics . . .” (p. 765). In
particular, Lesh and Harel (2003), and Lesh and Zawojewski’s (1992) description of
“local concept development” highlight the simultaneous increase in an understanding of a
specific problem situation and the development of one’s mathematization of the problem.
“[S]tudents begin these type of learning/problem-solving experiences by developing
[local] conceptual systems (i.e., models) for making sense of real-life situations where it

TME, vol10, nos.1&2, p .475
is necessary to create, revise or adapt a mathematical way of thinking” (Lesh &
Zawojewski, 2007, p 783).
What is meant by local concept development and learning? Consider the Grant
Elementary School Reading Certificate activity described in Figure 1, in which students
are asked to create a set of “rules for awarding certificates” (i.e., a decision model). As
described in Figure 1, the students generate a variety of models as an answer to this
problem, and their answers provide windows to their mathematical thinking and
learning—their local concept development.
Grant Elementary School Reading Certificates Problem1
In this activity, third grade students are asked to create and apply a set of decision rules for
awarding certificates to readers who read a lot and who read challenging books. The students
are given sample sets of individual reader’s accomplishments, each presented in a table
including the title of each book read, the number of pages for each book, and the difficulty
level of the book (labeled as easy, medium, hard). The tension between the two criteria for
earning a certificate (reading a lot of books and reading challenging material) was intentional,
in order to enhance the potential for various reasonable models to be developed.
Summary of Group #1 Response:
 Students should read either 10 books, or more than 1000 pages.
 At least 2 of the books read should be hard books.
This group clearly communicates the decision rules (i.e., model) and takes into account both
required conditions: reading many books, and reading challenging books. Readers can readily
apply the rules to the given data sets. For example, in one data set, the reader had read 5 books
(two of which were hard), and a total of 722 pages. Given the clarity of the decision rules, a
reader can figure what he or she needs to do to earn a certificate. In this illustrative case, one
way for the reader to earn a certificate is to read 5 more books (even if they are all easy).
Another way is to pick one long book that has at least 279 pages.
Summary of Group #2 Response:
 A student gets 1 point per page for easy-to-read books.
 A student gets 2 points per page for hard-to-read books.
 A student has to earn 1000 points to get a reading certificate.
This set of decision rules is clearly communicated, and a reader could easily apply the decision
rules and self-assess. However, a reader could earn a reading certificate award by reading only
easy books, not meeting the criteria that readers must read both hard and easy books.
Therefore, the set of rules does not meet the requirements for a “good” set of rules.
Figure 1. Two Illustrations of Local Concept Development
1 This activity, in full, can be found in Yeatts, C. L., Battista, M. T., Mayberry, S.,
Thompson, D., & Zawojewski, J. S. (2004). Navigating through problem solving and
reasoning in grade 3. Reston, VA: National Council of Teachers of Mathematics.

The mathematical goals of the activity are three-fold. First, each group of problem
solvers is expected to generate a mathematical model, meaning they must develop a
procedure or algorithm that meets the criteria given—that those earning a certificate must
read a lot of books and read challenging books. In the generation of a model, many
students engage in other types of mathematical knowledge development, such as
quantifying qualitative information and differentially weighing and/or rank ordering
factors. Each of the two responses described in Figure 1 represents different locally
developed concepts, which are represented in the groups’ model (i.e., a set of rules). Note
that the first response meets the criteria, whereas the second does not. Note, also, how in
each case, the model developed is situated in the context of the problem, and is also
dependent on the knowledge that individuals bring to the group—about mathematics,
about reading programs, about meaning of “challenging books” and meaning of “reading
a lot.” A second goal is for students to practice basic skills, such as recognizing the need
for and carrying out calculations, and comparing and ordering numbers. These take place
as the students test their proposed models, and in the full activity, students are given
further sets of data to conduct additional tests of the model they have generated. A third
area of learning is generalization, which is driven by the design of the problem. In
particular, a good response to this problem is one in which the model produced is re-
usable (reliably produces the same results for a given set of data), share-able (the decision
rules are clearly and precisely communicated to all of the students, the teachers, and the
parents, resulting in reliable application of the model across users), and modifiable
(rationales and assumptions on which the model is built are articulated so others can
make intelligent adjustments for new situations). Without assumptions or rationales

TME, vol10, nos.1&2, p .477
explained, intelligent modification of models can be quite difficult, if not impossible.
Notice that neither of the two sample responses in Figure 1 meets the modifiability
criteria for generalization, but they have addressed the re-usability and the share-ability
criteria for generalization.
Over the years, Lesh and colleagues have reported on the local concepts
developed by small groups of students as they engage in various problems, such as the
one described in Figure 1. They indicate that individual students often pose initially
primitive solutions, and as a result of social interactions, challenges, testing and revision,
their initial solutions typically move toward a consensus model that is more stable. The
learning of mathematics is described as an iterative process of expressing, testing and
revising one’s conceptual model. In particular, by using mathematical modeling as a way
to think about mathematics learning, Lesh and Doerr (2003) describe a move away from
behaviorist views on mathematics learning based on industrial age hardware metaphors in
which the whole is viewed simply as a sum of the parts and involving simple causal
relationships. Their perspective on mathematics learning also moves beyond software-
based information processing metaphors, which involve layers of recursive interactions
leading at times to emergent phenomena at higher levels that are not directly derived
from the characteristics of lower levels. Instead, they align their models-and-modeling
perspective on mathematics learning with a biology-based “wetware” metaphor, in which
“neurochemical interactions . . . involv[e] logics that are ‘fuzzy,’ partially redundant, and
partly inconsistent and unstable” (Zawojewski, Hjalmarson, Bowman, & Lesh, 2008, p.
4). Assumed is that students arrive to school with dynamic mathematical conceptual
systems already in place, that these conceptual systems are active and evolving before,

during and after problem solving and learning episodes, and that students must be
motivated to engage in experiences by intellectual need (Harel, 2007) in order to learn.
Thus, even when two students in a group may appear to have the same end product
knowledge on one task, changing the task slightly, but keeping it mathematically
isomorphic with the original, often reveals that the two students are thinking about the
intended mathematical ideas in significantly different ways (Lesh, Behr, & Post, 1987;
Lesh, Landau, & Hamilton, 1983).
What is the role of the small group in learning? Social aspects of acquiring
knowledge from communities have been characterized in society over the decades (e.g.,
Mead, 1962, 1977, Thayer, 1982), and more recent work describes learning in
communities of practice in various trades and occupations (Greeno, 2003; Boaler, 2000;
Wenger & Snyder, 2000; Lave & Wenger, 1991; Wenger, 1998). These situations of
social learning are characterized by the presence of a teacher, tutor, or mentor who
models, teaches and collaborates with novices while engaged in the specific context of
practice, rather than in a classroom. Other social aspects of learning have also been
documented in situations where there is no teacher/tutor/mentor available. For example,
researchers have documented successful collaborations among groups of diverse experts,
where any needed leadership emerges flexibly from within the group in response to
emerging challenges and opportunities (Cook & Yanow, 1993; Wenger, 2000; Wenger &
Snyder, 2000; Yanow, 2000). Both perspectives on social aspects of learning are based
on the assumption that all members of a group bring some understanding to the table, that
the knowledge each brings is idiosyncratic, that the knowledge elicited by the problem is
specific to the context, and that local concept development takes place among the group

