Cognitive development in a secondary science setting

Research in Science Education, 30(4), 403-416

Cognitive Development in a Secondary Science Setting Lorna C. Endler

University of California, Santa Barbara Trevor Bond

James Cook University

Abstract Observations were made of the progressive change in the cognitive development of 141 students over the course of their secondary education in an Australian private school. Cognitive development was measured in years 8, 10 and 12 using Bond's Logical O-erations Test. Rasch analysis of each of the data sets provided ability estimates for students in the )ear groups of 1993 (year 8), 1995 (year 10) and 1997 (year 12). Twenty-nine students from the year group of 1993 were tested on all three occasions. We analysed data from these 29 students in order to investigate the children's cognitive development across years 8, 10 and 12. We also examined the influence of the Cognitive Acceleration through Science Education (CASE) Thinking Science program on the cognitive development and scholastic achievement of these students. We found increased mental growth between years 8 and 10 for most students in the Thinking Science cohort, which could not be predicted from their starting levels. There was a significant correlation between cognitive development and the scholastic achievement of these students. Although boys as a group were more advanced in cognitive development than girls in years 8 and 10, no difference was found in the rate of cognitive change based on sex up to year 10. However girls showed cognitive gains across years 10-12 which were not found in boys. The students who were new to the school also showed increased cognitive development in years I 1 and 12. Students who had experienced the Thinking Science course were more eognitively developed than students who joined the school after the intervention had taken place. This study supports the claim of Adey and Shayer that there is a relationship between cognitive development and scholastic achievement, even though we used different measures of cognitive development and scholastic achievement.

This study followed the progress o f a year group o f students in a private secondary school in North Queensland from their entry in year 8 until they left the school in year 12. The first author was the Head o f Science at the school during the period o f the study, and the second author was her MEd supervisor. Although a great deal o f testing is carded out by teachers in schools, it is not usually directed towards gaining a measure o f children's long term cognitive development. Because most testing in the secondary school takes place within discrete subject areas, teachers have only a series o f fleeting glimpses o f the development o f their students. Furthermore, most developmental studies o f children by researchers are cross-sectional, rather than longitudinal. In contrast, this study tracked the cognitive development o f individual students over their secondary schooling and examined the influence o f the Cognitive Acceleration through Science Education (CASE) Thinking Science program on their cognitive development (Adey, Shayer, & Yates, 1995). In the early part o f the last century the prevailing view o f children's cognitive activity was that

