Running head: SCIENTIFIC THINKING

 

 

Scientific Thinking in Eighth Grade Students: Attitude and Metacognition about the Nature of Science

 

Erin E. Peters

George Mason University

Written in order in partial fulfillment of requirements for EDRS 811 in Fall 2005

Dr. Dimiter Dimitrov

 

 


Abstract

            There is a gap in research where the fields of the nature of science and metacognition intersect. The purpose of this investigation is to answer the following research questions: a) To what extent do attitude and perceived metacognitive abilities in observation, data collection, and measurement predict the ability to explain scientific reasoning in making conclusions? b) Are there differences between males and females in ability to explain reasoning for conclusions and do they depend on ethnic group? c) Is there a relationship between the ability to have metacognition about observation and the ability to have metacognition about data collection?

            Three hundred and eight eighth-grade science students from an urban middle school in the mid-Atlantic region of the United States participated in a sixteen question Likert-scale survey. After all outliers were removed, results showed evidence that there is statistically significant predictive relationship, F(4, 264) = 115.07, p ‹ .001. The revised multiple regression equation is as follows: Y (hat) = .218x1 + .228x2 + .124x3 + .384x4 + C, where x1 is attitude, x2 is perceived metacognition in observation, x3 is perceived metacognition in measurement and x4 is perceived metacognition in data collection. There was no evidence that the ability to explain reasoning in making conclusions differ for minority students and for white students, among Hispanics and African American students, or among males and females. The analysis of data also showed that there were no interaction effects. The Chi-Squared test showed that there is a relationship between the ability to have metacognition about observation and the ability to have metacognition about data collection, c2 (128) = 283.887, p‹ .001. Baseline relationships have been indicated by data among attitude, perceived metacognition in observation, data collection, measurement and the ability to explain reasoning in making conclusions. Future studies could ask go beyond perceptions to ask students to make or evaluate conclusions from given data.

Introduction

            One of the most prominent reforms in science education in the past ten years is inquiry science (American Association for the Advancement of Science, 1993). Educators who teach inquiry science are striving to improve student understandings and explanations about the real world. In other words, inquiry science is the enactment of the nature of science. Too often, inquiry science is taught as either the scientific method or as “hands-on,” disconnected activities (Bybee, 2004). National documents such as the National Science Education Standards (1996) or The Benchmarks for Science Literacy (1993), written for the audience of science teachers, tend to give ambiguous guidelines for teaching science inquiry. In the current environment of standards-based education, it is easy for science teachers to slip into the mode of disseminating information rather than teaching the ways of knowing that categorize the discipline of science (Duschl, 1990).  McComas, Almazroa, and Clough (1998) call for a more prominent role of the nature of science in curriculum to be explicitly taught for maximum effectiveness. Researchers have delved into how teachers perceive the nature of science and the implications this has on instruction.

Defining the Nature of Science

            In the past, there was not a consensus on what elements of the nature of science were important to teach, but in the past ten years researchers have converged on aspects of the nature of science, and more recently there has been an agreement on the elements of the nature of science (McComas et al., 1998). The literature converges on seven aspects of the nature of science that defines science as a discipline: a) scientific knowledge is durable, yet tentative, b) empirical evidence is used to support ideas in science, c) social and historical factors play a role in the construction of scientific knowledge, d) laws and theories play a central role in developing scientific knowledge, yet they have different functions, e) accurate record keeping, peer review and replication of experiments help to validate scientific ideas, f) science is a creative endeavor, and g) science and technology are not the same, but they impact each other (McComas, 2004). Evidence of these principles as the foundation for how science operates as a discipline can be found in science education research journals, books about the philosophy and epistemology of science, and practitioner handbooks.

Translating Knowledge of the Nature of Science into Classroom Practice

            Even with modest gains in understanding of the nature of science, teachers still fail in translating this knowledge into classroom practice. A study of a group of preservice teachers with adequate knowledge of the nature of science showed that there was not much instruction involving the nature of science due to a preoccupation with classroom management and the mandated curriculum (Abd-El-Khalick, Bell, & Lederman, 1998). In a study involving preservice teachers in Spain, researchers found that there was no correspondence between teacher conceptions of the nature of science and classroom practice (Mellado, 1997). In Australia, a study showed that even when both teachers and students believed science to be an evolving discipline, the status quo in the classroom was in direct contrast to this belief (Tobin & McRobbie, 1997). The class was taught with a traditional lecture format, and teachers and students alike were comfortable with the format although it was opposed to their belief about how science is done. Even college science faculty members who had very sophisticated understandings of the nature of science, when collaborating on the development and implementation of a integrative non-major science course, did not offer any explicit instruction in the nature of science during the course (Southerland, Gess-Newsome, & Johnston, 2003). In a case study of an experienced teacher who sought help from researchers in how to apply her sophisticated understanding of the nature of science to her fourth grade classroom had difficulty explicitly teaching any elements of the nature of science (Akerson & Adb-El-Khalick, 2003). Apparently, the mechanisms that help operationalize the understanding of the nature of science into classroom instruction are poorly understood.