TME, vol10, nos.1&2, p .479
members while simultaneously each individual in the group is adapting and modifying
one’s own understanding.
Social aspects of problem solving and learning are also related to the development
of representational fluency, because interactions among collaborators require
representations be used to communicate. When presenting initial solution ideas to peers, a
problem solver typically describes one’s own model using spoken words, written
narratives, diagrams, graphs, dynamic action (e.g., gestures or using geometric software),
tables, and other representations. The interpreting peer, who works to make sense of these
representations, may request clarification, an additional explanation, or may point out
inconsistencies, misrepresentations or other flaws. The peers, thus, iteratively negotiate a
consensus meaning. Lesh and Zawojewski (2007) describe various social mechanisms
that can elicit the use of representations, leading to the development of representational
fluency, including: problem solvers making explanations to each other; groups or
individuals keeping track of ideas they have tried; problem solvers making quick
reference notes for new ideas to try as they continue in a current line of thinking; and
problem solvers documenting their current line of reasoning when they must temporarily
disrupt the work. These types of mechanisms, based largely on communication with
others and oneself, provide the need to generate and use representations, and develop
representational fluency.
Toward an Alternative Framework
Given the assumptions about learning grounded in problem solving, a number of
challenges face the development of a framework for a problem-driven mathematics
curriculum. How can a curriculum framework feature problem-solving activity as the

center of learning, while national and state standards documents highlight specific
mathematical content as the central feature? How can a curriculum framework
accommodate both the multi-topic nature of realistic mathematics problems and the pure
mathematical nature of other mathematics investigations? How can a framework be
represented to convey the complexities implied by the previous questions, yet be practical
in meeting practical classroom needs? How can a framework be represented to inform the
static nature of various standards documents, while also being responsive to changing
societal needs and demands?
Envisioning a Curriculum Framework and It’s Representation
What is Meant by a Problem-Driven Framework?
The development of problem-driven mathematics text series gained momentum in
the U.S. in response to the 1989 NCTM Curriculum and Evaluation Standards for School
Mathematics. In general, the NSF-funded texts (described in Hirsch, 2007a) are
comprised of units of study organized around applied problems or mathematical themes.
In many cases, these curricula use mathematical problems to launch and motivate
learning sequences that progress toward development of understanding and proficiency
for specified mathematical goals. For example, two of the design principles for
developing the Mathematics in Context text series, which is based on the Dutch RME, are
that the starting point of any instructional sequences “should involve situations that are
experientially real to students” and “should . . . be justifiable by the potential end point of
a learning sequence” (Web & Meyer, 2007, p. 82). The commitment to an experiential
basis reflects the commitment to problem-solving as a means to learning, while the well-

TME, vol10, nos.1&2, p .481
defined mathematical end points correspond to a commitment to a curriculum framework
organized around specific mathematical standards or learning objectives. In contrast, the
problem-driven curriculum, Mathematics: Modeling Our World, described by Garfunkel
(2007), is characterized by using mathematical models as end points. The dilemma for
the Mathematics: Modeling Our World development team was coordinating the
mathematics content naturally emerging from their model-based problem-driven
curriculum with a standard mathematics topic driven curriculum framework. Garfunkel
describes how the team grappled with the need to “cover” the scope and sequence of the
required curriculum:
“[W]e believed (and still believe) that if we could not find, for a particular
mathematics topic, a real problem to be modeled, that that topic would not
be included in our curriculum.. . . Instead of ‘strands’ as they are usually
defined we chose to organize curriculum around modeling themes such as
Risk, Fairness, Optimization. We made an explicit decision . . . not to
create a grid with boxes for mathematical and application topics. Instead,
within the themes we chose areas and problems that we believed would
carry a good deal of the secondary school curriculum. . . . For example, it
was decided that one of the major mathematical themes of Course 1 was to
be Linearity, so that each of the units in the course had to carry material
leading to a deepening understanding not only of linear functions and
equations, but also of the underlying concept of linearity.” (pp.161-162).
Garfunkel’s dilemma illuminates a fundamental mismatch between a curriculum
framework that identifies a list of specific mathematical learning objectives or standards
as outcomes, and the development of a curriculum framework driven by problem solving,
and in particular, modeling. The “coverage” issue seems to force the enacted problem-
driven curriculum to be a mix of problem-driven units accompanied by a collection of
gap fillers to address missed content objectives. Thus, while Mathematics: Modeling Our
World began the journey toward a problem-driven curriculum, it was challenged by the

coverage constraint, speaking to the question about what content should be included in a
mathematics curriculum framework.
As a result, questions are raised about envisioning a problem-driven curriculum
framework. Should a problem-driven curriculum framework have as final goals students’
deep understanding of mathematical ideas that support certain types of problems, models
or themes, or to demonstrate abilities about certain big mathematical ideas that were
initiated in problem-solving settings? If the goal of a problem-driven curriculum is to
cover certain mathematical models or themes, should the designers of a curriculum cover
only those areas that naturally emerge in modeling or problem-solving work? If, on the
other hand, the goal of a problem-driven curriculum framework is to accomplish certain
big mathematical ideas, is the power of learning those ideas through problem solving to
some extent defeated?
What is the Nature of Mathematics Content in a Problem-Driven Framework?
This larger question raises at least three issues about what mathematics to include
in a problem-driven framework: What type of problems will the curriculum framework
accommodate? What are the boundaries on the mathematics content to be covered? And
how does the curricular framework adapt to evolving societal, scientific and
technological needs concerning what mathematics is important?
A problem-driven curriculum framework would need to incorporate pure
mathematical investigations, real-world applications, and modeling problems, among
others. Whereas some problems nicely map onto a single mathematical big idea, others,
especially applied and modeling problems emphasize multiple mathematical big ideas—