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it was the same as that of adults and would become more efficient with use. Piaget challenged this perspective by his claim that children's thinking passes through a series of stages that progressively show greater sophistication. His idea that a child thinks and learns in qualitatively different ways during particular developmental periods was revolutionary at that time. In their book The Growth of Logical Thinking from Childhood to Adolescence, Inhelder and Piaget (1955/1958) outlined the transition of children's thinking from the concrete operational period of childhood to the formal operational period of adolescence. The formal operational adolescent can think effectively in the abstract mode. Formal thinkers are able to dissociate general ideas or concepts from the contexts in which they were learned and, therefore, they do not require specific concrete cues in order to trigger the recall of these general principles. They are also able to intellectually manipulate concepts by integrating them into universal generatisations or by taking these generalisations back to their first principles. In contrast, the thinking of a child in the concrete operational stage is characterised by reasoning limited to their reflection on personal physical experiences in the concrete world. Formal operational thinking is hypothetico-deductive; the student is able to conceive of new ideas, concepts, hypotheses or principles, explore their implications and then test for their validity. Secondary science curriculum materials in the UK require students to use formal thinking (Adey & Shayer, 1994). This is also true in Queensland, where the senior science syllabi produced by the Board of Senior Secondary School Studies require teachers to specify in their work programs how they will provide opportunities for their students to demonstrate complex reasoning skills. Performance on these more difficult assessment items determines the overall success of the student in the subject. The inclusion of assessment tasks which involve complex scientific concepts requires students to use what Piaget calls formal operational thinking: higher order thinking skills are essential for the manipulation of formulae, the design of scientific experiments and making the necessary connections between concrete experimental data and abstract scientific theory. From this perspective, the study of sciences becomes a challenge to a student who has not yet reached the formal operational period: that is, achievement in science, and in other subjects, is likely to be related to cognitive development and enhancement of cognitive development would be likely to improve scholastic achievement. There have been many attempts to accelerate cognitive development (Kuhn & Angelev, 1976; Rosenthal, 1979; Lawson & Snitgen, 1982). Most of the earlier studies were rather limited, consisting of short term projects. However Adey, Shayer and Yates (1989) showed that it is possible to significantly restructure the thinking capacity of students by interventions over a considerably longer time scale. The CASE Thinking Science program is based on practical investigations involving cognitive conflicts which require the use of Piaget's formal schemata for their successful solution (Adey, Shayer, & Yates, 1995). The practical work in the laboratory is followed by problem solving tasks which give the students a chance to reflect on their own thinking and so transfer what they have learned to other contexts. The material is usually presented to 11- to 14-year olds in the first two years ol~their secondary schooling. The results of the CASE Thinking Science project in the UK were quite remarkable. Following extensive trials in schools, clear evidence was obtained of improved scholastic achievement. Pupils in eight British comprehensive schools who experienced Thinking Science lessons achieved much better grades in science, mathematics and English than fellow pupils in the 1989 and 1990 General Certificate of Secondary Education (GCSE) examinations (Adey & Shayer, 1994).

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Research Questions Previous studies, particularly those which informed the original CASE investigators, raised a number of important general questions concerning the cognitive development of the sample of children in this study: Question I: Is there evidence of cognitive development across the five years of secondary schooling? Question 2: If so, can periods of more rapid mental growth be detected? The model put forward by lnhelder and Piaget in The Growth of Logical Thinking from Childhood to Adolescence (1955/58), which has been supported by replication studies (Lovell, 1961; Jackson, 1965; Lawson & Blake, 1976), proposes that the adolescent years are the time of progression from concrete operational to formal operational thinking. Our study provided an opportunity to examine longitudinal empirical data in order to investigate this question and also to determine whether the rate of cognitive developm-:r~tmight be greater during years 8-10 than in years 10-12. Previous research has suggested that periods of more rapid intellectual growth can be detected in the early adolescent years (Shayer & Wylam, 1978; Shayer, Kiachemann,& Wylam, 1976; Shayer & Adey, 1981). Question 3: Is development the same for all students, or are there differential rates within the group? Much of the post-Piagetian research has shown that there is a great deal of variation in the process of cognitive development. The nature of the schooling which children receive appears to be an important factor affecting this aspect of adolescent development (Kuhn & Angelev, 1976; Rosenthal, 1979; Shayer & Adey, 1981). Furthermore, teachers and parents are aware of the existence of early and late developers. One question for this study was whether children within the sample would show differing rates of development. Adey (1992) reported differences in the pattern of cognitive development of young male and female adolescents. The proportion of girls reaching formal operational thinking did not increase after the age of 14 years, whereas this effect was seen later, at 15 years, in boys. If this phenomenon is due to the later onset of adolescence in males, as was thought, the sex of the individual would become an important variable in our study. Question 4: Are patterns of cognitive development related to scholastic achievement? Shayer and Adey measured cognitive development using the Piagetian Reasoning Tasks which they devised. Shayer (1998) reported evidence of the long term effect of cognitive acceleration on pupils' school results, showing a relationship between cognitive development and scholastic achievement. It was interesting to speculate whether Shayer and Adey's claims about the relationship between cognitive development and scholastic achievement could be supported in a study using a different measure of cognitive development as well as other indicators of school achievement.