Student Understanding of the Nature of Science

            Investigations into student understanding of the nature of science originate in different realms, but tend to converge on the same finding, that students need to experience cognitive dissonance in order to eliminate archaic conceptions of the nature of science. When students were presented with discrepant events in a long-term setting, their notions of the nature of science began to conform to professional scientists’ understanding of the nature of science (Clough, 1997). Students in another classroom instructed in canonical understanding of science did not show maturity in their understanding of the nature of science, but after incorporating student ideas, including exploration of misconceptions, into instruction the students showed gains in their understanding of the nature of science (Akerson, Flick, & Lederman, 2000). Hogan (2000) suggests that science education researchers can gain a better understanding of how students operationalize the nature of science by dividing up their knowledge into two categories: distal knowledge, how students understand formal scientific knowledge, and proximal knowledge, how students understand their own personal beliefs and commitments in terms of science. Hogan believes that by seeing how the two categories of knowledge intersect, researchers can gain access into how to better develop student understanding of the nature of science.

            In another study of student understanding of the nature of science, it was found that students views depended greatly on moral and ethical issues, rather than in newly presented material (Zeidler, Walker, Ackett, & Simmons, 2002). Instead of changing their archaic notions of the nature of science, students tended to hang on to their prior understandings even when presented with conflicting information. Undergraduate science majors were found to change their conceptions of the nature of science during a long-term project that offered many opportunities to discover conflicting information (Ryer, Leach, & Driver, 1999). It appears from the research that students will change their conceptions of the nature of science to more sophisticated through long-term exposure to discrepant information, but before that can be accomplished more information about student processes in learning the nature of science is needed.

Instruments Used to Measure Understanding of the Nature of Science

           Many of the instruments used in the studies regarding the nature of science tend to be objective, pencil and paper assessments which subsequently changed into more descriptive instruments. Toward the end of the 1990’s several researchers make arguments that traditional paper and pencil assessments would not be adequate in fully explaining what needs to be known about teacher and student conceptions of the nature of science (Lederman, Wade, & Bell, 1998). Researchers responded to this argument by conducting interviews along with surveys or by including several open-ended questions on surveys in order to get more descriptive data. Several versions of an instrument originally developed by Lederman, the Views of Nature of Science (VNOS), have been used mostly by the researchers who focus on preservice teachers. Items in this instrument ask teachers to explain scientific activities in their classroom. Researchers then use a rubric to identify when teachers explicitly mention one of the seven identified aspects of the nature of science. Other instruments have been developed to be more descriptive in explaining student achievement in the nature of science such as Scientific Inquiry Capabilities and Scientific Discovery (Zachos, Hick, Doane, & Seargent, 2000). Although the objective, pencil and paper assessments have been altered to include more description of mechanisms, there is still a need for improved assessments regarding both the teacher and student understandings of the nature of science.

Using the Nature of Science for Metacognition

            The aspects of the nature of science can be useful in helping students to think about their epistemology. Examining the nature of science can supply characteristics that distinguish science from other ways of knowing and explicitly help students scrutinize their rationale in forming ideas (Duschl, Hamilton, & Grandy, 1992). Teachers can utilize these characteristics in their lessons to help students to examine the information they know and think about how student knowledge is scientific. Educational researchers studying metacognition are in agreement that traditional methods of teaching do not allow students to demonstrate all of their knowledge about science (Driver, Newton, & Osborne, 2000).

            The field of the nature of science still requires a great deal of exploration. In order to fully understand how people learn such as esoteric subject as the nature of science there needs to be more dialogue between the scientific community and science teachers (Glasson & Bentley, 2000), more understanding of student views of the nature of science (Zeidler et al., 2002), and more understanding of how teachers who have a sophisticated view of the nature of science can incorporate these ideas into classroom practice. Bell and Lederman (2000) studied scientists who had sophisticated but different views on the nature of science to see how they made decisions based on their views. Their research showed no differences in decision making because the scientists made their professional decisions based on personal values, morals/ethics and social concerns. Bybee (2004), a researcher who has been involved in policy for decades, notices an overemphasis in teaching strategies regarding the nature of science and an under emphasis on contemporary learning theory. The field of the nature of science has been successful in defining operational elements of the nature of science and now it is time for the field to progress into cognitive science domains.

            Literature in metacognition emphasizes the lack of consensus on how epistemological factors influence student learning (Brown, 1987). More research in developing a thinking strategy or ethic to evaluate the scientific merit of information can change how students develop their scientific way of knowing. Many instructors attempt to teach scientific thinking veiled as the scientific method, which is limiting the way students construct epistemologies regarding the nature of science. Cognitive change can be invoked through deep processes such as metacognition (Flavell, 1987). More research in this field will help to produce more fully informed ideas on how epistemological factors influence student learning.