TME, vol10, nos.1&2, p .483
adding to the complexity of developing such a framework. Consider, for example, the
Aluminum Crystal Size2
MEA, included in Figure 2, as an illustration.
Aluminum Crystal Size Problem Description
The activity is situated in the context of the manufacture of softball bats that would resist denting, but also
won’t break. In materials science, one learns that the larger the typical size of crystals in a metal, the more
prone to bending, and the smaller the typical size the more brittle the metal. A problem was posed that had
two purposes. The first was to motivate the problem solver to quantify crystal size. The second was to
establish a context where a client needs a procedure to quantifying crystal size as part of their quality
control. The client in the problem “hires” the problem solver to create a way to measure, or quantify,
aluminum crystal size using two-dimensional images, such as the ones here:
The images are given in different scales, making visual comparison of crystals in the three samples
difficult. Therefore, the mathematical procedure would need to take scale into account.
A number of different approaches typically emerge, including:
 Draw a rectangular region to designate a sample within each image. Calculate the area of the
rectangle in which the crystals are enclosed. Count the number of crystals in a rectangle drawn.
Compute the average area per crystal. Compare samples.
 Select a sample of crystals within each image and estimate the area of each crystal (e.g., by
measuring the distance across the widest part of a crystal, and the length of the distance
perpendicular to that widest part, and then finding the product of those lengths). Compute the
average area per crystal. Compare samples.
Figure 2. Aluminum Crystal Size Problem
In the Aluminum Crystal Size Problem, multiple big ideas in mathematics are
relevant to producing a good solution. Spatial reasoning is important as the problem
solver needs to figure out ways to quantify regions that are not consistently shaped nor
consistently sized, yet must be considered collectively as a “class” tending toward a
2
For the full activity, see: Diefes-Dux, H. A., Bowman, K. J., Zawojewski, J. &
Hjalmarson, M. (2006). Quantifying aluminum crystal size part 1: The model-eliciting
activity. Journal of STEM Education and Research (7) 1&2, 51-63.
Hjalmarson, M., Diefes-Dux, H. A., Bowman, K. J. & Zawojewski, J. S. (2006).
Quantifying aluminum crystal size part 2: The Model-Development Sequence. Journal
of STEM Education and Research h (7) 1&2, 64-73.

certain size. The problem solver must also consider what parts of the regions to use in the
quantification. Measurement is another big idea addressed, since a definition of crystal
size needs to be generated and mathematized. Proportional reasoning is needed because
the micrographs are all shown to different scales, which needs to be accounted for in the
development of the mathematical model for crystal size. Sampling is important when
deciding what regions of the micrograph to use to determine the size of the crystals in the
full image; a good solution will incorporate a method for selecting samples to include in
the mathematical model. Measures of centrality are likely to emerge because quantifiable
characteristics of the various crystals need to be summarized in some way to come up
with a single measurement of crystal size. Finally, mathematical modeling is the
centerpiece of the activity. If the Aluminum Crystal Size activity is used as the
centerpiece of a unit of study, the problem context drives what mathematical topics are
encountered. A framework, then, is needed to help make decisions about which topics to
investigate more deeply, whether to stay within the problem context in those
investigations, and whether or when to incorporate other more conventional lessons or
purely mathematical investigations on the conventional topics.
The second consideration concerns the boundaries of mathematics curricular
topics. For example, an economics problem may require designing a mathematical model
that optimizes costs while producing the highest quality possible. An engineering
problem may have ethical ramifications, where the “best” possible mathematical solution
to attain cost-effectiveness may not meet equity considerations. A problem may lend
itself to an elegant mathematical solution that uses cutting-edge technology, but the
solution may not work with the commonly available technology. In the real world, when

TME, vol10, nos.1&2, p .485
clients want quantitative-based solutions that are cost-effective yet most powerful,
thoroughly but quickly produced, and usable by a wide audience yet secure from abusers,
the mathematical and non-mathematical considerations are inseparable. A collaboration
of engineering educators have grappled with such an issue in the context of engineering
education, where the goal has been to teach foundational engineering principles through
mathematical modeling problems that carry competing constraints when considering
ethical components (e.g., Yildirim, Shuman, Besterfield-Sacre, 2010).
The content of mathematics curriculum needs to be an entity that can evolve, and
can be flexible and nimble as problems faced in the workplace and society evolve—the
third consideration. To illustrate, two hundred years ago the computational algorithms
needed for bookkeeper’s math were appropriately the main focus of school mathematics
content. Now-a-days, research on current professional use of mathematics in fields such
as engineering (e.g., Ginsburg, 2003, 2006), health sciences (Hoyles, Noss, & Pozi, 2001;
Noss, Holyes & Pozi, 2002) and finance (Noss & Hoyles, 1996) reports an increasing
need for students to develop or adapt mathematical models to solve novel problems and
to flexibly interpret and generate representations. Zawojewski, Hjalmarson, Bowman, &
Lesh (2008) indicate that “the real world uses of mathematics are described [in the
studies referenced above] as often requiring that mathematical knowledge be created or
reconstituted for the local [problem] situation and that content knowledge be integrated
across various mathematics topics and across disciplines” (p. 3).

A Proposal for an Alternative Problem-Driven Curriculum Framework
Major dilemmas of constructing on over-arching curriculum framework were
illuminated using the two problem driven curriculum frameworks described above. But,
even when considered together, the RME and Mathematics: Modeling Our World do not
necessarily accommodate all aspects of important mathematics to be learned. In
particular, the RME framework is driven by problem-solving launches followed by a
sequence of activities and instruction that lead to an increased understanding of formal
mathematical systems. Garfunkel’s Mathematics: Modeling Our World is organized
around themes such as risk, fairness, optimization and linearity, each representing
important areas of mathematics associated with formal mathematical modeling. Both
generally aim toward formal mathematical goals, but do not have as end goals
mathematics deeply embedded within broad contextual situations and areas such as ethics
or equity. The alternative proposed here is based on a notion of model-development
sequences that broadens the one described by Lesh, Cramer, Doerr, Post, & Zawojewski
(2003). Like RME and Garfunkels’ curricula’s development, the underlying assumption
is that powerful learning of mathematics emerges from students’ mathematization of
problematic situations. Going beyond RME and Garfunkel, a problem-driven
mathematics curriculum framework built around model-development sequences has the
potential to incorporate both formal mathematical big ideas/models and real world messy
models that are intertwined with non-mathematical constraints.
Lesh, Cramer et al. (2003) describe model-development sequences as beginning
with model-eliciting activities (MEAs), which are instantiated in the two problems
presented so far (Figures 1 & 2). The main characteristic of a MEA is that the problem

