ABOUT THIS JOURNAL EDITORIAL BOARD CURRENT ISSUE ARCHIVES FOR CONTRIBUTORS COPYRIGHT

AFFILIATES AND SPONSORS

Radical Pedagogy (2007)

ISSN: 1524-6345

Darwin, Descartes and Dewey: The Biological Basis for a Problem-Based Learning Curriculum

Russell D. Pritchard, Ed.D.

Philadelphia University

School House Lane & Henry Avenue

Philadelphia, PA

215-951-2761

pritchardr@philau.edu

Dr. Pritchard is an assistant professor in the masters degree program in Instructional Design & Technology

at Philadelphia University. Prior to teaching there, Dr. Pritchard taught high school science in New Jersey,

and was the Director of Training for a major computer company. He has a B.A. from The College of New

Jersey, an M.Ed. from Penn State University, and an Ed.D. from Wilmington College.

Abstract

RADICALPEDAGOGY

Digital instructional technologies are frequently selected by educators without first deciding what the goals of

education (or training) are, what teaching methods will best achieve these goals, or how various digital

instructional technologies will support or enhance these goals. Thus, the digital instructional technologies

themselves frequently become the focus of curricular reforms.

In this paper I will make the case that educators must first decide what the goals of education are, what

instructional methods will best serve these goals, and only then should they select digital instructional

technologies that help their students achieve their educational goals. This case will be built upon a deductive

argument for using the instructional methodology known as problem based learning (PBL) as the foundation

for establishing educational goals and selecting the most appropriate digital instructional technologies.

Introduction

In the winter of 2006, I attended a conference where teachers and professors presented their ideas on using

digital instructional technologies to improve the teaching and learning process. In one session, the presenter

explained how his school district had recently purchased a software package that used the Global Information

System (GIS); students were using this instructional technology to locate, plot, and otherwise graphically

display all the water wells in their county. The presenter indicated that his students generally enjoyed using the

software and seemed to be actively engaged in completing their projects, thus fulfilling the districts mission

of promoting student-centered, active learning.

The purpose of this particular session was an overview on using the GIS-based software package, and so

more fundamental pedagogical questions were left unasked: How did learning this software package support

the goals of the course (or school district)? Were the students learning some valuable skill or were they

merely learning the mechanics of a neat piece of software? What possible value could plotting the locations

of the water wells in their county be to the students? Was the software being taught because the teacher

enjoyed it or because it kept the students busy (read: active), thus meeting the requirements of a vaguely

stated state standard that required the use of technology in the classroom? Not only were these questions not

answered, but the notion of what instructional goal this digital technology was supposed to serve was never

even broached. The subsequent sessions I attended that day likewise did not attempt to establish a theoretical

or empirical connection between any particular digital instructional technology and instructional goals.

As an educator, I was reminded yet again that the educational process is frequently enacted backwards by

instructional technologists: we find an interesting (read: neat) piece of digital technology and back into a

pedagogical rationale with no overarching model for achieving our educational goals. It would seem that

Bruners lament is as true today as it was in 1966 when he wrote: It is interesting that there is a lack of an

integrating theory in pedagogy, that in its place there is principally a body of maxims (31).

An efficacious use of digital instructional technologies must begin with the selection of an overarching

instructional model, one that is grounded in sound deductive logic and embodies ultimate purpose as well as

instructional strategies.

The Proposed Instructional Strategy: Problem-Based Learning

Imagine two possible pedagogical scenarios for a course called Environmental Problems being taught at an

urban university. In the first scenario, the professor begins with a traditional approach, using a curriculum

guide that lists the topics to be covered, what behaviors (in the form of behavioral objectives) each student

will be expected to demonstrate by the end of the course, how and when students will be tested (usually with

an inauthentic paper and pencil instrument), and what laboratories will be conducted. The course content is

professor-centered (only the professor decides what curriculum to use) and likely varies little from one

semester to the next.

In the second scenario, the professor begins instead with a Problem-Based Learning (PBL) approach.

Although there are many ways to incorporate PBL into a curriculum, a constructivist orientation might use the

first few classes for students to determine what problem they would like to learn more about (in some

courses, students may even be asked to solve the problem). For example, a student may wish to find out why

the incidence of diabetes is so high in a particular part of the city, or if there is a statistically significant link

between houses made using lead-based paint and IQ scores for children. Thus, the curriculum becomes

student-centered, and the professor conducts the rest of the semester using the student-derived problem as

the focal point. Because the problems are student-derived, the course content is relevant and dynamic.

