Formal reasoning ability and misconceptions concerning genetics and natural selection

JOURNAL OF RESEARCH IN SCIENCE TEACHING VOL. 25, NO. 9, PP. 733-746 (1988)

FORMAL REASONING ABILITY AND MISCONCEPTIONS CONCERNING GENETICS AND NATURAL SELECTION

ANTON E. LAWSON

Department of Zoology, Arizona State University, Tempe, Arizona 85287

LOIS D. THOMPSON

Chaparral High School, Scottsdale, Arizona 85254

Abstract

Students often hold misconceptions a b u t natural phenomena. To overcome misconceptions students must become aware of the scientific conceptions, the evidence that bears on the validity of their misconceptions and the scientific conceptions, and they must be able to generate the logical relationships among the evidence and alternative conceptions. Because formal operational reasoning patterns are necessary to generate these logical relationships, it was predicted that, following instruction, formal operational students would hold significantly fewer misconceptions than their concrete operational classmates. To test this hypothesis 13 1 seventh-grade students were administered an essay test on principles of genetics and natural selection following instruction. Responses were categorized in terms of the number of misconceptions present. The number of misconceptions was compared to reasoning ability (concrete, transitional, formal), mental capacity (<6, 6, 7), verbal intelligence (low, medium, high), and cognitive style (field dependent, intermediate, field independent). The only student variable consistently and significantly related to the number of misconceptions was reasoning ability; thus, support for the major hypothesis of the study was obtained.

Introduction

The primary purpose of this study was to allow an initial test of the hypothesis that formal reasoning ability is necessary for seventh-grade students to overcome naive prior misconceptions and develop valid biological conceptions concerning genetics and natural selection. The role of student mental capacity, verbal intelligence, and cognitive style were also explored.

Research has documented that many students come to class holding numerous misconceptions. Misconceptions are defined as knowledge spontaneously derived from extensive personal experience that is incompatible with established scientific theory (e.g., Driver, 1981, 1983; Posner, Strike, Hewson, & Gertzog, 1982; Halloun

0 1988 by the National Association for Research in Science Teaching Published by John Wiley & Sons, Inc. CCC 0022-4308/88/09733-15$04.00

134 LAWSON AND THOMPSON

& Hestenes, 1985a, 1985b). Importantly, these misconceptions are not viewed as simply minor misunderstandings or trivial gaps in knowledge that students may have forgotten. Rather they are allegedly embedded in “highly robust” alternative conceptual frameworks for the interpretation of natural events, many of which were seriously advocated by leading intellectuals of the past (cf. Halloun & Hestenes, 1985b; Viennot, 1979).

What factors allow some students to overcome these misconceptions and develop scientifically valid conceptions while others do not? The following four factors can be suggested:

(1) Formal reasoning ability. Parmenedes, the ancient Greek philosopher, stated, “The senses deceive us. ” He believed that one can only reach truth through reason. Personal experience provides the basis for knowledge that is inaccurate (e.g., optical illusions, Piagetian nonconversation responses). Leading naturalists of the past advocated ideas such as spontaneous generation, special creation, and the inheritance of acquired characteristics. These ideas have their roots in personal experience. Maggots appear to be spontaneously generated from rotting flesh; people create objects so living objects must also be created by “people” (with special God-like properties); children look like their parents so changes in the appearance of the parent will cause a change in the appearance of an as-yet-unborn child.

The rejection of these ideas during the history of biology required the generation of alternative hypotheses and their test through logical reasoning, experimentation, data collection, and considerable argumentation. Biologists who became aware of these alternative ideas (e.g . , evolution, natural selection, genetics) and the available evidence and were able to follow the lines of reasoning used to argue their case, were generally convinced and were able to overcome prior “misconceptions” in favor of the more scientifically accurate conceptions.