TME, vol10, nos.1&2, p .487
requires students to create a mathematical model in response to the task posed, which
could be extended to the production of smaller parts of formal mathematical systems.
MEAs have traditionally been designed using six specific design principles (Lesh,
Hoover, Hole, Kelly, & Post, 2000) to devise “authentic” contexts, involving a client with
a specified need for a mathematical model that facilitates making a decision, making a
prediction, or explaining a reoccurring type of event in a system. Following the initial
MEA are planned model-exploration activities (MXAs), which vary from comparing and
contrasting trial models posed by peers in a class, to more conventional meaning-based
instruction on various mathematical aspects of the model. For example, the Aluminum
Crystal Size problem may be followed up with a lesson on the role and power of random
sampling for making inferences, or an opportunity for students to compare and contrast
their procedures for determining typical crystal size in micrograph samples. Similarly,
one of the authors interviewed a teacher who enacted an MXA activity with her third
grade students who had completed the Grant Avenue Reading Certificate Problem
(Figure 1). After the teacher asked the third grade students to present their rules to each
other, she asked students to identify similarities and differences among the sets of rules,
and probed students perceptions of the pros and cons. By asking questions about what
aspects of the situation each set of rules attended to and ignored, and how the choice of
variables influenced the impacts the outcomes, she was teaching foundational ideas of
modeling. For example, Group 2’s response (Figure 1) ignores the number of books in
the data—depending only on the number of pages to represent “reading a lot.” Group 1
(Figure1), on the other hand, used all three types of data (number of pages, number of
books and the rating of easy/medium/hard). Even though Group 2’s response did not fully

meet the criteria articulated in the problem, the use of page numbers only, and not the
number of books, is defendable as an indicator of amount of reading. Helping the
students articulate rationales for their decisions supports the development of an initial
understanding that models are systems that represent larger systems, and inevitably
capture some features of the original system, while ignoring other aspects.
A model-development sequence closes with a model-adaptation or model-
application activity (MAA). To illustrate the power of a MAA, consider the full sequence
of activities that has been used in the first-year engineering course (with students fresh
out of high school) at Purdue University3
. The opening Nano Roughness4
MEA, (see
Figure 3) is “set in the context of manufacturing hip-joint replacements where the
roughness of the surface determines how well the joint replacement moves and wears
within the hip socket” (Hjalmarson, et al., p. 41). Given digital images of the molecular
surface of different samples of metal, students were asked to create a procedure for
quantifying the roughness of each sample, which resulted in a variety of models. The
subsequent MXA introduced students to a conventional engineering model for
quantifying roughness, the average maximum profile (AMP) method, and then asked
them to compare their model for quantifying roughness to the conventional engineering
model. The goal for this MXA was to enable students to identify and understand trade-
3
Purdue’s first-year engineering program has been using MEAs and model-development
sequences for the past 9 years with approximately1500 student per year in West
Lafayette, Indiana, USA (Hjalmarson, Diefes-Dux & Moore, 2008).
4
The full activity can be found in J. S. Zawojewski, H. A. Diefes-Dux, & K. J. Bowman
(Eds.), (2008). Models and modeling in engineering education: Designing experiences
for all students (pp. 317-322). Sense: Rotterdam, The Netherlands. The lead author of the
activity is Tamara J. Moore.

TME, vol10, nos.1&2, p .489
offs between models, and to identify and understand rationales and assumptions
underlying different models.
Nano Roughness MEA
This goal of this activity is to produce a procedure to quantify roughness of metal surfaces at a
microscopic level. Students are given atomic force microscope (AFM) images, similar to the one
below, of three different samples of metal surfaces. At the atomic level, the lighter parts of the image
represent higher surface, and the darker parts of the image represent lower surface. The gray scale
indicator, to the side, provides information about the height of the surface. To motivate the problem
situation, the students learn that the company, who is their client, specializes in biomedical
applications of nanotechnology. They are planning to produce synthetic diamond coatings for use in
orthopedic and biomedical implants, and need to have a way to quantify roughness of the coating
surfaces. Given three top-view images of gold samples (illustrated in the one sample below), the
modelers are asked to develop a procedure for quantifying the roughness of the material so the
procedure could be applied to measure roughness in other types of metal samples.
Sample of an AFM image of gold surface (AFM data courtesy of Purdue University Nanoscale Physics Lab)
Figure 3. Nano Roughness MEA Description
The model-development sequence closes with a Model-Adaptation Activity
(MAA) that requires students to adapt either their model for measuring roughness, or the
conventional model, to a new situation. To do the work in the Purdue example, students
were given a raw data set that had been used to produce a sample digital image. These
raw data had been gathered by using an atomic force microscope (AFM), which uses a
nano-scale probe dragged along the surface of the metal sample in lines at regular

intervals, measuring the relative heights along the bumps of the molecules. The students
were asked to generate, using MATLAB, a cross sectional view of any line segment
drawn on an image of the gold surface. In particular, they produced graph-like products
that portrayed the relative heights of the bumps and valleys for any line segment drawn
on an image. The mathematical learning goals for this MAA were to conduct 2-
dimensional array manipulations of the data and to incorporate statistical reliability
considerations into the process. Broader learning goals for the Nano Roughness problem
include programming and fundamental engineering principles—illustrating how
mathematics learned may be embedded and intertwined to what traditionally has been
considered non-mathematical topics.
While MEAs, and their accompanying model-development sequences have
traditionally been tied to authentic realistic modeling contexts, the basic concept of
eliciting a mathematical model can be broadened to incorporate the more traditional
modeling work, such as described by Garfunkel (2007). The model-development
sequence framework can also be envisioned to include the elicitation of aspects of formal
mathematical systems, such as what is the aim in RME. In other words, model-
development sequences have a great deal of potential to serve as an umbrella framework
that provides a way to unify problem-driven curricula frameworks, especially when
considering the flexibility of Learning Progress Maps (LPMs), which is proposed as a
possible way to represent problem-driven curriculum frameworks.
Envisioning a Representational System for Problem-Driven Curriculum

