Chemical Education Today
Commentary
Introductory Students, Conceptual Understanding, and Algorithmic Success by David B. Pushkin
An article last year by Lin et al. (1) referred to research on conceptual learners in general chemistry. In retrospect, two points need to be addressed: (1) the distinction between conceptual and algorithmic learning, and (2) the clarification of the “second tier student”. Until we can better rationalize these points, we cannot truly understand the education process in chemistry and other physical sciences. Conceptual versus Algorithmic Learning Science education researchers indicate that many novice learners in chemistry (2,3) and physics (4,5) are able to apply algorithms without significant conceptual understanding, a phenomenon independent of major. There are a number of possible reasons for this: 1. Novice learners tend to be more declarative and procedural in their knowledge orientation. By this, I mean that novice learners tend to be very adept with arbitrary facts and generalized algorithms. Rarely do novices think in terms of integrated or applied knowledge. 2. Novice learners tend to be very dualistic in their thinking regarding their role in the education process (6). To illustrate, I refer to the first two stages of William Perry’s theory of adult cognitive development (7). Dualistic learners are very submissive in accepting what they are told by their instructors as unquestioned knowledge. Multiplistic learners will still accept the words of their instructors but only under testing conditions. When a grade is not at stake, such thinking reverts to whatever learners believe regardless of the of the instructor’s viewpoint. 3. Novice learners are subjected to science curricula and pedagogy that discourage critical and conceptual thinking (8-10). 4. Those who teach introductory chemistry and physics place more value on algorithmic learning than on conceptual learning, giving learners the impression that science is “math in disguise”.
What essentially distinguishes conceptual learners from algorithmic ones is that the former are more advanced and less dualistic in their thinking, more experienced in problem solving, more situational in their knowledge orientation, and more verbal in their reasoning (6, 8, 11). By no means are these dichotomous modes of thinking; conceptual learners are clearly at the more evolved end of the spectrum of cognitive development. Conceptual learners are rarely unable to be algorithmic. Perhaps this is because “conceptual problem– solving ability” (3) is a misnomer; science education researchers often refer to “problems” in a quantitative context (4). It is important to clarify items of conceptual thinking assessment as items of qualitative understanding. Does this mean that such assessment items fail to encourage critical thinking? Hardly; these items have much more potential to pro-
mote critical thinking than multi–step “plug–and–chug” problems. Algorithmic learners can master assessment items requiring mimicking, regurgitation, and short–term memorization. They cannot, however, master assessment items requiring evaluation, comparison, and attribution skills. Such assessment items would require long–term cognitive development where knowledge is genuinely stored, structured, and networked. Conceptual learners can master these types of items. They are capable of probing information and explaining the underlying reasons for their observations and conclusions regarding scientific phenomena. They are capable of recognizing characteristics in novel situations and applying relevant prior knowledge. This happens primarily because conceptual learners evolve over a period of time from their learning experiences; their understanding is a manifestation of collected knowledge, not immediate knowledge. Conceptual learning is an evolution beyond fundamental competence. We can foster conceptual learning by providing students a variety of learning experiences and assessment items. A broad scope of exposure does not necessarily take away from the development of algorithmic skills; it can actually enhance and strengthen those skills. If we wish to encourage students to develop strong qualitative and quantitative thinking skills (i.e., conceptual and algorithmic), we should provide opportunities to demonstrate both. For example, why not ask students to explain their reasoning for solving a stoichiometry problem? Granted, there will be students who can qualitatively explain but not calculate well. However, there will also be students who can calculate without the slightest clue as to why they are doing so, as well as students who can calculate and reasonably explain. As chemistry and physics educators, we would be surprised by how many students through the years hated tests that forced them to not use numbers and algorithms exclusively. However, when so many science departments place students according to their math placement tests—not to mention SAT math or ACS test scores—it is no surprise that introductory students walk away from courses with little if any conceptual understanding (8). Traditional assessment is focused too much on “nuts–and–bolts” content and too little on “big picture” comprehension. The “Second Tier Student” It is my understanding from Sheila Tobias’s writings (9,10,12) that “second tier” students are capable of understanding and succeeding in science, but their experiences in science and math courses have been unsatisfactory. As a result, these students either take the minimal science and math requirements of their degree programs, or they avoid science and math altogether. It is not a matter of whether they are conceptual or algorithmic learners; they are turned off by sci-
JChemEd.chem.wisc.edu • Vol. 75 No. 7 July 1998 • Journal of Chemical Education
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Chemical Education Today
ence and math for two of the same reasons stated earlier: (1) Novice learners are subjected to science curricula and pedagogy that discourage critical and conceptual thinking; and (2) those who teach introductory chemistry and physics place more value on algorithmic learning than on conceptual learning, thus giving learners the impression that science is “math in disguise”. The sad truth is that many science instructors fail to stimulate students in introductory courses. Perhaps it is due to these instructors’ attitudes towards introductory courses or nonmajors versus majors (13,14). Perhaps it is due to their unresolved epistemologies regarding teaching and learning (8,15). If instructors fail to share the joy of science with students and project instead the themes of obedience and drudgery, students will be turned off, regardless of major and ability level. In my mind, “second tier” students need to have both the qualitative and quantitative aspects of science in order to appreciate science and possibly develop a career interest in it. The “first tier” students are those who master an apprenticeship. Granted, they are the best students grade–wise, but they may not be the best future scientists. Why do I say this? Students who master an apprenticeship neither learn to think independently nor contingently; they are future professionals needing to be led, as opposed to leading themselves (6,8). It is misleading to assume “second tier” students are incapable of being algorithmically successful; these students are cognitively intolerant of being exclusively algorithmic. In a sense, “first tier” and “second tier” students are more a consequence of our curricula and pedagogy than their abilities or career interests.