Problem-Based Learning, according to Evensen and Hmelo (2000), is an effective instructional method

because it anchors the learning in concrete, real-life problems, thereby developing the students capacity for

hypothesis-driven reasoning; thus, students are more satisfied with their curriculum because they are working

on a problem that is personally meaningful. In Gagnes influential work, The Conditions of Learning, problem

solving was identified as the highest form of learning because it allows knowledge to be transferred to novel

situations through the formation of new schema. Problem solving results in the acquisition of new ideas that

multiply the applicability of principles previously learned (Gagne, 1965, 57).

Before one accepts the premise, however, that PBL allows students to be more satisfied with their curriculum

or that the ability to solve problems is the highest form of learning, we must ask: is there a reason for the

apparent efficacy of this teaching methodology? The answer, I believe, is yes, and its rooted in the biology

of Homo sapiens, a deductive argument for which will be built upon the work of three great thinkers: Darwin,

Descartes, and Dewey.

Darwin: The Evolution of Our Problem Solving Nature

In his ground-breaking work The Descent of Man, Darwin (1879/2004) states that, Man still bears in his

bodily frame the indelible stamp of his lowly origin (689). The indelible stamp describes, among many

other things, characteristics or dispositions of our species (Homo sapiens) that have been evolving for

millions of years in an environment that was, for much of that period, much different than the one were

living in now. The shaping process of that indelible stamp was natural selection, which works to

homogenize a species into a standard overall design by concentrating the effective genesthe ones that build

well-functioning organsand winnowing out the ineffective ones (Pinker, 2002, 142).

The effective genes (or stamps) that Pinker describes were those that helped our hominid ancestors adapt to

and control their environment, both through physical organs and cognitive abilities: Where animals compete

in strength, humans vie in intellect, and the superior minds win out (Darwin, 1879/2004, xxiv). Geary (2002)

described these cognitive abilities as those that allow the individual to process goal relevant information

(323). Goal relevancy as used by Geary means that as a species we evolved through natural selection to

solve problemsthat is, to control our environmentfor the purpose (read: ultimate cause) of propagating

the species. We have evolved to become problem-solving entities, and the capacity to do so is encoded in our

DNA. Undeniably, the problems we encounter today (predicting local economic conditions) are different than

those encountered by our hominid ancestors (predicting rain). Nonetheless, the stamp of a general problem-

solving disposition still bestows upon modern man a selective advantage ( Keil & Wilson, 1999, xl). Thus, the

forces of natural selection as they relate to the evolution of Homo sapien information-processing and

problem-solving abilities establish a biological basis for a teaching and learning modelProblem-Based

Learningin a modern, technological-based society.

Descartes: Problem Solving as Rationalism

If we accept the idea that the forces of natural selection have endowed modern Homo sapiens with a set of

highly evolved dispositions for problem-solving, we must accept the idea that modern Homo sapiens are not

born with brains that are a blank slate. Rather, we must accept, or at least acknowledge, the so-called

rationalist point of view, which posits that various innate dispositions in the Homo sapien brain are not

predicated upon any form of experience. Classic rationalists like Rene Descartes believed that Homo sapiens

are more than the sum of our empirical interactions with our environment, being endowed with a rational

soul.

However, if we are inclined to reject the rationalist view and accept an empirical (blank slate) notion of the

Homo sapien brain, the proposition that Homo sapiens are problem-solving entities is not negated; the ultimate

end for a blank mind is to learn through association to achieve mastery of the environment. The rationalism

versus empiricism debate becomes moot, as both support the same end: to solve problems for the purpose of

controlling the environment.

Dewey: Problem Solving and the Learning Process

John Dewey, arguably one of the most influential educational theorists of the 19 th century, exhorted

educators to incorporate the interests of the child into the curriculum, making the child the center of the

school (1938/1997). But what constitute the interests of the child? According to Pinker (2002), an

educational theory that addresses the interests of the child must be based on a theory of human nature; thus, a

curriculum based upon teaching students to control or to understand their environment follows naturally from

the premises mentioned above. Smilkstein (2003) eloquently describes this type of a curriculum as brain-

compatible (2), with a scientific basis for knowing how and what to teach. A deep understanding of any

topic can be achieved by using a problem-based (i.e., brain-compatible) curriculum because it is based upon

the premise that our brains have evolved to solve problems for the purpose of controlling our environment.