TME, vol10, nos.1&2, p .491
Using a metaphor of topographical maps, Learning Progress Maps (LPMs) can be
thought of as a dynamic representation of mathematics curriculum and students’ learning
(Lesh, Lamon, et al., 1992; Lesh, unpublished manuscript). Lesh’s goal in developing this
concept has been to help teachers readily answer practical classroom questions such as:
What concepts do my students still need to address in this unit I am teaching? Which
topics would be strategic to address next? What are concepts or topic areas where my
students appear to require more experience? Which students are having difficulty with
specific concepts, and which have demonstrated learning in those areas? Single score
results from large-scale measures do not provide useful information for these questions,
whereas item-by-item information for every student might be overwhelming to use as an
everyday tool to make decisions about classroom instruction. Portfolio assessment is
difficult to define and standardize, let alone use for day-to-day classroom decisions about
instruction. On the other hand, good teachers do develop their own personal methods to
keep track of individual students’ progress in a variety of ways, although their systems
are idiosyncratic to the teacher, often very detailed, and usually perceived by others as
too time consuming to maintain.
How Might a Learning Progress Map (LPM) Represent a Problem-driven Curriculum?
Consider a hypothetical topographical map representing a curriculum organized
around mathematical big ideas, important mathematical models, or formal mathematical
systems, presented in Figure 4. Lesh, Lamon, et al. (1992) describe the mountains of the
landscape as corresponding to the “big mathematical ideas” of a given course (6 to 10 big
ideas in this case), and the surrounding terrain of foothills and valleys as depicting facts
and skills related to the big ideas. Using the topographical maps metaphor, one can think

about the height of the mountain as representing the relative importance of big
mathematical ideas in the course, while relationships among the big ideas can be
expressed by the proximity of the mountains to each other. The tops of the mountains
would represent deep understanding of the big idea, abilities supporting the big ideas can
be represented on the sides of the mountains, and associated tool skills (e.g.,
manipulations, skills, facts) can be represented by the regions of the surrounding foothills
and valleys.
Figure 4. Representation of Big Ideas, Supporting Abilities, and Tools in a Course
A top-down view of the topographical curriculum map (illustrated in Figure 5),
might delineate the interplay of the big mathematical ideas, supporting abilities and tool
skills to be “covered” in the given course, in a way analogous to a traditional scope-and-
sequence document.

TME, vol10, nos.1&2, p .493
Figure 5. Top Down View of Curriculum Scope and Sequence
On a LPM, problem-solving, modeling, deep insights into a designated
mathematical big idea, and higher-order mathematical thinking about the idea would be
designated in regions on the tops of the mountains. Thus, problems that involve multiple
big mathematical ideas, such as the Nano Roughness MEA, could be represented by
multiple mountains (e.g., 3-d geometry, proportions, sampling, measurement,
mathematical models). The height of the mountains, and the arrangement of the regions
around them, would represent the relative importance of the major mathematical areas
with respect to the MEA. Supporting concepts and procedures would correspond to the
sides of the mountain, and needed skills and facts would correspond to the adjoining
valleys around each mountain. For example, in the Nano Roughness MEA, the fluent
interpretation and manipulation of the scales would be an important component of
proportional reasoning, and thus represented on the sides of a proportional reasoning
mountain. The valleys nearby each mountain would represent the automatic skills and
concepts that might be thought of as the tools of the trade for that big idea, such as
masterful and precise computation or algebraic manipulation. Another illustration might
be the linearity theme of Mathematics: Modeling out World, as described by Garfunkel
(2007). Linearity might be the name of the mountain, and the idea of representing linear
expressions in various forms (as narratives of situations, as tables, as graphs) may each
correspond to regions along the side of that mountain, and fluent manipulation of linear
equations might be represented in the valley nearby the mountain.
The potential flexibility of the proposed representational system is greatly enabled
by the power of technology. For example, given that the concept of linearity is a major
theme in Course 1 of Mathematics: Modeling out World, linearity may be important to a

number of units, and emerge in a variety of contexts. In the mountain representational
system for each unit, the theme of linearity may be represented as an overlay of a
particular colors or textures (e.g., striping, dotting) on all terrains. Further, theoretical
perspectives on learning may also be represented using different intensities of colors to
illustrate the three dimensions in RME, or an activity’s classification in modeling
sequences i.e., MEAs, MXAs and MAAs. The envisioned representation of a curriculum
framework could provide teachers with the opportunity to manipulate the map, providing
varied views of the curriculum. For example, a teacher may want to see how linearity
emerges across chapters within a course by viewing any and all mountains that represent
linearity across chapters. While one can imagine many useful scenarios of manipulation,
the greatest potential for LPMs, however, is probably representing students’ progress
through the curriculum.
How Might a Learning Progress Map (LPM) Serve Assessment?
In a problem-driven curriculum framework, assessment of big ideas and models
would be supported by LPMs which are envisioned as providing manipulable
representations of students' attained curriculum. Specific assessment data can be used to
“fill in” appropriate regions of a LPM for a particular student in a course. Since in a
problem-driven curriculum, the students’ mathematical experiences begin in problem-
solving environments, and supporting skills may be learned or mastered later and at
various levels, record-keeping is potentially very challenging. Planning assessment points
to correspond with particular regions of the map would be a strategy for input points that
would in turn help keep track of accomplishments by individuals, while also potentially
providing a visual picture that organizes the assessment data for the individual students.

TME, vol10, nos.1&2, p .495
Assessment data points can be drawn from students’ responses to problem-solving
or modeling activities and used to guide subsequent instructional activity. To illustrate,
consider the work of Diefes-Dux and colleagues, who have been very active in
documenting students’ modeling performance on iterations of revised solutions to MEAs.
They have developed systematic ways to evaluate the development of mathematical
models that students generate (e.g., Carnes, Diefes-Dux, & Cardella, 2011; Diefes-Dux,
Zawojewski, & Hjalmarson, 2010). Their assessment rubric (Diefes-Dux, Zawojewski,
Hjalmarson, & Cardella, 2012) that addresses four general characteristics of the models is
made into task-specific versions for each MEA. Their recent work has focused on the
challenge of identifying and implementing feedback to students with a goal of prompting
students to rethink and revise their solution model to be more powerful and efficient
(personal conversation with Diefes-Dux, January 17, 2012). One can imagine that this
line of research would be enhanced with the proposed framework and representational
system. For example, Diefes-Dux and colleagues’ evaluate the generalizability of
students’ models based on three criteria. Assessment of a model’s “re-usability”
documents the stability of the model over its independent applications; that is, whenever
the model is re-applied to a given data set the model will produce the same results each
time. Assessment of model’s “share-ability” documents whether the model is
communicated well enough so that other users can apply the model independently and
reliably. Finally, the assessment of the model’s “adaptability” focuses on the articulation
of critical rationales and assumptions on which the model is constructed, so that an
external user would be able to intelligently modify the model for new, somewhat
different, circumstances. These three dimensions could be easily represented and