Research Questions

            There is a gap in research where the fields of the nature of science and metacognition intersect. The research that has been done in the field of the nature of science attempts to take new teachers and explicitly teach them elements of the nature of science. There is very little success using different variations of this method. There is less success in getting teachers to translate this knowledge into classroom practice. Perhaps it is because teachers need to evaluate their own learning in order to facilitate student learning in such an esoteric concept as the nature of science. Dawson (2000) claims that in the classroom there is usually not enough repetition for metacognitive awareness and student competence level is not usually taken into consideration. Allowing prior research to inform my own research, this study intends to construct a baseline of student attitudes regarding metacognition in science. Prior research has helped to define the nature of science, to illuminate difficulties in teacher and student understanding of the nature of science, and to show supportive metacognitive processes that can be used as a basis for the construction of new metacognitive tools that will help to scaffold teachers’ and students’ understanding of the nature of science to more meaningful comprehension. The purpose of this investigation is to measure student attitudes toward science and perception of metacognition about the nature of science. Since little is known about the relationships among the variables, the study is intended to develop a baseline of perceptions. Once the relationships among the variables are better understood, further studies can progress to practical applications of metacognition in the classroom. The results of this study may help to inform future investigations into mechanisms that operationalize the concepts of the nature of science through metacognitive prompts. Related to this study are the following research questions: a) To what extent do attitude and perceived metacognitive abilities in observation, data collection, and measurement predict the ability to explain scientific reasoning in making conclusions? b) Are there differences between males and females in ability to explain reasoning for conclusions and do they depend on ethnic group? c) Is there a relationship between the ability to have metacognition about observation and the ability to have metacognition about data collection? Related to the first research question is the following hypothesis, Ho: R2 = 0 and the following multiple regression equation:  Y(hat) = b1X1 + b2X2 + b3X3 + b4X4 + C where the predictors are X1 = attitude, X2 = perceived metacognitive ability in observation, X3 = perceived metacognitive ability in data collection, and X4 = perceived metacognitive ability in measurement, and the criterion variable (Y-hat) is ability to explain scientific reasoning in making conclusions. Related to the second research question are the following null hypotheses: H01: (µAA + µH)/2 = µW  (x1), H02: µAA = µH  (x2), H03: µM = µF  (x3), H04:  x4 = x1x3, H05: x5 = x2x3.  The first null hypothesis states that there are no differences in the mean scores on ability to explain reasoning in making conclusions between minorities and white participants. The second null hypothesis states that there are no differences between African American participants and Hispanic participants in the mean scores on ability to explain reasoning in making conclusions. The third null hypothesis states that there are no differences between males and females in mean scores on ability to explain reasoning in making conclusions. Null hypotheses four and five indicate that there are no interactions among the data.

            With regard to the third research question, is there a relationship between the ability to have metacognition about observation and the ability to have metacognition about data collection, the hypothesis compares the Chi-Square test statistic to the Chi-Square critical value.  Pearson Chi-Squared coefficient will be computed through SPSS software, so the hypothesis will be tested by evaluating the p-value in the output. SPSS printouts are found in Appendix 3.

Method

Sample

            Three hundred and eight eighth-grade science students from an urban middle school in the mid-Atlantic region of the United States participated in the study. The middle school serves 928 students, grades six through eight. Seventeen percent of students from this school receive free or reduced price for lunches. The sample population consisted of 7.9% Black students, 10.7% Hispanic students, and 69.2% White students. Table 1 shows relevant demographic information.

                                                                Insert Table 1 here                                              

 

 There were 50 students with learning disabilities in the sample. One group of eighth grade students was not included because the researcher is also their science teacher, which may influence the data due to the students’ direct contact with the content being investigated.

Instruments

            Data were collected on the survey instrument, shown in Appendix 1, by eighth grade classroom teachers on a selected day. Three science teachers administered the survey to all of their students, approximately 100 students per teacher. Students completed the survey within an allotted time of twenty minutes. Students did not receive explicit instruction on the content of the survey, although an objective requiring instruction in the nature of science has been part of the science curriculum at the selected school since 2003. The students completed 16 questions on a Likert-scale survey which asked them to think about all of their experiences in all science classes, not just their current science class.

            The 16-item survey was designed to test five different student perceptions: a) attitude about the subject of science, b) use of metacognition in observation, c) use of metacognition in data collection, d) use of metacognition in measurement, e) ability to explain reasoning in making conclusions. Each of the topics was chosen because they exemplify skills that are valuable in teaching science as a way of knowing. Appendix 1 shows how the topics chosen for the survey relate to science classroom teaching.

            Students were asked to choose a number between 1 and 5 to show whether they agreed with the statement (5) or disagreed with the statement (1). Multiple questions were designed to test the same variable so that instrument subscale reliability could be verified. Questions 1, 3 and 8 tested student attitudes toward science. Questions 2, 4 and 11 tested student perception of ability to have metacognition about observations. Questions 7 and 16 tested student perception of metacognitive ability in measurement. Questions 5, 6, 9 and 15 measured student perception of metacognitive ability in data collection. Questions 10, 12, 13 and 14 measured student perceived ability to reason when making conclusions. A copy of the survey can be found in Appendix 2.

            Field tests of the survey were conducted with three high achieving, three average achieving and three low achieving readers from the eighth grade. Feedback regarding comprehension and meaning of the questions provided during the field test interviews after the survey guided the revisions of the instrument. Changes in the statements were made based on the interviews of the students after the draft survey was administered. The students involved in the field test did not take the survey, since they had prior knowledge of the intention of the survey.

            Reliability as measured by alpha test for the entire instrument is .8892. Subscales were also tested for reliability using the alpha test. The subscale for observation items is .4338. The subscale for measurement items is .6004. Items that measure metacognition for data collection had a reliability of .6174. Items that measure metacognition for attitude had a reliability of .6237. The items that tested the ability to explain reasoning in concluding had an alpha test of .7136. A summary of the reliability scores are listed in Table 2.

 

                                                                Insert Table 2 here                                              

 

Procedure

            Approval for this study was obtained from the Human Subjects Review Board at the sponsoring university and from the Research and Evaluation office at the participating school district.  Three hundred and eight consent and assent forms were set to the parents and students of the target sample. Two hundred and seventy seven were returned with the correct information, resulting in an initial return rate of 85%. Two weeks after the permission forms were sent home, a reminder was sent to the parents and students who did not complete a permission form. The second contact resulted in the completion of more permission forms. The final return rate was 89.9%.