By analogy we can hypothesize that the same thing happens in the science classroom. For students to overcome their misconceptions they must (a) become aware of the scientific conception, (b) they must become aware of the evidence that bears on the validity of the alternative conceptions, and (c) they must be able to generate and/or follow a discussion of the logical relationships among the alternative conceptions and the evidence. In other words, they must be able to logically “see” how the evidence supports the scientific conceptions and contradicts the naive misconception (cf. Posner, Strike, Hewson, & Gertzog, 1982). Because formal reasoning patterns are precisely those used to evaluate alternative conceptions in a logical hypothetico-deductive manner (e. g., combinational reasoning, control of variables, probabilistic, and correlational reasoning), we predict that concrete operational students who do not use these patterns effectively will fail to overcome their ndive misconceptions. More precisely, formal operational students are predicted to hold significantly fewer misconceptions than concrete operational students following instruction.

Table I depicts the study’s central working hypothesis. The left-hand column indicates that both concrete and formal operational students bring to class naive concepts derived from personal experience. For example, students may have heard news reports that a mother’s drug addiction or acquisition of the AIDS virus is passed on to the newborn, hence drawing the conclusion that all environmentally induced changes will be passed on. The next column in Table I depicts the effect of instruction, which is to introduce the scientific conception (in this case ideas of

FORMAL REASONING ABILITY 735

TABLE I Hypothesized Interaction between Science Instruction and Ndive Misconceptions of Concrete

and Formal Operational Students

Before After Instruction Instruction Instruction End Product

Concrete Students

naive concept --c naive concept ______p naive concept

scientific scientific concept * concept

Formal Students

naive concept naive concept +naive concept

__cI scientific t . c

scientific -scientific concept concept concept

genetics and natural selection that contradict the idea of the inheritance of acquired characteristics).

Up to this point the concrete and formal students are no different. In the next column however-the after-instruction column-the students differ considerably. What we are hypothesizing is that concrete operational students lack skill in using the reasoning patterns necessary to evaluate potentially competing hypotheses (e. g. , the hypothesis of the inheritance of acquired characteristics versus the natural selection of genetically acquired characteristics); therefore, they will not “self-regulate” and will not reject the naive concept. Rather the end product is to retain two contradictory notions that the concrete student, by and large, fails to recognize as contradictory. Which of the two hypotheses he or she invokes in specific situations will be largely unpredictable and based upon irrelevant problem cues.

In contrast with the concrete student, the formal student, having previously acquired skillful use of the reasoning patterns and mental habit necessary to reflect on the relative merits and demerits of competing hypotheses, will mentally compare deductions derived from each hypothesis with available evidence and reject the naive conception in favor of the scientific one.

Both of the previously described scenarios differ considerably from the model shown in Table 11. Here students are believed to arrive in class as blank slates (tabula rasas), and instruction is seen simply as a matter of providing students with the scientific conceptions.

(2) Mental capacity. Mental capacity, or working memory capacity (Pascual- Leone & Smith, 1969) theoretically varies in students and may contribute to students’ failure to grasp scientific conceptions because comprehension of scientific conceptions frequently requires the coordination of a relatively large number of independent


TABLE 11 A Blank-Slate Model of Student Concept Acquisition


no concept scientific concept - scientific concept pieces of information. Thus a limited mental capacity may contribute to the retention of misconceptions (e.g., Lawson, 1983).

(3) Verbal intelligence. Although it is not theoretically clear what is meant by the term verbal intelligence, it is clear that science classrooms and textbooks require the processing of a considerable amount of verbally presented information. Verbal intelligence, defined as a measure of one’s ability to recognize analogies in verbally presented material (Shipley , 1940), can be expected to correlate positively with comprehension of verbally presented scientific concepts; thus low verbal intelligence could be expected to accompany the retention of misconceptions.

(4) Cognitive style (field dependence/field independence). The cognitive style of field dependence/field independence reflects one’s ability to disembed relevant spatial patterns from complex and potentially confusing contexts (Witkin, Moore, Goodenough, & Cox, 1977). Although spatial patterns are utilized in measurement of field dependencehndependence, the cognitive style has been found to correlate with verbal disembedding as well, and therefore may correlate with the number of misconceptions students hold. The prediction is that field-independent students, because they are less likely to be confused by misleading contextual information, will hold fewer misconceptions than field-dependent students.