manipulated in the envisioned framework to look for patterns and trends in students’
series of revised models.
In a problem-driven curriculum framework, assessment of students’ performance
on concepts, skills, and procedures that support big ideas and models can be facilitated by
LPMs and guided by available mathematics education research. For example, in the
Ongoing Assessment Project (OGAP), Petit and colleagues (e.g., Petit, Laird, & Marsden,
2010) examined all available mathematics education research in selected domains,
identified important benchmarks and “trouble spots” of understanding, and targeted those
specific concepts and skills for the development of assessment items and activities. They
have completed the work on fractions, multiplication and proportions. Such assessment
items can be used as data points in the side regions and valleys of mountains
corresponding to the big mathematical ideas. Further, in conjunction with the growing
body of research on learning progressions (e.g., Clements, 2004), assessment points that
have been embedded in the learning trajectories can become benchmarks that are
carefully placed to track general progress as students eventually abstract from their
variety of situations to generalized mathematical ideas.
How Might a Learning Progress Map (LPM) be Used to Inform Practice and Programs?
The envisioned dynamic LPMs would provide a means for teachers to quickly and
easily identify information relevant to day-to-day questions for teaching and students’
learning. For example: What concepts do my students still need to address? Which topics
would be strategic to address next? Which students are (or are not) having difficulty with
specific concepts? Using a keystroke, summarized students’ assessment data could
displayed on the LPM, providing opportunities for nimble decision-making about

TME, vol10, nos.1&2, p .497
classroom practice. By illuminating the whole class’s attainment on the LPM curriculum,
teachers would be able to see what yet needs to be addressed in the course, and what may
need some reteaching. Profiles of individual students’ attainment could help teachers plan
to group students for differentiated instructional experiences. LPMs could, for example,
help teachers to form problem-solving groups by identifying students with a variety of
expertise relevant to the problem. Individual profiles, when displayed side-by-side, could
also inform teachers’ decisions about students access to limited resources (e.g., volunteer
tutors, particular technological assistance, advanced placement coursework).
Self-assessment could become a major component of classroom experience.
Students could use their own individual profiles to self-assess their own progress, and
perhaps even select problems through which they can address their own areas of need. In
an advanced version of LPM, where the curriculum topics are linked to appropriate
problems, perhaps students could select a context they like to think about (e.g., sports,
health care), and be assigned an appropriate problem from the targeted area of need. By
integrating an assessment system with the curricular map, LPMs could be used as a tool
to guide students’ selection of problems that have the potential to move them forward
mathematically.
Professional development and program evaluation can also be enhanced through
LPMs. Lesh, Lamon, et al., (1992) describe a variety of program level assessments that
could be accomplished by dynamic LPMs. For example, a summary class attainment map
that looks like the one in Figure 5, suggests instruction that is highly skill-based, and thus
provides an opportunity to for a teacher to confront one’s own (perhaps unconscious)
assumption that problem solving and deep conceptual understanding can only be

addressed after all of the “basics skills” have been accomplished. On the other hand, a
summary class attainment map that looks like the one illustrated in Figure 6 might
suggest that a teacher is effectively implementing a problem-driven curriculum, given
that the attained map illustrates splashes and spreads from multiple points near the tops of
the mountains, and oozing downward to the sides of mountains and surrounding valleys.
Fig 5: LPM (green) in Skill-based Attainment by Students
Fig 6: A LPM (green) in Multi-level Attainment by Students
Reflections
The envisioned problem-driven mathematics curriculum framework supported by
a dynamic representational system, LPM, seems feasible. Given the potential of today’s
technologies, design research (Kelly, Lesh, Baek, 2008) methodologies could be used to

TME, vol10, nos.1&2, p .499
simultaneously build, study and revise theoretical, pedagogical, and practical
considerations of a problem-driven curriculum framework and its representational
system. The LPM could be manipulated and revised quickly and easily in response to
various changing conditions, such as changes in what constitutes important mathematics,
changes in important problem context, changes in new content-driven state standards, and
changes in interdisciplinary and social considerations. While the representational system
has yet to be actualized, many aspects of problem-driven curricular frameworks are
already under research and development. Imagining future work that links technology-
driven LPMs and problem-driven curriculum frameworks brings a variety research
questions and potential issues for investigation.
Given that problem-driven mathematics curriculum frameworks are grounded in
the assumption that students learn mathematics while engaged in complex problem-
solving activity, a question arises about how LPMs could be used to represent such
curricula. What would a LPM look like for a course, or a unit of study? What will be
identified as the “big ideas” or mountains around which the mathematical terrain is
developed? What variables need to be represented in the LPM, beyond content topics?
What needs to be fixed and what needs to be flexible in the software? These are only a
few of the questions that need to be answered in interdisciplinary teams of mathematics
educators, curriculum developers, assessment experts, and software developers in a
design process.
How can LPMs be used to identify when, and the extent to which, problem-based
instruction supports the given problem-based curriculum? Collaborative research and
development would be needed to design software to display an image, such as the one in

Figure 5 that represents successful implementation of problem-based instruction. The
design of the software would require the identification of variables and development of
models to show the splashes and spreads from multiple points near the tops of the
mountains, oozing downward, and eventually filling in the valleys. The needed data
include the curriculum specifications, student assessment data, and teacher input about
experiences implemented. The goal would be to provide real-time information to teachers
and their support personnel concerning what students are learning, and to use that
information to adjust instructional strategies to align with those appropriate for problem-
based learning.
How might LPMs assist classroom teachers in their enactment of a problem-
driven curriculum, yet help to keep an eye on “content coverage” as potentially required
by other stake holders? To support implementation of problem-driven curricula in
environments that are driven by standards and emphasize content coverage, teachers’
need to have tools that help them traverse the challenges of real world implementation.
The envisioned LPMs must have embedded in them the ability to manipulate the
representations so that teachers can easily check on “content coverage” while teaching a
problem-based curriculum. Further, they need to be able to easily check on individual
student progress in order to plan for reasonable differentiation. Challenges in
implementing a problem-based curriculum must be addressed by well-designed LPMs
that are easily used by teachers to inform their questions and issues.
References
Boaler, J. (2000). Exploring situated insights into research and learning. Journal for
Research in Mathematics Education, 31, 113-119.