            Three science teachers volunteered to administer the survey on a selected day. Students were instructed to act on their first instinct and to rate their perception from the first or second reading of the statement. Students were also asked to write an “M” in the top right hand corner of the survey paper if they were male, and to write an “F” if they were female. Students were also asked to write their ethnicity with their gender at the top right hand corner of the survey. The physical materials required were a writing instrument and the survey. Teachers described how the Likert-scale was used to determine responses and then allowed students to read and responded to the survey independently. Students were given twenty minutes to rate their perceptions on the survey and all students completed the survey within the allotted time.

            Possible bias could be introduced through students who did not take the survey seriously or by students who did not understand the statements on the survey. Some bias may have been introduced because all of the items used the same versions of the Likert-scale and some students may have recorded their understandings on the wrong end of the scale.   

Data Analysis

            Related to the first research question, to what extent do attitude and perceived metacognitive abilities in observation, data collection, and measurement predict the ability to explain scientific reasoning in making conclusions?, is the following multiple regression equation:  Y(hat) = b1X1 + b2X2 + b3X3 + b4X4  + C where the predictors are X1 = attitude, X2 = perceived metacognitive ability in observation, X3 = perceived metacognitive ability in data collection, and X4 = perceived metacognitive ability in measurement and the criterion variable (Y-hat) is ability to explain scientific reasoning in making conclusions. The statistical method used is multiple regression which will be used to check if the equation applies at the population level. Multiple regression analysis will continue by checking the standardized coefficients to see which are most important to the relationship. R square will be determined to find the percent of overlap between all of the predictors, attitude, perceived metacognitive ability in observation, perceived metacognitive ability in data collection, and perceived metacognition in measurement, and the ability to explain reasoning in making conclusions. Semi-partial correlations will be determined to find the unique contribution of each predictor to the ability to explain reasoning in making conclusions. Outliers will be explored for the X components, Y component and influential data points. The X component outliers will be identified by comparing the computed cutting score to the Leverage value. The Y component outliers will be determined by computing the studentized deleted values and examining if the range is above positive three or below negative three. The influential data points will be determined by examining Cook’s coefficient to see if the maximum value is above one. If any outliers are discovered, they will be deleted from the data set and the statistical analysis for multiple regression will be completed again.

            Related to the second research question, are there differences between males and females in ability to explain reasoning for conclusions and do they depend on ethnic group, are the following null hypotheses: H01: (µAA + µH)/2 = µW  (x1), H02: µAA = µH  (x2), H03: µM = µF  (x3), H04:  x4 = x1x3, H05: x5 = x2x3.  The first null hypothesis states that there are no differences in the mean scores on ability to explain reasoning in making conclusions between minorities and white participants. The second null hypothesis states that there are no differences between African American participants and Hispanic participants in the mean scores on ability to explain reasoning in making conclusions. The third null hypothesis states that there are no differences between males and females in mean scores on ability to explain reasoning in making conclusions. Null hypotheses four and five indicate that there are no interactions among the data. Dummy coding will be input according to the hypotheses and an analysis of variances will be determined using a multiple regression model for ANOVA purposes through SPSS software. P-values will be examined to determine if there is evidence to reject or fail to reject the null hypotheses.

            With regard to the third research question, is there a relationship between the ability to have metacognition about observation and the ability to have metacognition about data collection, the Pearson Chi-Squared coefficient will be computed through SPSS software and analyzed for any significant correlation.

Results

            Results from the multiple regression in Table 3 shows evidence that there is statistically significant predictive relationship among attitude, perceived metacognition in observation, perceived metacognition in data collection, and perceived metacognition in measurement to predict ability to explain reasoning in making conclusions F(4, 272) = 118.99, p ‹ .001. The multiple regression equation is as follows: Y (hat) = .195x1 + .266x2 + .112x3 + .384x4  - 0.1, where x1 is attitude, x2 is perceived metacognition in observation, x3 is perceived metacognition in measurement and x4 is perceived metacognition in data collection. All of the p-values are less than .05, showing that all predictors are statistically significant. Results are summarized in Table 3 and Table 4.

 

Insert Table 3 and Table 4 here

 

            The standardized coefficients show the relative influence of each variable. From most influential to least influential, the predictors are data collection (0.402), observation (0.250), attitude (0.206), and measurement (0.132). The percent overlap for all predictors and the ability to explain reasoning in making conclusions is 63.6%, as seen in Table 4. The unique contribution of attitude over ability to explain reasoning in making conclusions, controlling for all other predictors, is 3.1%, found by squaring the semi-partial correlation. The unique contribution of perceived metacognition in observation over ability to explain reasoning in making conclusions, controlling for all other predictors, is 4.2%. The unique contribution of perceived metacognition in measurement over ability to explain reasoning in making conclusions, controlling for all other predictors, is 0.9%. The unique contribution of perceived metacognition in data collection over ability to explain reasoning in making conclusions, controlling for all other predictors, is 7.5%. Results are summarized in Table 4 and 5.