Factor 1, formal reasoning ability, can be considered to be the central working hypothesis of the present study while factors 2-4 can be considered to be alternative hypotheses.

Method

Subjects

Subjects (Ss) were 131 seventh-grade students (67 males and 64 females), ages 12.3 years to 14.1 years, mean age = 13.1 years, enrolled in five sections (all taught by the same teacher) of a required life-science course at a modern public junior high school located in a suburban middle- to upper-class community near Phoenix, Arizona. The students were typical of seventh-grade students in the school district and were selected primarily for that reason.

Pretesting. The students were pretested to determine their (1) ability to reason in a formal hypothetico-deductive mode, i. e., their level of intellectual development (concrete operational, transitional, formal operational); (2) mental capacity, i.e., number of independent units of information they can simultaneously operate on in


working memory (1-7 units); (3) verbal intelligence, i.e., their ability to recognize verbal analogies (low, medium, high); and (4) cognitive style of field dependence/ field independence, i.e., their ability to disembed simple visual patterns from complex backgrounds (field dependent, intermediate, field independent).

Instruction. Students were then taught units on evolution and genetics by their classroom teacher based upon a standard lecture-textbook approach; i.e., class lectures/discussions were followed by textbook readings and end-of-chapter study questions with an occasional student activity (Heimler & Lockard, 1977). Instruction lasted for approximately one month and included the following topics: fossils, life in the past, the theory of evolution, natural selection, descent with change, mutations, reproduction, dominant and recessive traits, Mendel’s laws, blending, gene theory, reduction division, and sex determination.

Posttesting. At the conclusion of instruction students were posttested using a series of open-ended essay questions that called for the prediction and explanation of biological phenomena involving principles of genetics and natural selection. Responses were then evaluated and scored based upon the number of misconceptions contained. The relationships between the predictor variables and number of student misconceptions were then determined using standard data-analysis techniques including the computation of product-moment correlation coefficients, multiple regression analyses, and analyses of variance.

Predictor Variables

Formal Reasoning Ability. Ss’ formal reasoning ability (developmental level), was assessed by use of a slightly modified version of the Lawson Classroom Test of Formal Reasoning (Lawson, 1978). The modified test includes 11 items requiring Ss to isolate and control variables and use proportional, probabilistic, and conservation reasoning.

Each test item involves a demonstration used to pose a question or call for a prediction. Ss responded in writing in individual test booklets that contained only the questions followed by a number of possible answers or space to generate an answer. Ss were instructed to respond by checking the box next to the answer they thought correct and to explain why they chose that answer. Correct responses and explanations were awarded two points for a possible total of 22 points.

Scores on the test can be treated as a continuous distribution or grouped as follows: 0-6 = concrete operational reasoning; 7- 13 = transitional reasoning; 14-22 = formal operational reasoning. The split-half reliability of the modified test with the present sample was 0.68.

Mental Capacity. The Figural Intersection Test (FIT) (Burtis & Pascual-Leone, 1974) was used to assess mental capacity. The FIT is a group test consisting of 42 items. For each item the S must place a point marking the intersection of from two to eight overlapping figures. An item with eight overlapping figures theoretically requires a mental capacity of seven for successful completion. An item with seven overlapping figures requires a mental capacity of six and so on. Scoring procedures are detailed in Bereiter and Scardamalia (1979). Split-half reliability of the FIT was 0.83.

Verbal Intelligence. The verbal (abstractions) section of the Shipley Hartford Intelligence Scale was used to measure verbal intelligence (Shipley, 1940). The


section consists of 20 word series completion items administered within a ten- minute time limit. Items range in degree of difficulty (e.g., (a) white-black short- long down-- ; (b) tar-pitch-throw saloon-bar-rod fee-tip-end plank---meals). An S’s score on the section is simply the number of items correctly completed. Split-half reliability of the test was 0.75.