TME, vol10, nos.1&2, p .501
Carnes, M.T., Diefes-Dux, H.A., & Cardella, M.E. (2011). Evaluating student responses
to open-ended problems involving iterative solution development in Model Eliciting
Activities. Proceedings of the American Society for Engineering Education Annual
Conference, Vancouver, CAN.
Clements, D. & Sarama, J. (2004a). Hypothetical learning trajectories [special issue],
Mathematical Thinking and Learning, 6(2).
Clements, D. & Sarama, J. (2004b). Learning trajectories in mathematics education.
Mathematical Thinking and Learning, 6, 81 – 89.
Cook, S. D. N & Yanow, D. (1993). Culture and organizational learning. Journal of
Management Inquiry 2(4), 373-390.
Diefes-Dux, H. A., Bowman, K., Zawojewski, J., & Hjalmarson, M. (2006). Quantifying
aluminum crystal size part 1: The model-eliciting activity. Journal of STEM
Education and Research (7) 1&2, 51-63.
Diefes-Dux, H. A., Zawojewski, J. S., & Hjalmarson, M.A. (2010). Using educational
research in the design of evaluation tools for open-ended problems. International
Journal of Engineering Education, 26 (4): 807-819.
Diefes-Dux, H. A., Zawojewski, J. S., Hjalmarson, M. A., & Cardella, M. E. (2012). A
framework for analyzing feedback in a formative assessment system for mathematical
modeling problems. Journal of Engineering Education (101) 2, 375-406.
Freudenthal, H. (1991). Revisiting Mathematics Education. China Lectures. Dordrecht,
The Netherlands: Kluwer Academic Publishers.
Garfunkel, S. (2007). Mathematics: Modeling our world. In Ch. Hirsh (Ed.). Perspectives
on design and development of school mathematics curricula. (pp. 161- 164). NCTM,
Reston, VA.
Ginsburg, J. (2003). The mathematical behaviors of structural engineers. Doctoral
Dissertation. Standford University. Dissertation Abstracts International, A. 64, 5.
Ginsburg, J. (2006). The mathematical modeling of structural engineers. The
Mathematical Thinking and Learning. 8(1), 3 – 36.
Greeno, J. (2003). Situative research relevant to standards for school mathematics. In J.
Kilpatrick, W. G., Martin & D. Schifter (Eds.), A research companion to Principles

and Standards for School Mathematics (pp. 304-332). Reston, VA: The National
Council of Teachers of Mathematics.
Harel, G. (2007). The DNR system as a conceptual framework for curriculum
development and instruction. in R. Lesh, E. Hamilton, & J. Kaput (Eds.). Foundations
for the future in mathematics education (pp.263-280).Mahweh, NY: Erlbaum and
Asssociates.
Hjalmarson, M., Diefes-Dux, H. A., Bowman, K., & Zawojewski, J. S. (2006).
Quantifying aluminum crystal size part 2: The Model-Development Sequence.
Journal of STEM Education and Research 7(1&2), 64-73.
Hjalmarson, M., Diefes-Dux, H., & Moore, T. (2008). Designing model development
sequences for engineering. In J. S. Zawojewski, H. A. Diefes-Dux, H. & K. J.
Bowman (Eds.), Models and modeling in engineering education: Designing
experiences for all students, (pp. 37-54). Rotterdam, The Netherlands: Sense
Publishers.
Hirsh C. R. (Ed.) (2007a). Perspectives on the design and development of school
mathematics curriculum. Reston, VA: The National Council of Teachers of
Mathematics.
Hirsh, C. R. (2007b). Curriculum materials matter. In C.R. Hirsh (Ed.), Perspectives on
the design and development of school mathematics curriculum, (pp. 1-5). Reston,
VA: The National Council of Teachers of Mathematics.
Hoyles, C., Noss, R., & Pozi, S. (2001). Proportional reasoning in nursing practice.
Journal for Research in Mathematics Education, 32 (1), 4–27.
Kelly, E. A., Lesh, R. A., & Baek, J. Y. (Eds.). (2008). Handbook of design research
methods in education: Innovations in science, technology, engineering and
mathematics learning and teaching. London: Routledge.
Lave, J. & Wenger, E. (1991). Situated learning: Legitimate peripheral participation.
Cambridge: UK: Cambridge University Press.
Lesh, R. (unpublished manuscript). Improving assessment practices & encouraging
teacher development using graphic, interactive, N-dimensional learning progress
maps.

TME, vol10, nos.1&2, p .503
Lesh, R., Behr, M., & Post, T. (1987). Rational number relations and proportions. In C.
Janvier (Ed.), Problems of representation in teaching and learning mathematics.
Hillsdate, NJ: Lawrence Erlbaum Associates.
Lesh, R., Cramer, K., Doerr, H. M., Post, T., & Zawojewski J. S. (2003). Model
development sequences. In R. A Lesh & H. Doerr (Eds.), Beyond constructivism: A
models and modeling perspective on problem solving, learning and instruction in
mathematics and science education (pp. 35-58). Mahwah, NJ: Lawrence Erlbaum
Associates.
Lesh, R. & Doerr, H. M. (in press). Alternatives to trajectories and pathways to describe
development in modeling and problem solving. In W. Blum, R. Borromeo Ferri, &
K. Maaß (Eds.), Mathematics teaching in the context of reality, culture and teacher
professionalism.
Lesh, R. & Doerr, H. M. (2003). Foundations of a models and modeling perspective on
mathematics teaching, learning, and problem solving. In R. Lesh & H. M. Doerr
(Eds.), Beyond constructivism: Models and modeling perspectives on mathematics
problem solving, learning, and teaching (pp. 3–33). Mahwah, NJ: Erlbaum.
Lesh, R. & Harel, G. (2003). Problem solving, modeling, and local conceptual
development. Mathematical Thinking and Learning, 5(2&3), 157-190.
Lesh, R., Hoover, M., Hole, B., Kelly, E., & Post, T. (2000). Principles for developing
thought-revealing activities for students and teachers. In A. Kelly and R. Lesh (Eds.),
Handbook of research design in mathematics and science education. Mahwah, NJ:
Erlbaum Publishers.
Lesh, R., Lamon, S. J., Gong, B., & Post, T. R. (1992). Using learning progress maps to
improve instructional decision making. In Lesh & Lamon (Eds.) Assessment of
Authentic Performance in School Mathematics (pp. 343-365). Washington DC:
AAAS.
Lesh, R., Landau, M., & Hamilton, E. (1983). Conceptual models and applied
mathematical problem-solving research. In Lesh & Landau (Eds.) Acquisition of
mathematics concepts and processes (pp. 263-343). New York City: Academic Press.
Lesh, R. & Yoon, C. (2004). Evolving communities of mind – in which development
involves several interacting and simultaneously developing strands. Mathematical