Insert Table 5 here

 

            Outliers for the predictors are determined by calculating the cutting scores using the following formula: lc = [3(p+1)]/n where p is the number of predictors and n is the number of samples. The computed Leverage value (.085) exceeds the cutting score (.043) so the data set must be examined for the outliers and they must be removed from the data set. Since the studentized deleted maximum value is 3.232, exceeding three, the data set must also be examined for outliers in this component. The Cook’s distance (0.177) is well below one which indicates that there are no points of influential data. Outliers will be deleted from the data set and the statistical analysis for multiple regression will be completed again. Results are summarized in Table 6.

Insert Table 6 here

 

            Seven data points were found that exceeded the cutting score of .0433 in the Leverage value column. One data point was found that exceeded three for the studentized deleted value and was removed. Multiple regression model was again run for the clean data. Results from the cleaned data multiple regression in Table 7 shows evidence that there is statistically significant predictive relationship among attitude, perceived metacognition in observation, perceived metacognition in data collection, and perceived metacognition in measurement to predict ability to explain reasoning in making conclusions F(4, 264) = 115.07, p ‹ .001. The revised multiple regression equation is as follows: Y (hat) = .218x1 + .228x2 + .124x3 + .384x4 – 0.06, where x1 is attitude, x2 is perceived metacognition in observation, x3 is perceived metacognition in measurement and x4 is perceived metacognition in data collection. All of the predictors had p-values equal to or less than .005, showing that all of the predictors are statistically significant. Results are summarized in Table 8.

 

Insert Table 7 and 8 here

 

            The standardized coefficients show the relative influence of each variable. From most influential to least influential, the predictors are data collection (0.397), attitude (0.231), observation (0.206), and measurement (0.147). The percent overlap for all predictors and the ability to explain reasoning in making conclusions is 63.5%. The unique contribution of attitude over ability to explain reasoning in making conclusions, controlling for all other predictors, is 3.8%, found by squaring the semi-partial correlation. The unique contribution of perceived metacognition in observation over ability to explain reasoning in making conclusions, controlling for all other predictors, is 2.7%. The unique contribution of perceived metacognition in measurement over ability to explain reasoning in making conclusions, controlling for all other predictors, is 1.1%. The unique contribution of perceived metacognition in data collection over ability to explain reasoning in making conclusions, controlling for all other predictors, is 6.9%. Results are summarized in Table 8.

            To determine the overlap among the predictors a multiple regression model was run using attitude as the criterion variable and perceived metacognition in observation, perceived metacognition in data collection, and perceived metacognition in measurement as predictors. The results shown in Table 9 show that there is a statistically significant difference among the criterion and the predictor variables. Attitude has a small overlap with all of the other predictors: perceived metacognition in observation is 2.0%, perceived metacognition in data collection is 2.4% and perceived metacognition in measurement is 2.8%. Perceived metacognition in observation has a larger overlap with perceived metacognition in data collection, 9.0%, but has a small overlap with perceived metacognition in measurement, 0.3%. Perceived measurement in data collection has the largest overlap with perceived metacognition in measurement, 45.9%. Results are displayed in Table 10.

 

Insert Table 9 and 10 here

 

            Related to the second research question, are there differences between males and females in ability to explain reasoning for conclusions and do they depend on ethnic group, are the following null hypotheses: H01: (µAA + µH)/2 = µW  (x1), H02: µAA = µH  (x2), H03: µM = µF  (x3), H04:  x4 = x1x3, H05: x5 = x2x3.  There is no evidence at the population level the mean in the ability to explain reasoning in making conclusions differ for minority students and for white students, p = .990. Due to the high p-value, the first null hypothesis is not rejected. There is no evidence that the population mean in the ability to explain reasoning in making conclusions differs between African Americans and Hispanics, p = .249. Due to the high p-value, the second null hypothesis is not rejected. There is no evidence that the population mean in explaining reasoning in making conclusions differs in males and females, p = .075. The difference between the combined mean scores on the ability to explain reasoning in making conclusions for minorities does not depend on gender, p = .624. The differences in scores on the ability to explain reasoning for making conclusions for African Americans and Hispanics does not depend on gender, p = .121. Results are displayed in Table 11.

Insert Table 11 here

 

            Related to the third research question, is there a relationship between the ability to have metacognition about observation and the ability to have metacognition about data collection, a chi-square test was performed and analyzed. The test showed that there is a relationship between the ability to have metacognition about observation and the ability to have metacognition about data collection, c2 (128) = 283.887, p‹ .001. Results are displayed in Table 12.

Insert Table 12 here

 

Discussion

Summary

            Performing and analyzing multiple regression shows evidence that there is statistically significant predictive relationship among attitude, perceived metacognition in observation, perceived metacognition in data collection, and perceived metacognition in measurement to predict ability to explain reasoning in making conclusions. Once all outliers were eliminated from the data, the multiple regression equations is Y (hat) = .218x1 + .228x2 + .124x3 + .384x4, where x1 is attitude, x2 is perceived metacognition in observation, x3 is perceived metacognition in measurement and x4 is perceived metacognition in data collection. All of the hypothesized predictors demonstrated significance with the relative importance, from highest to lowest, being data collection, attitude, observation, and measurement in predicting the ability to explain reasoning in conclusions. The percent overlap for all predictors and the ability to explain reasoning in making conclusions is 63.5%, which shows to be significant. All of the predictors showed smaller amounts of unique contributions to the ability to explain reasoning in conclusions, less than 10%. The predictor for attitude had only approximately a 2% overlap with the other predictors. Observation had a larger overlap with data collection, but a very small overlap with measurement. The predictor of data collection had the largest overlap with measurement.