Cognitive Style (Field DependencelField Independence). Ss’ ability to disembed relevant information from irrelevant background was assessed by means of the Gottschaldt Figures Test (Crutchfield, Woodworth, & Albrecht, 1958). The GFT is a timed test in which the S’s task is to locate and outline simple figures concealed in 20 complex ones. Split-half reliability of the GFT was 0.67.

Dependent Variable

Number of Biological Misconceptions. The number of biological misconceptions students held with regard to principles of genetics and natural selection was assessed using a series of three open-ended essay questions that called for predictions and explanations of three related biological phenomena. The questions (after Brumby , 1984) were as follows:

(1) Skin Color (a) If the fair-skinned and fair-haired girl shown in the picture grew up in

Africa, what would you predict would happen to the color of her skin? (b) If she married someone of her own race and they lived in Africa and

had children born in Africa, what would you predict their children’s skin would look like at birth?

(c) Please explain your prediction.

( 2 ) Amputated Finger (a) If this little girl had an accident and her finger was amputated and she

married someone with a similar amputation, what would you predict their children’s fingers would look like at birth?

(b) Please explain your prediction.

(3) DyedHair (a)

(b)

(c) Please explain your prediction.

If this fair-haired girl dyed her hair throughout her life, what would you predict would happen to the natural color of her hair? If she married a fair-haired man who also dyed his hair, what would you predict their children’s hair would look like at birth?

S’s responses were scored independently by both authors in terms of the number of times predictions and explanations were based upon misconceptions of the phenomena in question. Interrater reliability was high, with the few disagreements re- solved by discussion. For purposes of comparison, correct responses follow:

(1) (a) Her skin would most likely become darker (i.e., tanned due to increased exposure to the sun).

(b) The children’s skin at birth should be fair. (c) Skin color at birth is determined by the combined genetic

contribution of both parents, which in this case produces a fair- skin trait.


The fingers should be normal (i.e., not shortened). The finger sizelshape etc. at birth is determined by genes contained in the sex cells (i.e., sperm and egg) passed from parent to offspring during conception, therefore, any loss of finger cells and their genes will have no effect on the offspring.

The natural color of her hair would probably not change due to the dye; that is, unless the natural hair had become gray with advancing age. The children’s hair would be fair. As in the previous situations hair color at birth or natural hair color is determined by a combination of parental genes that are not influenced by traits environmentally acquired by the parents.

Incorrect responses, that is, responses that contained misconceptions, generally were based upon the misconception that characteristics at birth are determined by the parents’ environmentally acquired characteristics. In other words, many students seemed to hold a Lamarckian view of the inheritance of acquired characteristics. Other misconceptions were less systematic and more difficult to characterize but manifested themselves in unusual, nonsensical use of terms such as carrier, sex linkage, dominant, recessive, and adaptation.

Below are examples of responses that were considered to be based upon misconceptions:

(1) Skin Color

“As generations pass on, the offspring will become a little darker each time because of adaptation.”

“Probably somewhat darker because the mother’s chromosomes have adapted .” “It’s not a sex-linked trait. Their skin would be dark because both of their parent’s skin is dark.”

“Both parents’ skin was dark and it will probably be passed on.”

“A little darker, but just because of the temperature.”

“In Africa the sun shines hotter and people’s skin gets much darker, so when children are born their skin would be a little darker than normal.”

(2) Amputated Finger

“The finger was cut off too fast for the genes to change.”

“If there is a change in the bodily functions it is likely to be passed on. ”

“The child will probably have a finger missing because the traits of both parents are strong.”

“Well, if parents had amputated fingers their children would have the same problem with their finger ’cause of the traits.”

“Some of the cells would be gone from both parents so the offspring would have less cells.”


“Not normal because there were no traits for fingers to give to the offspring.”

“The fingers would be short because she is not a carrier.”

“The children would have missed one finger because the parent would miss that cell and it can’t reproduce.”

“The lost finger would be inherited from the parents.”

(3) Dyed Hair

“The recessive trait is the color they dyed the hair so it wouldn’t be these anymore. The kid’s hair would be a medium color.”

“The hair was dyed constantly so the genes had to change.”