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Question 5: Is there any evidence that Thinking Science had any effect on the cognitive development of the subset o f students who were exposed to that intervention? Adey and Shayer (1990) found a greater increase in the Piagetian levels of students who had been exposed to the Thinking Science intervention than would normally be expected in children o f the same age. It follows that similar increases might be predicted to occur in the children of this study who experienced the intervention course compared with those who did not have the experience. Although the subset of students in years 10-12 who joined the school after the Thinking Science program may not be regarded as a control group, they do serve as a useful group for comparison purposes.

Method

Sample The students in this study attended a coeducational private school (years 8-12) in North Queensland. There is no entrance test for academic selection, and fees are modest compared with similar private schools in larger cities in Australia. The school has a diverse population, serving the local urban community as a day school and has a boarding facility for students from rural North Queensland, including Aboriginal and Torres Strait Islander children. There are also a number of international students, some funded by their families (e.g., from Japan) and others by Australian overseas aid schemes (e.g., from Papua New Guinea). Approximately one third of the students in the sample in year 8 had completed their primary education in the junior department (K-7) of the same school, which operates independently on the same campus. A similar number of new year 8 day students came from local state primary schools. Years 8, 10 and 11 are the most common years for the intake of new boarding students, and these students from a variety of primary school backgrounds made up the remainder o f the year 8 sample (N=53). The school is situated in a garrison city which has a transient population of both blue and white collar workers, as well as a significant number o f people in the defense forces. There is a high annual turn over in the school population which reflects the local demographics. During the fiveyear period o f the study, 45% of the original year 8 sample let~ the school to be replaced by a similar number of new students.

Intervention The Thinking Science program was presented to the three classes in each year at the school as a series o f 80 minute lessons. About one lesgon every three weeks was conducted over the twoyear period of years 8 and 9 (1993-94). The classes at each year level were taught by three science teachers, who worked closely as a team to follow a common curriculum. The first author, the school's science coordinator, taught one class in each year and acted as a mentor to her peers during the implementation of the Thinking Science program. Although each Thinking Science lesson was delivered as a unique learning experience, the teachers made every effort to create connections between the Thinking Science concepts and the regular science curriculum in order to encourage general transfer to other contexts.

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Data Collection Cognitive developmental data were collected from each student participating in the school's science classes in 1993, 1995 and 1997. Fifty-three students in year 8 were tested using Bond's Logical Operations Test in February 1993 at the start of the school (calendar) year. In November 1995, the 40 students remaining of the original group of 53 were retested at the end o f year 10, together with a further 40 students who were new to the school. By November 1997, only 29 of the original 53 students from year 8 remained in the school and these were tested for a third time, together with 37 of their year 12 peers who were newcomers.

Instruments Cognitive development was measured using Bond's Logical Operations Test [BLOT] (Bond, 1976). The measurement of Piagetian levels to monitor adolescent cognitive development is suggested by Adey and Shayer in their book Really Raising Standards (1994), which provides a convincing argument for the current relevance of the Piagetian model. Bond's Logical Operations Test (BLOT) is a thirty-five-item multiple choice :est, which assesses children's cognitive development (Bond, 1995b). The BLOT was developed as an alternative to the mdthode clinique interview technique of Inhelder and Piaget. This test has a number of advantages over the more traditional Piagetian m~thode clinique. A pencil and paper test is much easier to conduct and less time consuming, for both the researcher and the subject. It can be conveniently administered to a large sample of people at one time. Because the results are easily quantifiable, the test also lends itself to statistical analysis and evaluation. The validity o f BLOT has been confirmed by Bond (1976, 1995b). The usefulness of the test has been endorsed by other workers in the field including Christiansson (1983) and Smith and Knight (1992). Bond (1995b) has demonstrated concurrent validity of his BLOT and Shayer and Adey's Piagetian Reasoning Task Pendulum (PRT III), a post-test used in the CASE project. Although BLOT and PRT III differ in format and only partially overlap in their range of age and ability of the subjects, the Pearson correlation coefficient between raw scores on PRT III and BLOT was: r = .75 (Adey, 1989).