            There was no evidence that the ability to explain reasoning in making conclusions differ for minority students and for white students. There is also no evidence that the ability to explain reasoning in making conclusions differed among Hispanics and African American students.  Males and females also did not differ in their ability to explain reasoning in making conclusions. The analysis of data also showed that there were no interaction effects.

            There was statistically significant evidence that there is a relationship between the ability to be metacognitive in observing and the ability to be metacognitive in data collection. When students can think about the reasons they observe, they can also think about the reasons they organize the data in a particular way.

Implications for practice

            This study gives evidence that students can improve their ability to explain reasoning behind making conclusions by improving on the ability to be metacognitive about making observations, collecting data, measuring and improving their attitude about science. The processes of science that include observation, measurement, data collection, and generalizing in addition to attitude about science are closely linked, as shown by the multiple regression equation. Students could improve their ability to form valid conclusions by first thinking about their reasoning behind forming observations, choosing measurement tools, organizing data points, and having a positive attitude toward the discipline. Perhaps data collection had the highest contribution in the regression because the connections made in data collection form the basis for the generalizations made in concluding. Perhaps data collection and measurement had the most overlap because they are the two processes in science that are the most closely linked. Once measurements are taken, they must be interpreted in order to be placed in the data set.

            The results showed no differences among the different ethic and gender groups delineated in this study. African Americans, Hispanics and whites showed no differences in their ability to explain reasoning in making conclusions. Males and females also showed no differences in their ability to explain reasoning in making conclusions. This study suggests that the ability to be metacognitive does not depend on ethnicity or gender.

            The relationship shown in this study between the perceived ability to be metacognitive about observation and the perceived ability to be metacognitive about data collection could be a way for students to learn to be metacognitive. If students can begin to give reasons for why they made particular observations, it could help them think about how data organization must be established in order to make conclusions.

Limitations of the Study

            The sample in this study (n=277) was adequate, but was given to a very homogeneous population. The sample consisted of only eighth grade students who were mostly white. If a more minorities were included in the sample, there may have been some differences among the groups. Another limitation of the study was the survey construction. More questions needed to be asked in each area in order to obtain higher alpha subset scores. Because this study only examined perceptions of students, it was limited in its usefulness. Actual performance of the variables, rather than student perceptions of the variables, could lead to richer data.

Recommendations for Future Research

            Suggestions for future research include redesigning the survey to include more questions for each variable and distributing the survey to a more heterogeneous group. The survey could also be distributed to a more geographically diverse group, in order to get national trends. Also, rather than collecting perceptions of ability to explain conclusions, future researchers could ask students to make or evaluate conclusions from given data in order to find relationships between metacognition in observation, data collection, measurement and scientific reasoning behind conclusions.


References

Abd-El-Khalick, F., Bell, R. L., & Lederman, N. G. (1998). The nature of science and instructional practice: Making the unnatural natural. Science Education, 36, 404-420.

Akerson, V. L., & Adb-El-Khalick, F. (2003). Teaching elements of the nature of science: A yearlong case study of a fourth-grade teacher. Journal of Research in Science Teaching, 40, 1025-1049.

Akerson, V. L., Flick, L. B., & Lederman, N. D. (2000). The influence of primary children's ideas in science on teaching practice. Journal of Research in Science Teaching, 37, 363-385.

American Association for the Advancement of Science. (1993). Benchmarks for science literacy. New York: Oxford University Press.

Bell, R. L., & Lederman, N. G. (2000, April). Understanding of the nature of science and decision making on science and technology based issues. Paper presented at the meeting of the National Association for Research in Science Teaching.

Brown, A. (1987). Metacognition, executive control, self-regulation, and other more mysterious mechanisms. In F. E. Weinert & R. H. Kluwe (Eds.), Metacognition, Motivation and Understanding. Hillsdale, NJ: Laurence Erlbaum Associates, Publishers.

Bybee, R. W. (2004). Scientific inquiry and science teaching. In L. B. Flick & N. G. Lederman (Eds.), Scientific Inquiry and Nature of Science. Boston: Kluwer Academic Publishers.

Clough, M. P. (1997). Strategies and activities for initiating and maintaining pressure of students' naive views concerning the nature of science. International Journal of Science Education, 28, 191-204.

Dawson, R. E. (2000). Critical thinking, scientific thinking, and everyday thinking: Metacognition about cognition. Academic Exchange Quarterly.

Driver, R., Newton, P., & Osborne, J. (2000). Establishing the norms of scientific argumentation in classrooms. Science Education, 84, 287-312.

Duschl, R. A. (1990). Restructuring science education: The importance of theories and their development. New York: Teachers College Press.

Duschl, R. A., Hamilton, R. J., & Grandy, R. E. (1992). Psychology and Epistemology: Match or Mismatch When Applied to Science Education? In R. A. Duschl & R. J. Hamilton (Eds.), Philosophy of Science, Cognitive Psychology, and Educational Theory and Practice. Albany, NY: State University of New York Press.

Flavell, J. H. (1987). Speculations about the nature and development of metacognition. In F. E. Weinert & R. H. Kluwe (Eds.), Metacognition, Motivation and Understanding. Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers.

Glasson, G. E., & Bentley, M. L. (2000). Epistemological undercurrents in scientists' reporting of research to teachers. Science Education, 84, 469-485.

Hogan, K. (2000). Exploring a process view of students' knowledge about the nature of science. Science Education, 84, 51-70.