“The dye would set in the hair and might cause a mutation and the children’s hair would be darker.”

“As the original color was changing, so was the gene for hair color. But since it changes so slowly, it would not quite be the dyed color.”

“Her hair color would be dark. Their children would have dark-haired skin. Their children are dominant.”

Results

Scores on the Classroom Test of Formal Reasoning ranged from 0 to 20, ST = 9.8. Twenty-seven Ss (21%) scored from 0-6 and were classified as concrete operational; 83 Ss (63%) scored from 7-13 and were classified as transitional, while 21 Ss (16%) scored from 14-22 and were classified as formal operational.

Mental-capacity estimates based upon application of the Bereiter and Scardamalia (1979) procedures to responses on the Figural Intersection Test resulted in the following: 47 Ss (36%) = capacity of 7; 51 Ss (39%) = capacity of 6; 14 Ss (11%) = capacity of 5; 8 Ss (6%) = capacity of 4; 2 Ss (1%) = capacity of 3. Responses of the remaining nine Ss (7%) indicated that they failed to adequately understand the test directions; therefore, their responses were not evaluated.

Scores on the Verbal Section of Shipley Hartford Intelligence Scale ranged from 0 to 14 (total possible = 20), x = 9.5. Ss were classified as having high, medium, or low levels of verbal intelligence by assigning scores of 0-6 to the low category (16 Ss = 12%), scores of 7 to 10 to the medium category (63 Ss = 48%), and scores of 1 1 to 14 to the high category (52 Ss = 40%).

Scores on the Gottschaldt Figures test of field dependence/field independence ranged from 0 to 16 (total possible = 20), x = 49. Ss were classified into field- dependent, intermediate, and field-independent categories based upon the following criteria: 0-5 = field dependence (85 Ss = 65%); 6- 1 1 = intermediate (38 Ss = 29%); 12-16 = field independence (8 Ss = 6%).

A total of 128 student responses were considered to represent biological misconceptions on the essay test administered following classroom instruction. This represents approximately one misconception per student (128/13 1 = 0.98). Item 3. Dyed hair had 45% of the misconceptions followed by Item 1 . Skin color with 38%, followed by item 2, amputated finger, with 17%.

Of the 21 formal-operational Ss, 14 (66.7%) had no misconceptions, five (23.8%) had one misconception, and two (9.5%) had two misconceptions. Of the

FORMAL REASONING ABILITY 74 1

83 transitional Ss , 35 (42.2%) had no misconceptions, 32 (38.6%) had one misconceptions, 9 (10.8%) had two misconceptions, and 7 (8.4%) had three misconceptions. Of the 27 concrete-operational Ss, 2 (7.4%) had no misconceptions, 10 (37.0%) had one misconception, 11 (40.7%) had two misconceptions, and 4 (14.9%) had three misconceptions.

Intercorrelations among Predictor Variables and Number of Misconceptions

The raw scores of Ss on the four predictor variables and the single dependent variable-number of misconceptions- were used to compute Pearson product- moment correlations for pairs of variables. The results of those computations are shown in Table 111. The predictor variables show moderate positive intercorrelations indicating some interdependence; r = 0.18 ( p < 0.05) between form reasoning ability and mental capacity to r = 0.43 ( p < 0.001) between formal reasoning ability and verbal intelligence. Only two of the predictor variables showed a significant relationship to the dependent variable. Formal reasoning ability showed a moderate and highly significant inverse relationship with number of misconceptions r = -0.41 ( p < 0.001), while mental capacity showed a low but still significant ( p < 0.05) inverse relationship (r = -0.17). The negative coefficients indicate that better reasoning ability and larger mental capacity corresponded with fewer misconceptions.