Discussion: Integrating Problem-Based Learning into the Curriculum

If Homo sapiens have a predisposition toward problem solving hard coded into their DNA, how do we as

educators build a curriculum around PBL? In F igures 1 and 2, I propose two overarching instructional

models that integrate the pedagogical construct of PBL with other well-established learning models. I also

propose how various digital instructional technologies can be used to support a PBL curriculum.

It is beyond the scope of this paper to fully explain each component of the two models, but briefly Figure1

represents a curricular flow, from deciding what the purposes of education are and what type of mind we

are trying to develop (The Tyler Rationale), through what educational experiences (The Learning Cycle and

The Cognitive Apprenticeship) the teacher could use to implement a PBL curriculum. For example, we might

decide that the individual we are trying to develop should be a reflective problem solver (the purpose of

education), and that student-generated problems would form the basis for other instructional models (The

Learning Cycle and The Cognitive Apprenticeship). In such a model, the capacity to solve problems becomes

the goal of the curriculum as well as an instructional strategy.

Figure 2 represents a more thorough explanation of how PBL can be integrated into The Learning Cycle, with

example digital instructional technologies (shown in parenthesis) that would conceptually support or enhance

the curriculum:

Figure 2AStudent Selection of a Relevant Problem: Students can use library-based electronic

databases, like ProQuest or ScienceDirect, to help in the selection and definition of the student-defined

problem. For the more visually-oriented student, virtual field trips might be employed.

Figure 2B: Once the problem has been defined by the student, the problem is fed into The Learning

Cycle; data are collected by directly observing and experiencing the problem using hardware and

software packages like Probeware (used for microcomputer-based chemistry or biology laboratories)

or numerous Web-based data sets.

Figure 2C: Students store data from their observations and experiences in databases like MS Access and

subsequently build intuitive, user-friendly interfaces to the databases. Students can then make sense

of the data by using software that will help them turn data into information; for example, students can

learn to better conceptualize information by using statistical analysis tools like MS Excel, motion

graphics software like Flash, or graphic organizers like Inspiration.

Figure 2D: Solutions to the student-generated problems are evaluated by the professor, fellow students,

or other subject matter experts (SMEs) by means of Web-based electronic portfolios. The SMEs can

be located virtually anywhere in the word, allowing the evaluation to be based upon a high degree of

cultural diversity. The entire Learning Cycle becomes an iterative process; if the solution is deemed

inadequate by the professor or other SME, the student may repeat any phase of the Learning Cycle,

possibly using different digital instructional technologies.

In Figure 2, the digital instructional technologies per se are not the focus of the curriculum; rather, their

selection and subsequent incorporation into the pedagogical models are based upon their ability to enable or to

enhance various components of a PBL curriculum; only those digital instructional technologies that can be

empirically verified as supportive of an instructional goal will be selected. If there is no empirical evidence that

the technology will improve learning, it should not be used. Thus, digital instructional technologies become

cognitive artifacts, which are physical objects made by humans for the purpose of aiding, enhancing, or

improving cognition ( Keil & Wilson, 1999, 126). For example, again referring to Figure 2A, Student

Selection of Relevant Problem, the students use of a library-based electronic database was selected on the

basis of its ability to enable and enhancea problem-based instructional goal.

The Evaluation Phase of the Learning Cycle becomes the ideal time not only to assess the students work, but

also to empirically measure the contribution of the various digital instructional technologies; if a students

solution to a problem is deemed unsatisfactory, the instructor must review the pedagogical model and

supporting digital instructional technologies to see what can be improved. For example, the student may not

have defined the problem very well at the beginning of the Learning Cycle, thus precluding an adequate

solution to the original problem. The instructor must now determine if using an electronic database was the

proper digital instructional technology for framing the problem; could an interview with a remotely located

expert using a Web-cam have been more helpful? The challenge for educators in the Evaluation Phase is to

quantify the contribution of a particular digital instructional technology in achieving an instructional goal;

merely introducing another piece of technology without empirically establishing this causal connection can

only detract from our effectiveness as educators.