Lederman, N. G., Wade, P. D., & Bell, R. L. (1998). Assessing the nature of science: What is the nature of our assessments? Science and Education, 7, 595-615.

McComas, W. F. (2004). Seeking NOS Standards: What Content Consensus Exists in Popular Books on the Nature of Science. Paper presented at the meeting of National Association for Research in Science Teaching.

McComas, W. F., Almazroa, H., & Clough, M. P. (1998). The nature of science in science education: An introduction. Science & Education, 7, 511-532.

Mellado, V. (1997). Preservice teachers' classroom practice and their conceptions of the nature of science. Science & Education, 6, 331-354.

Ryer, J., Leach, J., & Driver, R. (1999). Undergraduate science students' images of science. Journal of Research in Science Teaching, 36, 201-219.

Southerland, S. A., Gess-Newsome, J., & Johnston, A. (2003). Portraying science in the classroom: The manifestation of scientists' beliefs in classroom practice. Journal of Research in Science Teaching, 40, 669-691.

Tobin, K., & McRobbie, C. J. (1997). Beliefs about the nature of science and the enacted science curriculum. Science & Education, 6, 355-371.

Zachos, P., Hick, T. L., Doane, W. E., & Seargent, C. (2000). Setting theorietical and empirical foundations for assessing scientific inquiry and discovery in educational programs. Journal for Research in Science Teaching, 37, 938-962.

Zeidler, D. L., Walker, K. A., Ackett, W. A., & Simmons, M. L. (2002). Tangled up in views: Beliefs in the nature of science ans responses to socioscientific dilemmas. Science Education, 86, 343-367.

 


Table 1

 

Demographic Information

 

                        White               African American          Hispanic           Diabilities

                        _______          ________________    ________        _________

 

Students           222                              19                    36                    50

 

Percent             80%                             7%                   13%                 18%

 

 

 

 

Table 2

 

Alpha Tests for Reliability

 

            Entire               Attitude            Observation      Measurement    Data                 Conclusions

 

            Survey                                                                                      Collection

            ______________________________________________________________________

 

            .8892               .6237               .4338               .6004               .6147               .7136

           

 

 

 

Table 3

 

Significance of Multiple Regression Equation

 

                                    Sum of             df         Mean               F                      Sig

                                   

                                    Squares                        Square

                                    _______          ___      _______          ________        __________

 

Regression                    126.107             4        31.527             118.992           .000

 

Residual                         72.066           272      0.265

 

Total                            198.172           276

 

 

 

 

 

 

Table 4

 

Coefficients

 

                        Unstandardized Standardized                t           Sig       Correlations

 

                        Coefficients                  Coefficients                                                     

                        ____________            ______________                                _________________

 

                        B          Standard          Beta                                                     Zero     Partial      Part

                                   

                                    Error

                        _________________________________________________________________

 

Attitude            .067     .159                 .206                             4.426   .000     .548     .282       .177

 

Observation      .195    .040                 .250                             4.844   .000     .609     .322       .205

 

Measurement    .122     .042                 .132                             2.646   .009     .603     .158       .097

 

Data                 .384     .051                 .402                             7.500   .000     .724     .414       .274

 

 

 

 

Table 5

 

Total Contributions of Predictors to Ability to Explain Reasoning in Conclusions

 

R                      R Square          Adjusted R Square                   Standard Error of the Estimate

 

.798                 .636                 .631                                         .51473

 

 

 

 

 

 

 

 

 

 

Table 6

 

Residuals Statistics

 

                        Minimum          Maximum         Mean   Standard Deviation                   N

 

                        _________________________________________________________

 

Studentized       -3.232              3.063               .000                 1.007                           277

           

Deleted

 

Cook’s             .000                 .177                 .004                 .012                             277

 

Distance

 

Centered          .000                 .085                 .014                 .012                             277

 

Leverage

 

 

 

 

 

Table 7

 

Significance of Clean Data Multiple Regression Formula

 

                                    Sum of             df         Mean               F                      Sig

                                   

                                    Squares                        Square

                                    _______          ___      _______          ________        __________

 

Regression                    117.712             4        29.428             115.069           .000

 

Residual                         67.516           264      0.265

 

Total                            185.228           268

 

 

 

 

 

 

 

Table 8

 

Coefficients for Clean Data

 

                        Unstandardized Standardized                t           Sig       Correlations

 

                        Coefficients                  Coefficients                                                     

                        ____________            ______________                                _________________

 

                        B          Standard          Beta                                                     Zero     Partial      Part

                                   

                                    Error

                        _________________________________________________________________

 

Attitude            .218     .042                 .231                             5.248   .000     .574     .307       .195

 

Observation      .228    .052                 .206                             4.392   .000     .603     .261       .163

 

Measurement    .124     .044                 .147                             2.843   .005     .616     .172       .106

 

Data                 .384     .054                 .397                             7.080   .000     .726     .399       .263

 

 

 

 

 

 

Table 9

 

Significance of Variable Uniqueness

 

 

                                    Sum of             df         Mean               F                      Sig

                                   

                                    Squares                        Square

                                    _______          ___      _______          ________        __________

 

Regression                    58.934               3        19.645             35.179             .000

 

Residual                       147.980          265      0.558

 

Total                            206.914           268

 

 

 

 

Table 10

 

Correlations and Percent Overlaps Among Predictors

­­­­­­­­­­­­­­­­­­­­­­­­­­

 