Multiple Regression Analysis

Stepwise multiple regression analysis was used to determine which of the not totally independent predictor variables was the most important in predicting number of misconceptions, i.e., which variable or variables make the greatest unique con-

TABLE IU Intercorrelation Matrix for Predictor Variables and Number of Misconceptions

Variable Number and Name 1. 2. 3. 4 . 5

Predictor Variables

1. Formal Reasoning Ability 1.00 ,21** .43*** .36*** - .41***

2. Mental Capacity 1.00 .18* .42*** - . 1 7 *

3. Verbal Intelligence 1.00 .32*** 0.03

4 . Cognitive Style 1.00 - .06

DeDendent Variable

5. Number of Misconceptions 1.00

* p < 0.05. ** p < 0.01. *** p < 0.001.


TABLE IV Stepwise-Multiple Regression Summary for the Prediction of Number

of Biological Misconceptions

Analysis of Variance

R = . 42 DF Sum of Squares Mean Square

R2 = .18 Regression 1 2 0 . 6 6 2 0 . 6 6

Residual 1 2 0 9 7 . 3 1 .81

F = 2 5 . 4 8 , p < ,001

Misconceptions = 2.42 - 0.12 (FRA) - 0.02 (MC) + 0.05 (VI) + 0.05 (CS). FRA = formal reasoning ability, MC = mental capacity, VI = verbal intelligence, CS = cognitive style.

tribution to predicting number of misconceptions. Two separate analyses were performed. The first was a stepwise with no forced entry of predictor variables. Results of this analysis are summarized in Table IV. The only predictor variable to show a statistically significant ( p < 0.05) correlation with number of misconceptions was formal reasoning ability, which uniquely accounted for 18% of the misconception score variance (R = 0.42, R2 = 0.18, F = 25.48, p < 0.001). With forced entry of all the predictor variables R increased to 0.47, R2 = 0.22 (i.e., 22% of the variance accounted for); yet the F value decreased, F = 8.46, p < 0.001.

Analysis of Variance

Figure 1 summarizes the results of four separate analyses of variance, one for each predictor variable with Ss classified into various groups as described previously

, 2.007 vi

1.75 G fi 1.50

Z+ 1.25

‘“a 1.00

g m 0.75

0.50 m 5 0.25

t

0

El5 =z

Z

o’oo C T F (6 6 7 L M H F D M F I REASONING MENTAL VERBAL COGNITIVE

ABlLlM CAPACITY INTELLIGENCE STYLE

Fig. 1. Number of misconceptions per student within each category of student for each of the predictor variables. C = concrete operational, T = transitional, F = formal operational; <6, 6, and 7 represent mental capacity estimates; L = low verbal intelligence, M = intermediate verbal intelligence, H = high verbal intelligence; FD = field dependent, M = field intermediate, FZ = field independent.


(e.g., concrete, transitional, formal). Group differences in number of misconceptions reached significance ( p < 0.05) for only one variable-formal reasoning ability. As shown, the concrete Ss had an average of 1.67 misconceptions per student, the transitional Ss had an average of 0.89 misconception per student, and the formal Ss had an average of only 0.75 misconception per student (F2, 12? = 12.19, p < 0.OOOl). Mental-capacity group differences were M < 6 = 1.39, M of 6 = 0.86, M of 7 = 0.91 (F4, 122 = 1.84, p > 0.10). Verbal-intelligence group differences were low intelligence = 1.06, medium intelligence = 1.02, high intelligence = 0.92 (F2, 128 = 0.18, p > 0.10). Cognitive-style group differences were field dependent = 0.92, field intermediate = 1.18, field independent = 0.75 (F2, 128 = 1.25, p > 0.05).

Discussion

Results of the present study provide support for the hypothesis that formal reasoning patterns are necessary for the elimination of some biological misconceptions. Little or no evidence has been found to support the alternative hypotheses that mental capacity, verbal intelligence, or the cognitive style of field dependence/ independence play a significant role in the overthrow of misconceptions.

In essence, results show that a significantly smaller percentage of formal operational students hold the misconception that acquired characteristics can be genetically transmitted to offspring than concrete operational students. Formal students seem to understand that a newborn child’s characteristics are determined by a combination of parental genes carried in the sex cells and that environmentally induced changes in parents will not affect the offspring. Concrete operational students, on the other hand, appear to have failed to achieve this understanding.