A curriculum built upon PBL with the empirically validated incorporation of various digital instructional

technologies would seem to be a natural extension of our brains evolution. It is not my contention, however,

that PBL is the only learning strategy that can take advantage of our brains physiology: a curriculum based

upon the concept of multiple intelligences (as envisioned by Howard Gardner) may be another example of a

brain-based curriculum (Smilkstein, 2003). The pedagogical concept of multiple intelligences would not

supplant PBL it would become a teaching strategy that works in conjunction with a curriculum based upon

PBL.

Conclusions

It was not the purpose of this paper to focus upon an explication of the age-old debate about nature-verses-

nurture; a topic of such great importance will, perhaps, never be solved to the satisfaction of social

scientists, even considering the recent advances in genetics. I presented a deductive argument, based on the

work of three influential thinkers, that Homo sapiens are genetically predisposed to solve problems for the

purpose of controlling their environment. I further argued that the primary goal of educators should be

building a curriculum based on Problem-Based Learning. With PBL established as the epicenter of the

curriculum, I then explained how student-derived problems can be integrated into and made compatible with

other proposed models of effective pedagogy. Finally, I argued that digital instructional technologies should

enable or enhance a curriculum that is based upon pedagogical models that are either theoretically or

empirically well supported (PBL, in this case). Thus, building a curriculum that focuses upon PBL (or other

well-supported models) while employing modern digital instructional technologies to enhance the problem-

solving experience of our students should be our goal as educators.

Yet this appears not to be happening. Our goal seems to be digital instructional technologies for their own

sake, with little or no attention being paid to improving educational curriculum, problem-based or otherwise.

Anecdotal evidence of this tendency is not hard to come by: vendors at instructional technology conferences

promote the use of the latest electronic or Web-based paraphernalia, with scant attention paid to its

incorporation into a pedagogical model; smart whiteboards are left unused in classrooms because

professors do not understand their teaching value; Course Management Systems (CMS) like Blackboard and

WebCT are employed at many universities because they make course delivery more efficient, with virtually no

large-scale scientific research to support their usefulness in promoting a deeper understanding of the subject

(Coates, James, & Baldwin, 2005).

The challenge, according to Smilkstein (2003), is for educators to develop curricula and select

pedagogical strategies that will most effectively help students learn by using their brains innate learning proc

esses (30). Our research efforts, therefore, should focus on empirically measuring what digital instructional

technologies best support or enhance a curriculum based on how the brain actually works. In such an

environment, digital instructional technologies like the Global Information System would be used not to locate

water wells, but to solve problems that are meaningful to the student.

Problem-Based Learning requires a student-centered, democratic learning environment. Many educators are

uncomfortable with such constructs, concerned about losing control of the class or being unable to measure

the outcomes. These concerns can be addressed to a large extent through the use of digital instructional

technologies; as students work on projects that are relevant to controlling or understanding their own

environment through PBL, their motivational levels will increase and classroom behavior will be positively

directed. Further, the use of active feedback and multi-source expert evaluation will allow educators to more

meaningfully and authentically assess student achievement. Thus, as we redefine our role as educators, we

would do well to remember Bruners (1966) admonition:

The will to learn becomes a problem only under specialized circumstances like those of a school, where a

curriculum is set, students confined, and a path fixed. The problem exists not so much in learning itself, but

in the fact that what the school imposes often fails to enlist the natural energies that sustain spontaneous

learningcuriosity, a desire for competence, aspiration to emulate a model, and a deep-sensed commitment

to the web of social reciprocity. (127)

References

Bruner, J. S. (1966). Toward a theory of instruction. New York: W. W. Norton & Company, Inc.

Coates, H., James, R., & Baldwin, G. (2005). A critical examination of the effects of learning management

systems on university teaching and learning. Tertiary Education and Management, 11, 19-36.

Collins, A., Brown, J., & Newman, S. (1990). Cognitive apprenticeship: Teaching the crafts of reading,

writing, and mathematics. In L. B. Resnick (Ed.), Knowing , learning , and instruction : Essays in honor of

Robert Glasser . Hillsdale, NJ: Lawrence Erlbaum & Associates.

Darwin, C. (2004). The descent of man. New York: Penguin Putnam, Inc. (Original work published 1879).

Dewey, J. (1997). Experience and education. New York: Touchstone. (Original work published 1938).

Evensen, D. H. & Hmelo, C. E. (2000). Problem-based learning: A research perspective on learning

interactions. Mahwah, NJ: Lawrence Erlbaum Associates.