Analytical Methods Analysis of the raw data in this study was carried out using ACER Quest, a computer program which provides the data analyst with access to the most recent developments in Rasch measurement theory (Adams & Khoo, 1993). Inherent in the Rasch model is the notion that when a person is given a test item only two facts determine the probability of the person getting the answer fight - - the ability of the person and the difficulty of the item. The Quest Rasch analysis provides item estimates, case estimates, and fit statistics in the form of information tables and maps. The units in which Rasch estimates are repoiXed are logits. Rasch measurement models provide objective measurement which functions independently of the objects measured by a test and which can separate the effects of the measuring instrument from the objects measured. The appropriateness of applying the Rasch model to Piagetian measures of stages of thinking has been endorsed by Hautamaki (1989). Other recent applications of Rasch analysis include a test of computer anxiety (King & Bond, 1996) and the validation of written tests to measure Piagetian formal operational ability (Bond, 1995a, b). The work of Rasch is significant in that it has brought the principles o f scientific objective measurement to the often

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subjective field of educational psychology (Andrich, 1988; Bond & Fox, in press). Rasch analysis of each of the data sets in this study provided ability estimates for students in the year groups of 1993 (year 8, N= 53), 1995 (year 10, N=80) and 1997 (year 12, N = 66). The data obtained from the testing of whole year groups were used to create a detailed picture of the composition of each of the annual cohorts of students. The detailed analysis of the annual cohorts was included in the first author's MEd(Hons) thesis (Endler, 1998) but lies outside the scope of this paper. However, it is worth stating that there was a surprisingly high variation in the level of cognitive development within each of the annual cohorts, ranging from early concrete to late formal operations, confirming the authors' assertion that the population of the school was diverse. Although caution is needed in extrapolating from any sample to the general population, it would appear that this particular school population would be more typical of the general population than selective private schools in Australia. Data from the 29 students tested on all three occasions, who remained in the school throughout the five years of the study, were analysed separately to investigate the children's cognitive development across years 8, 10 and 12. We collected records of the scholastic achievement of these students over the five-year period to investigate the claim that school achievement is dependent at least to some extent on cognitive development. Students' results in the Australian Schools Science Competition in 1993 and 1995, and also the year 12 results from the Board of Senior Secondary School Studies of Queensland, were used as indicators of scholastic achievement. These were external achievement results obtained from State-wide or national test scores. We also examined the influence of the CASE Thinking Science program on the cognitive development and scholastic achievement of these students. The relative rates of cognitive development of the 29 boys and girls over the period of the study were compared to investigate the possible effect of sex. A subset of new students who entered the school between 1994 and 1997 were tested in 1995 (N=40) and 1997 (N--37). Data from these newcomers were analysed separately to provide comparisons with data from the Thinking Science cohort. Althgugh none of the new students would have experienced the complete Thinking Science program, it is possible that a few students in this subset experienced some of the Thinking Science lessons in year 9 and were therefore not quite accurately classified. Assuming that these misclassified students made gains similar to those of the cohort who had experienced the entire intervention, then any differences between the experimental and comparison groups would tend to be diminished.

Results and Discussion We analysed the ability estimates, obtained from Rasch analysis of the raw scores of the 29 students who had been tested with the BLOT on all three occasions (years 8, 10 and 12), to investigate the cognitive development of students over time. The mean slope of the data was calculated for the periods years 8-10, years 10-12 and years 8-10-12 to compare the rate of cognitive development of the students for these time intervals. Figure 1 shows that there was a significant increase in cognitive development between year 8 and year 12 and that the greatest change was between year 8 and year 10. A more detailed comparison of the rate of change of cognitive ability estimates is shown in Table 1, which gives the results of testing for differences in the rate of change in the three data sets (years 8-10, 10-12 and 8-12). The null hypothesis was that the data groups did not differ in the rate of cognitive change. The rapid change found in the

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