According to the working hypothesis of the present study, the reason for the greater number of misconceptions among concrete students is that (1) virtually all naive students, regardless of reasoning ability will tend to adopt a theory of the inheritance of acquired characteristics (i.e., the Lamarckian historical antecedent to Darwinian theory of natural selection). In this sense conceptual development in the child recapitulates historical development of the species; (2) concrete-operational students will fail to reject this naive theory when introduced to principles of genetics and evolution via natural selection because they lack skill with the reasoning patterns necessary to do so (i.e., formal patterns are those used to evaluate competing hypotheses by comparing their predicted consequences with empirical data in a hypothetico-deductive fashion). Failing to internally evaluate the relative merits and demerits of their naive theory and the competing theory of natural selection taught in class, they continue to invoke the naive theory to generate predictions about the offspring’s skin color, hair color, and finger length. Interestingly, they tend to invoke the naive theory less often when the phenomenon is not very subtle and occurs rapidly (amputating fingers) and more often when the phenomena is more subtle and of longer duration (tanning of skin and dyeing of hair). Perhaps these students believe that the longer it takes to acquire a characteristic the more likely it is to modify the genes responsible for dictating the offspring’s characteristics- a not unreasonable supposition, but an unwarranted conclusion.

Quite possible concrete Ss have “understood’ the theory of natural selection in a limited sense, but they fail to utilize it as the basis for consistently generating


predictions. If so, this seems analogous to the situation in which a student is able to use a proportions strategy to solve a written problem of the form 4/6 = 6/x for the unknown x, but fails to utilize the same strategy to solve the Pouring Water Task (Lawson, 1986). Instead, he or she uses addition to predict that water at the sixth mark in a wide cylinder will rise to the eighth mark when poured into a narrow cylinder because they saw previously that water at the fourth mark in the narrow cylinder rose to the sixth mark when poured into the wide cylinder (4 + 2 = 6 and 6 + 2 = 8). Student difficulties with the Pouring Water Task do not arise from inability to carry out the correct computations but seem to arise from a failure to recognize which computations to carry out. The same sort of difficulty may be operating in the present study. Concrete Ss may understand some aspects of genetics and natural selection, but they do not know which phenomena to explain with its use. They have two contradictory theories, but because they have failed to internally monitor their compatibility or incompatibility they have failed to discover that they are contradictory and invoke one or the other depending upon theoretically irrelevant problem cues such as the length of time it takes to effect a change in parental characteristics (i.e., it takes a long time to tan but a short time to cut off a finger; therefore, tan skin will be passed to offspring but cut-off fingers will not).

Although the data are consistent with the previous explanation one must exercise caution in interpreting the results as conclusive for a number of reasons, the most important being that some, but certainly not all, alternative hypotheses have been tested. For example, the students were not pretested to determine whether or not they all believed in the inheritance of acquired characteristics prior to instruction. This is a clear weakness in the present study. It is entirely possible that some of them did not. Table V depicts this possibility (rows 1 and 3). We have no reason to expect that seventh-grade students would spontaneously invent the theory of

TABLE V Alternative Possibilities for Conceptual Acquisition in Concrete and Formal Students


~~

Concrete Students

no concept naive concept naive concept

naive concept naive concept > naive concept

misunderstood instruction

Formal Students

no concept scientific scientific concept * concept


natural selection prior to instruction, as we all recognize Darwin’s genius in doing so and few teachers would ascribe Darwin’s experience, intellectual skills, and tenacity to the average seventh grader. On the other hand, the average seventh grader does generate theories, albeit ones naively consistent with direct experience and not based upon complex combinations of hypotheses, deductions , and evidence. Nevertheless a followup study should be conducted in which the preinstructional conceptions of the students are assessed and compared with postinstructional changes.