suspect. Studies focusing on the cognitive development of minority students are to be admired, respected, and encouraged; they are long overdue. However, we need to have strong theoretical frameworks to guide our studies. Minorities are not necessarily a unique variable. They happen to be within a larger context as learners of science. Learner–focused studies can be quite influential in the science and education communities, assuming the research scope is sufficiently broad and contextual.
Final Thoughts
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Although specific data are still being collected (and I do teach a significant number of minority students), I would find it difficult to believe that minority students are more conceptual than algorithmic, since being a novice learner should transcend race, ethnicity, or gender. There is considerable literature regarding novice learners in the physical sciences, research that should be redirected to address learners in terms of experience and familiarity with science concepts and problem–solving. The foundation for research should not be how “good” students compare to “poor” ones; considering common modes of assessment this is quite subjective and
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Literature Cited 1. 2. 3. 4. 5. 6.
7. 8.
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11. 12. 13. 14. 15.
Lin, Q.; Kirsch, P.; Turner, R. J. Chem. Educ. 1996, 73, 1003. Nakhleh, M. B. J. Chem. Educ. 1993, 70, 52. Nakhleh, M. B.; Mitchell, R. C. J. Chem. Educ. 1993, 70, 190. Maloney, D. P. Handbook of Research on Science Teaching and Learning; Gabel, D. L., Ed.; MacMillan: New York, 1994; pp 327–354. McMillan, C.; Swadener, M. J. Res. Sci. Teach. 1991, 28, 661. Pushkin, D. B. Teachers Says; Simon Says—Dualism in Science Learning. On Our Own Recognizance: Students and Teachers Creating Knowledge; Steinberg, S., Kincheloe, J., Eds.; Routledge Publishers: New York, 1998; in press. Perry, W. G. Forms of Intellectual and Ethical Development in the College Years, A Scheme; Holt, Rinehart, and Winston: New York, 1970. Pushkin, D. B. Post-Formal Thinking and Science Education: How and Why Do We Understand Concepts and Solve Problems? Post-Formal Thinking: Questioning Educational Psychology and the Education it Supports; Kincheloe, J., Ed.; Garland Publishers: New York, 1998; in press. Tobias, S. Revitalizing Undergraduate Science: Why Some Things Work and Most Don’t; Research Corporation: Tucson, AZ, 1992. Tobias, S.; Tomizuka, C. T. Breaking the Science Barrier: How to Explore and Understand the Sciences; The College Board: New York, 1992. Pushkin, D. B. J. Coll. Sci. Teach. 1997, 26, 238. Tobias, S. They’re Not Dumb, They’re Different: Stalking the Second Tier; Research Corporation: Tucson, AZ, 1990. Hoogstraten, C. Chem. Eng. News 1996, 74 (33), 66. Babcock, G. T. Chem. Eng. News 1996, 74 (40), 7. Pushkin, D. B. Chem. Eng. News 1996, 74 (40), 7.
Dave Pushkin teaches in the Department of Chemistry and Biochemistry, Montclair State University, Upper Montclair, NJ 07043; phone: 973/655-7118; email: pushkin@pegasus. montclair.edu
Journal of Chemical Education • Vol. 75 No. 7 July 1998 • JChemEd.chem.wisc.edu