Thinking and Learning, 6 (2), 205-226.
Lesh, R. & Zawojewski, J. S. (1992). Problem solving. In T. Post (Ed.), Teaching
mathematics in grades K-8: Research-based methods (pp. 49-88). Newton, MA:
Allyn & Bacon.
Lesh, R. & Sriraman, B. (2005). Mathematics education as a design science. ZDM-The
International Journal on Mathematics Education, 37(6), 490-505.
Lesh, R. & Zawojewski, J. S. (2007). Problem solving and modeling. In F. Lester (Ed.),
Second handbook of research on mathematics teaching and learning (pp. 763-804).
Charlotte, NC: Information Age Publishing.
Lester, F. K. Jr. & Charles, R. I. (Eds.) (2003). Teaching mathematics through problem
solving: Grades pre-K-6. Reston, VA: National Council of Teachers of Mathematics.
Lester, F. K. Jr. & Kehle, P. E. (2003). From problem solving to modeling: The evolution
of thinking about research on complex mathematical activity. In R. Lesh & H. Doerr,
(Eds.), Beyond constructivism: Models and modeling perspectives on mathematics
problem solving, learning and teaching (pp. 501-518), Mahwah, NL: Erlbaum.
Mead, G. H. (1962). Mind, self, and society: From the standpoint of a social behaviorist.
(C. W. Morris, Ed.). Chicago, IL: University of Chicago Press. (Original work
published in 1934).
Mead, G. H. (1977). The social psychology of George Herbert Mean: Selected papers. In
A. Strauss (Ed.), Heritage Sociology Series. Chicago, IL: University of Chicago
Press. (Original work published in 1956.)
National Council of Teachers of Mathematics (1989). Curriculum and evaluation
standards for school mathematics. Reston, VA: Author.
National Council of Teachers of Mathematics (2000). Principles and standards for
school mathematics. Reston, VA: Author.
National Governor's Association (NGA) & Council of Chief State School Officers
(CCSSO) (2011). Common Core State Standards for Mathematics. Retrieved on
April 10, 2011 from
http://www.corestandards.org/assets/CCSSI_Math%20Standards.pdf.
Noss, R. & Hoyles, C. (1996). The visibility of meanings: Modeling the mathematics of
banking. International Journal of Computers for Mathematics Learning, 1, 3-31.

TME, vol10, nos.1&2, p .505
Noss, R., Hoyles, C., & Pozi, S. (2002). Abstraction in expertise: A study of nurses’
conceptions of concentration. Journal for Research on Mathematics Education, 33,
204-229.
Petit, M. M., Laird R. E., & Marsden E. L. (2010). A Focus on fractions: Bringing
research to the classroom. New York: Routledge.
Piaget, J. (1928) Judgment and reasoning in the child. London: Routledge & Kegan.
Piaget, J. (1950). The psychology of intelligence. (London: Kegan Paul, Trench, &
Trubner. (Original work published in French in 1947)
Porter, A. C. (2002). Measuring the content of instruction: Uses in research and practice.
Educational Researcher 31 (7), 3-14.
Porter, A., McMaken, J., Hwant, J., & Yang, R. (2011). Common Core Standards: The
new U.S. intended curriculum. Educational Researcher 40 (3), 103-116.
Smith, S.A. & Smith M. E. (2007). A model for framing the cognitive analysis of
progressive generalization in elementary mathematics. In T. Lamberg & L.R. Wiest
(Eds.), Proceedings of the 29th
annual meeting of the North American Chapter of the
International Group for the Psychology of Mathematics Education, (pp. 249 – 255).
Stateline (Lake Tahoe), NV: University of Nevada, Reno.
Schoen, H. L. & Charles, R. I. (Eds) (2003). Testing mathematics through problem
solving: Grades -12. Reston, VA: The National Council of Teachers of Mathematics.
Silver, E. (Ed.) (1985). Teaching and learning mathematical problem solving: Multiple
research perspectives. Hillsdale, NJ: Lawrence Erlbaum.
Stein, M. K., Boaler, J., & Silver, E. A. (2003). Teaching mathematics through problem
solving: Research perspectives. In H. L. Schoen & R. I. Charles (Eds.), Teaching
mathematics through problem solving: Grades 6-12. Reston, VA: The National
Thayer, H. S. (Ed.). (1982). Pragmatism: The classic writings. Cambridge, MA: Hackett
Publishing.
van den Heuvel-Panhuizen, M. (2003). The didactical use of models in realistic
mathematics education: An example from a longitudinal trajectory on percentage.
Educational Studies in Mathematics, 54, 9-35.

Wenger, E. (1998). Communities of practice: Learning, meaning and identity.
Cambridge U.K: Cambridge University Press.
Wenger, E. (2000). Communities of practice and social learning systems. Organization,
7(2), 225-246.
Wenger, E. & Snyder, W. (2000). Communities of practice: The organization frontier.
[Electronic Version]. Harvard Business Review. 78(1), 139-145.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological
processes (M. Cole, V. John-Steiner, S. Scribner, & E. Souberman, Eds.). Cambridge,
MA: Harvard University Press.
Yanow, D. (2000). Seeing organizational learning: A “cultural” view. Organization, 7(2),
247-477.
Yeatts, C. L., Battista, M. T., Mayberry, S., Thompson, D. & Zawojewski, J. S. (2004).
Navigating through problem solving and reasoning in grade 3. Reston, VA: National
Yildirim, T. P., Shuman, L., & Besterfield-Sacre, M. (2010). Model-eliciting activities:
Assessing student problem solving and skill integration processes. International
Journal of Engineering Education, 26(4): 831-845
Zawojewski, J. S., Diefes-Dux, H, A., & Bowman, K. J. (Eds.) (2008). Models and
modeling in engineering education: Designing experiences for all students (pp. 317-
322). Rotterdam, The Netherlands: Sense Publications.
Zawojewski, J. S., Hjalmarson, M. A., Bowman, K. J., & Lesh, R. (2008). Chapter 1: A
modeling perspective on learning and teaching in engineering education. In J. S.
Zawojewski, H. A. Diefes-Dux, & K. J. Bowman (Eds.), Models and modeling in
engineering education: Designing experiences for all students (pp. 1-16). Rotterdam,
The Netherlands: Sense Publications.

A Proposal For A Problem-Driven Mathematics Curriculum Framework

More Related Content

Similar to A Proposal For A Problem-Driven Mathematics Curriculum Framework (20)

More from Lori Moore (20)

Recently uploaded (20)

A Proposal For A Problem-Driven Mathematics Curriculum Framework