Scientific reasoning, conceptual knowledge, & achievement differences between prospective science teachers having a consistent misconception and those having a scientific conception in an argumentation-based guided inquiry course

Learning and Individual Differences 30 (2014) 148–154

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Learning and Individual Differences journal homepage: www.elsevier.com/locate/lindif

Scientific reasoning, conceptual knowledge, & achievement differences between prospective science teachers having a consistent misconception and those having a scientific conception in an argumentation-based guided inquiry course Ömer Acar ⁎ Kocaeli University, Department of Primary Education, College of Education, Umuttepe Campus, İzmit, Turkey

a r t i c l e

i n f o

Article history: Received 18 July 2013 Received in revised form 12 November 2013 Accepted 6 December 2013 Keywords: Prospective science teachers Argumentation Inquiry Scientific reasoning Conceptual knowledge Achievement

a b s t r a c t This study examined scientific reasoning, conceptual knowledge, and achievement differences between prospective science teachers who had a consistent misconception and those who had a scientific conception in an argumentation-based guided inquiry physics course. Results showed that there were scientific reasoning, situational knowledge and achievement differences between the two groups at the beginning of instruction. However instruction helped these groups reduce the situational knowledge and achievement gaps. On the other hand, scientific reasoning gap still existed after the instruction. Both groups developed their scientific reasoning, declarative knowledge, and situational knowledge during the course. In light of these results, the author recommends that research can use a categorization, which is having a consistent misconception or scientific conception, to examine the effect of instruction by comparing learning gains of these two groups. In addition the author recommends that argumentation-based guided inquiry approaches should be incorporated into science curriculum in early education years. © 2013 Elsevier Inc. All rights reserved.

1. Introduction Inquiry-based learning environments have been favored to traditional learning environments in that students are supposed to be their own learning agents in these contexts. Ideally, students in inquiry-based learning environments should be fostered to reason between alternatives, explain the phenomena, and consequently construct their learning (Kuhn, 1993; Lawson, 2003). However studies show that student argumentation, which is a process of evidence-based reasoning between alternative theories, is not sufficient in inquiry learning environments (Kelly, Druker, & Chen, 1998; Watson, Swain, & McRobbie, 2004). To improve student poor argumentation, previous studies on argumentation provided argumentation-based instructional contexts. Encouraging results were obtained with regard to enhancement of argumentation and conceptual understanding (Osborne, Erduran, & Simon, 2004; Zohar & Nemet, 2002). As the most important hypothesis that can be drawn from inquiry approach is students learn better because they can construct their own learning, studies tested this assumption by mostly comparing low and high achievers' performance in control and experimental groups that received traditional and inquiry teaching respectively (Akkus, Gunel, & Hand, 2007; Geier et al., 2008; Huppert, Lomask, & ⁎ Tel.: +90 544 8197700. E-mail address: [email protected]. 1041-6080/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.lindif.2013.12.002

Lazarowitz, 2002; Lewis & Lewis, 2008; Liao & She, 2009; Wilson, Taylor, Kowalski, & Carlson, 2010). Results of these studies showed that learning gains of students go higher levels in inquiry classes compared to traditional classes (Akkus et al., 2007; Geier et al., 2008; Huppert et al., 2002; Lewis & Lewis, 2008; Liao & She, 2009). In addition they found race (Wilson et al., 2010), gender (Geier et al., 2008) and conceptual knowledge gap (Akkus et al., 2007) can be closed in inquiry learning contexts. However the results are elusive in obtaining the equity among low and high Scholastic Aptitude Test (SAT) scorers (Lewis & Lewis, 2008) and different scientific reasoners (Liao & She, 2009). In the case of argumentation-based inquiry contexts, several studies tested the effect of argumentation instruction by comparing learning gains of students in traditional instruction and argumentation-based inquiry instruction (Osborne et al., 2004; Zohar & Nemet, 2002). These studies found encouraging results regarding student argumentation and conceptual knowledge in favor of argumentation instruction. On the other hand, only a study by Zohar and Dori (2003) compared learning gains of low and high achievers receiving argumentation-based inquiry instruction. Although results of this study indicated both high and low achievers had significant reasoning gains after the instruction, no consistent result was found for the closure of the reasoning gap between these groups. Reviewed literature shows comparison of learning gains of students with different achievement levels is new to argumentation research. In addition, although efforts were undertaken to incorporate argumentation

