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  3. Acknowledgements

There is a growing acknowledgement that STEM education, at all levels, is not producing learners with a deep understanding of core disciplinary concepts [1]. A number of efforts in STEM education reform have focused on the development of “student-centered” active learning environments, which, while believed to be more effective, have yet to be widely adopted [2]. What has not been nearly as carefully considered, however, is the role of the curriculum itself as perhaps the most persistent obstacle to effective science education. It is now time to examine not only how we teach, but also what we teach and how it affects student learning.

Scientific understanding is difficult to master. It took humanity, as a community, many millions of minds, and thousands of years to formulate and establish foundational scientific concepts. Critical insights (atoms, gravity, quanta, genes, and selection), while often first enunciated by “geniuses,” make sense only within the context of a scientific community. An effective curriculum must develop core scientific ideas in a coherent and understandable way. This requires a different approach to curriculum development than the current piecemeal approach where chunks of seemingly disconnected ideas are presented in separate “chapters” or courses, often without explicitly connecting ideas that in fact depend upon one another. The common practice of juggling the order of topics—and even courses—makes little sense in the light of current research on how concepts are gradually built to form a “learning progression,” a process that is becoming increasingly common in the development of K-12 curricula [3], although there is, as yet, little attention being paid to them at the college level.

There is compelling evidence that it takes years of “deliberate practice” to become a disciplinary expert [4]. While the goal of science education is not for students to achieve “disciplinary expertise,” it is (or should be) to help students attain a robust understanding of core ideas. This more modest goal is itself quite ambitious. Studies of how experts move toward expertise indicate that well-informed coaches are critical. An important part of what these coaches do is to develop a navigable path, based on the student's current level of mastery. “If a coach pushes you too fast, too hard, you will only be frustrated and may even be tempted to give up trying to improve at all.” [5] This idea is mirrored in learning theories such as Vygotsky's concept of the zone of proximal development [6], the region of new ideas and skills that are accessible to the student, based on their current assumptions, knowledge, and thinking. This concept is illustrated quite nicely in Richard Feynman's discussion of magnetic forces [7]. To coach (i.e., teach) effectively, teachers need to understand where the student is, what are they assuming, and how they move from idea to idea. An effective teacher then needs to understand what it takes to “build out” from a student's zone of proximal development. Our goal as teachers, and course and curriculum designers, is therefore to plot an effective conceptual progression from where a student is, to where we would like them to end up.

Recognizing how challenging it is to build scientific understanding requires that we recognize that scientific thinking, in and of itself, is by no means easy and is certainly not “natural.” We are programmed by survival-based and eminently practical evolutionary processes to “think fast” [8]. In contrast, scientific thinking is slow, hard, and difficult to maintain. If students are not exposed to environments where they must practice and use the skills (both metacognitive and procedural) that they need to learn, they may fall back on fast, surface level answers, and fail to recognize what it is that they do not understand.

At the same time, as we try to nurture a scientific way of thinking in our students, we need to provide the motivation that justifies the hard work that real subject mastery requires. Here, we are addressing a nonscientific (and essentially noneducational) component of the science education system—its role in “sorting students.” There is often the implicit acceptance that an important goal of introductory science courses is to weed out the unqualified and unmotivated [9]. If materials are presented without regard to, what students are able to learn they will, very likely, become frustrated and tempted to give up [10]. Therefore, when designing a course, we need to think seriously, of what it is we expect students to master; if it is not within their “zone of proximal development,” we must alter our curriculum to give them a path to mastery.

This raises an important point. How do we determine exactly what it is that we expect students to master? This is more difficult than it would appear on the surface, because the complexity of scientific ideas and the unnaturalness of scientific thinking can obscure, even to us, what is to be learned. What is critical to a molecular level understanding of biological systems and what is distracting detail? We recommend that course designers take the time to explicitly describe, and in so doing, analyze their goals [11, 12]. This is a process that goes beyond often ambiguous “learning goals”; it requires the development of both explicit knowledge statements and performance expectations. Knowledge statements describe what students should know, and performance expectations describe what students should be able to do with that knowledge—what it looks like when students understand. This approach is being taken for K-12 curriculum development by the Next Generation Science Standards.1

We have taken this approach to content in two introductory courses: Biofundamentals,2 an “alternative” approach to the major's introduction to molecular biology course, and Chemistry, Life, the Universe, and Everything (CLUE),3 an NSF funded project to provide a coherent and comprehensive treatment of foundational ideas in chemistry. We are investigating how these “intelligently designed” courses affect students understanding compared to cohorts of similar students who were taught using more traditional curricular materials that do not use a learning progression [13], but as in all educational projects, the proof is in the pudding. Clearly, our own studies will only be believable when others, not connected with the projects use and assess these materials.


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  3. Acknowledgements

The authors greatly appreciate the supportive environment provided by our colleagues at UC Boulder, Clemson, and across the country.


  1. Top of page
  3. Acknowledgements
  • 1
    PCAST ( 2011) Prepare and inspire: K-12 education in science: Technology, Engineering, and Math (STEM) for America's Future, President's Council of Advisors on Science and Technology, Washington, D.C.
  • 2
    C. Henderson, M. Dancy( 2009) The impact of physics education research on the teaching of introductory quantitative physics in the United States. Phys. Rev. ST Phys. Educ. Res. 5, 020107.
  • 3
    C. V. Schwarz, B. J. Reiser, E. A. Davis, L. Kenyon, A. Acher, D. Fortus, Y. Shwartz, B. Hug, J. Krajcik ( 2009) Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. J. Res. Sci. Teach. 46, 632654.
  • 4
    N. Charness, P. J. Feltovich, R. R. Hoffman ( 2006) The Cambridge Handbook of Expertise and Expert Performance, K. A. Ericsson, editor, Cambridge Handbooks in Psychology, University of Cambridge Press, Cambridge.
  • 5
    K. A. Ericsson, M. J. Prietula, E. T. Cokely ( 2007) The making of an expert. Harv Bus Rev 85, 114121, 193.
  • 6
    L. S. Vygotsky ( 1978) Mind and society: The development of higher psychological processes, Harvard University Press, Cambridge, Massachusetts.
  • 7
    R. Krulwich ( 2012) Great Teacher, Short Question, Wild Answer, Available at: Accessed on 28 March 2012.
  • 8
    D. Kahneman ( 2011) Thinking fast and slow, Farrar, Straus and Giroux, New York.
  • 9
    J. Mervis ( 2011) Undergraduate science. Weed-out courses hamper diversity. Science 334, 1333.
  • 10
    J. Mervis ( 2010) Undergraduate science. Better intro courses seen as key to reducing attrition of STEM majors. Science 330, 306.
  • 11
    M. W. Klymkowsky ( 2010) Thinking about the conceptual foundations of the biological sciences. CBE Life Science Educ 9, 405407.
  • 12
    M. W. Klymkowsky ( 2011) Getting serious about science education. ASBMB Today, 1617.
  • 13
    M. M. Cooper, S. M. Underwood, C. Hilley, M. W. Klymkowsky (in press) Development and assessment of a molecular structure and properties learning progression. J. Chem Educ. in press.