                        Unstandardized Standardized                t           Sig       Correlations

 

                        Coefficients                  Coefficients                                                     

                        ____________            ______________                                _________________

 

                        B          Standard          Beta                                                     Zero     Partial      Part

                                   

                                    Error

                        _________________________________________________________________

 

Observation      .246    .075                 .210                             3.269   .001     .429     .197       .170

 

Measurement    .187     .063                 .211                             2.958   .003     .450     .179       .154

 

Data                 .215     .079                 .210                             2.718   .007     .475     .165       .141

 

 

 

 

 

Table 11

 

p-values for Dummy Coding Results

 

Null Hypothesis                                                            p-value

_____________________________                          _______________________________

 

:H01: (µAA + µH)/2 = µW  (x1)                                          .990

 

H02: µAA = µH  (x2)                                                         .249

 

H03: µM = µF  (x3)                                                           .075

 

H04:  x4 = x1x3                                                                                    .624

 

H05: x5 = x2x3.                                                   .121

 

 

 

 

Table 12

 

Chi-Square Tests

 

                                    Value                           df                                 Sig.

                        ________________________________________________________________

 

Pearson Chi-Square     283.887                       128                              .000

 

Likelihood Ratio           213.090                       128                              .000

 

Linear-by-                    90.070                            1                               .000

 

Linear Assoc.  

 

N of Valid Cases          269

 

 


Appendix 1

Using Aspects of the Nature of Science as a Metacognitive Tool

 

 

 

Nature of Science

 

Science as a discipline has a unique way of knowing, otherwise known as the nature of science. Scientists can think creatively and depend on empirical evidence to argue their points. Scientific knowledge is subject to change, but this does not make prior knowledge any less rigorous. Science knowledge is viewed with regard to the historical and social context since it is a human product. Scientifically literate people look for patterns in phenomena that can be later generalized.

The aspects of the nature of science can be useful in helping students to think about their epistemology. Examining the nature of science can supply characteristics that distinguish science from other ways of knowing and explicitly help students scrutinize their rationale in forming ideas. Teachers can utilize these characteristics in their lessons to help students to examine the information they know and think about how student knowledge is scientific.

 

 

 

 

 


Appendix 2

Survey Instrument

 

                                                            Gender ___________

Ethnic Group _____________

Thinking Scientifically Survey

When reading this survey, consider all of the experiences you had in all of your science classes, not just the class you are presently taking. Please write your gender and ethnic group in the upper right hand corner.

  • Read the statement
  • Circle the number that best describes how you feel about the statement.

 

  1. I enjoy being in science class.

Disagree with statement                  Neutral about statement                        Agree with statement

1                      2                                  3                                  4                                  5

 

  1. I think about how I learn in science class.

Disagree with statement                  Neutral about statement                        Agree with statement

1                      2                                  3                                  4                                  5

 

  1. I do well in science class.

Disagree with statement                  Neutral about statement                        Agree with statement

1                      2                                  3                                  4                                  5

 

  1. When I make an observation, it is clear and understandable to other people.

Disagree with statement                  Neutral about statement                        Agree with statement

1                      2                                  3                                  4                                  5

 

  1. When I complete a lab in science, I think about how the lab could be improved.

Disagree with statement                  Neutral about statement                        Agree with statement

1                      2                                  3                                  4                                  5

 

  1. When I classify objects in science, I think about how my classification system compares to other students’ classification systems.

Disagree with statement                  Neutral about statement                        Agree with statement

1                      2                                  3                                  4                                  5

 

  1. When I measure objects in science, I think about possible errors I could make when making measurements

Disagree with statement                  Neutral about statement                        Agree with statement

1                      2                                  3                                  4                                  5

 

 

 

  1. I like asking questions in science.

Disagree with statement                  Neutral about statement                        Agree with statement

1                      2                                  3                                  4                                  5

 

9.                  When I record data in science, I can understand what I did, even weeks after I gathered the data.

Disagree with statement                  Neutral about statement                        Agree with statement

1                      2                                  3                                  4                                  5

 

  1. When I make a conclusion for an experiment, I think about what observations might be the most effective to make my point.

Disagree with statement                  Neutral about statement                        Agree with statement

1                      2                                  3                                  4                                  5

 

  1. When I make observations, I think about all possible perspectives, not just the obvious ones.

Disagree with statement                  Neutral about statement                        Agree with statement

1                      2                                  3                                  4                                  5

 

  1. When I draw conclusions in an experiment, I think about what scientists have done on this topic.

Disagree with statement                  Neutral about statement                        Agree with statement

1                      2                                  3                                  4                                  5

 

  1. When given an assignment, I can see how it is a building block to bigger ideas.

Disagree with statement                  Neutral about statement                        Agree with statement

1                      2                                  3                                  4                                  5

 

  1. I can usually see patterns in my experiment results.

Disagree with statement                  Neutral about statement                        Agree with statement

1                      2                                  3                                  4                                  5

 

  1. When I organize data, I first think about the best way to explain what I am trying to show.

Disagree with statement                  Neutral about statement                        Agree with statement

1                      2                                  3                                  4                                  5

 

  1. I think about how my measurements help me explain an idea in an experiment.

Disagree with statement                  Neutral about statement                        Agree with statement

1                      2                                  3                                  4                                  5

 

 

 

 

 

 

Appendix 3

 

SPSS Printouts