Assuming that most students did indeed, at some point, adopt the naive inheritance of acquired characteristics theory, the concrete Ss’ failure to reject it may be largely due to their failure to understand concepts of natural selection and gene transfer (Table V, row 2). Assuming this is the case, one would be faced with the task of explaining why formal students understand these concepts while concrete students do not. Second, they may have understood the theory of natural selection but rejected it in favor of their own naive theory. We doubt this was the case, because it is assumed that formal reasoning is needed to evaluate the merits of competing theories; therefore, concrete Ss would most likely not have gone through the hypothetico- deductive evaluation process.

Educational Implications

Whichever of the components of the previous possible explanations are correct, it would seem extremely important for teachers to provide students with opportunities to discuss their prior conceptions (misconceptions) and carefully compare them with the newly introduced scientific conceptions in order to evaluate the logical and/or empirical inconsistencies or limitations of their prior conceptions. In other words, it is not enough to teach scientific conceptions. Teachers must also “unteach” naive misconceptions. To do so, according to the present hypothesis, requires not only that the students be introduced to more adequate conceptions, but that they must also understand the reasons for its correctness and for their naive conceptions in correctness. If understanding these reasons requires formal reasoning patterns, it would seem necessary for the students to be formal operational; hence instruction must be designed to promote its development in concrete operational students. For a review of programs designed to do so, see Lawson (1985).

References

Brumby, M. (1984). Misconceptions about the concept of natural selection by medical biology students. Science Education, 68(4) , 493-503.

Bereiter, C. & Scardamalia, M. (1979). Pascual-Leone’s M construct as a link between cognitive development and psychometric concepts of intelligence. Intelligence,

Burtis, P.J. & Pascual-Leone, J. (1974). FIT: Figural Intersection Test: A Group Measure of M-Space. Unpublished manuscript, York University.

Crutchfield, R.S., Wondworth, D.G., & Albrecht, R.S. (1958). Perceptual Performance and the Effective Person. Lackland Air Force Base, TX: Personnel Laboratory.

Driver, R. (1981). Pupils’ alternative frameworks in science. European Journal of Science Education, 3( l), 93- 101.

3, 41-63.


Driver, R. (1983). The pupil as scientist. Milton Keynes, England: The Open University Press.

Halloun, I.A. & Hestenes, D. (1985a). The initial knowledge state of college physics students. American Journal of Physics, 53( 1 l) , 1043- 1055.

Halloun, I.A. & Hestenes, D. (1985b). Common-sense concepts about motion. American Journal of Physics, 53(22), 1056- 1065.

Heimler, C.H. & Lockard, J.D. (1977). Focus on Life Science, 3rd. ed. Columbus, OH: C.E. Merrill.

Lawson, A.E. (1978). The development and validation of a classroom test of formal reasoning. Journal of Research in Science Teaching, 15(1), 11-24.

Lawson, A.E. (1983). Predicting science achievement: The role of developmental level, disembedding ability, mental capacity, prior knowledge and beliefs. Journal of Research in Science Teaching, 19(3), 233-248.

Lawson, A.E. (1985). A review of research on formal reasoning and science teaching. Journal of Research in Science Teaching, 22(7), 569-618.

Lawson, A.E. (1986). A neurological model of sensory motor problem solving with possible implications for higher-order cognition and instruction. Journal of Research in Science Teaching, 23( 16), 503-522.

Pascual-Leone, J., & Smith, J . ( 1 969). The encoding and decoding of symbols by children: A new experimental paradigm and a neo-Piagetian model. Journal of Experimental Child Psychology, 8, 328-355.

Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W.A. (1982). Accom- modation of a scientific conception: Toward a theory of conceptual change. Science Education, 66(2), 221-227.

Shipley, W.C. (1940). A self-administering scale for measuring intellectual impairment and deterioration. Journal of Psychology, 29, 37 1-377.

Viennot, L. ( 1 979). Spontaneous reasoning in elementary dynamics. European Journal of Science Education, 1(2), 205-221.

Witkin, H.A., Moore, C.A., Goodenough, D.R. & Cox, P.W. (1977). Field- dependent and field-independent cognitive styles and their educational implications. Review of Educational Research, 47(1), 1-64.

Manuscript accepted February 20, 1988

Documents

Formal reasoning ability and misconceptions concerning genetics and natural selection