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to inquiry courses in college level (e.g., Zembal-Saul, Munford, Crawford, Friedrichsen, & Land, 2002), paucity of study exists in this context which explored the effect of argumentation intervention on student learning gains (e.g., Kaya, 2013; Nussbaum & Sinatra, 2003). Exploring the effect of argumentation on students' learning gains is more important especially in prospective science teacher education programs because research draws attention to equipping teachers with argumentation in their early education careers (Osborne, Simon, Christodoulou, HowellRichardson, & Richardson, 2013; Simon, Erduran, & Osborne, 2006; Zohar & Schwartzer, 2005). The present study aimed to examine learning gains of low and high achieving prospective science teachers in an argumentation-based guided inquiry course. 2. Literature review 2.1. Achievement gap in inquiry and argumentation instruction Studies which focused on the comparison of student learning in middle school science inquiry-based and common place teaching pointed out a success of students, who were taught in inquiry-based contexts, across a range of learning gains (Geier et al., 2008; Wilson et al., 2010). In addition studies demonstrated that achievement gap between races (Johnson, 2009; Wilson et al., 2010) and genders (Geier et al., 2008) lessened after receiving inquiry instruction. However for college level, only one study found in the literature which aimed to examine the effectiveness of inquiry-based teaching over traditional instruction. Undergraduate students enrolled to a chemistry class were either taught using a traditional or a peer led guided inquiry instruction in this study (Lewis & Lewis, 2008). SAT scores were used to identify students with different achievement levels. Results indicated that students in inquiry outperformed students in traditional learning on a final exam. In addition, analyses showed final exam scores of students in inquiry were still significantly dependent on student SAT scores indicating inequity between low and high achievers after the instruction. Studies focused on argumentation, on the other hand, compared learning gains of students in argumentation-based instruction and students in common place instruction. Findings of these studies stated student argumentation and conceptual knowledge better developed in argumentation-based instructional contexts (Osborne et al., 2004; Zohar & Nemet, 2002). Only a study by Zohar and Dori (2003) found in the literature which analyzed learning gains of students from diverse achievement levels in an argumentation-based inquiry instruction. This study reported the results of data obtained from four different studies. Results of these studies indicated that students who received argumentation instruction outperformed their peers who received traditional instruction with regard to reasoning skills. Furthermore both low and high achievers gained from argumentation instruction regarding reasoning skills. It is clear that paucity of study exists in argumentation which focused on comparison of student learning gains from different achievement levels. Furthermore, although argumentation-based instructional contexts were provided to prospective science teachers, neither experimental design nor examination of students with different achievement levels was utilized in these studies (e.g., Acar, 2008; Zembal-Saul et al., 2002). Thus we have no clue about the effectiveness of argumentation-based instruction with this population group. Examination of this research population is necessary because equipment of teachers with argumentation skills in their education years is essential for their pedagogic performance in actual practice (Osborne et al., 2013; Simon et al., 2006; Zohar & Schwartzer, 2005). 2.2. Scientific reasoning, conceptual knowledge, achievement and misconceptions Since inquiry learning has been viewed as student exploration and discovery of scientific concepts using scientific methodology, it has

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been assumed that student scientific reasoning should develop in these settings (Daempfle, 2006). Two research lines that differed on their view of what constitutes of scientific reasoning examined if scientific reasoning can be enhanced through inquiry instruction. The first research line viewed scientific reasoning as a process involved in the construction of evidence based arguments. Within this research tradition, by arguing between different alternative positions namely argumentation, development of conceptual knowledge and reasoning skills is possible (Kuhn, 1993; Kuhn, Schauble, & Garcia-Mila, 1992). Curriculum materials and instruction have been designed in a way to promote student argumentation in this research. Results showed that student argumentation and conceptual knowledge may be enhanced in argumentation-based inquiry science classrooms (Acar, 2008; Martin & Hand, 2009; Osborne et al., 2004; Zohar & Nemet, 2002). The second line of research viewed scientific reasoning as constituting of reasoning skills that are content independent but dependent on developmental stages. That is to say, according to this approach to scientific reasoning, one's performance of scientific reasoning skills in a domain, e.g., control of variables, proportional reasoning, combinatorial reasoning, hypothetical reasoning, does not depend on domain specific content knowledge but depends on his developmental stage. Results of these studies showed that students' misconception level (Lawson & Weser, 1990; Lawson & Worsnop, 1992), their conceptual knowledge (Ates & Cataloglu, 2007; Coletta & Philips, 2005) and achievement (Johnson & Lawson, 1998) in a science course can be predicted by their scientific reasoning ability. That is to say, these studies found high scientific reasoners had fewer misconceptions and gained higher conceptual knowledge, and achievement. Finally, they found student scientific reasoning skills can be enhanced through inquiry-based instruction (Johnson & Lawson, 1998; Liao & She, 2009). Although these two research lines show reasoning skills are an important factor in explaining student learning, no special attention was given to compare gains of students with different pre-instructional reasoning abilities in inquiry-based instructions. In the literature, different criteria were used to group students to low and high achievers. Specifically, Akkus et al. (2007) used student scores on a baseline science test, Lewis and Lewis (2008) used SAT scores and Zohar and Peled (2008) used student past academic achievement. In addition, Geier et al. (2008) and Johnson (2009) examined the gender and race achievement gap respectively. If reasoning ability is a good predictor of a student's achievement in an inquiry science course (Johnson & Lawson, 1998), then an inquiry instruction that aims to enhance reasoning skills should take into account students' pre-instructional reasoning abilities. A study by Zohar and Dori (2003) investigated gains of low and high achievers in response to an inquiry instruction in which critical thinking and argumentation were incorporated. However authors categorized students based on their general academic achievement rather than their reasoning skills before instruction. Based upon the findings of the literature (i.e., Lawson & Weser, 1990; Lawson & Worsnop, 1992), it is hypothesized in this research that students with a consistent misconception would have low level of scientific reasoning and vice versa. Furthermore it is hypothesized that students with high scientific reasoning would also have high achievement (e.g., Johnson & Lawson, 1998). If these hypotheses are demonstrated then if an argumentation-based inquiry course would help to close the gap between students who have a consistent misconception and students who have a scientific conception can be investigated. To examine these hypotheses, following research questions were sought: 1. What are the scientific reasoning, conceptual knowledge and achievement characteristics and differences between prospective science teachers having a misconception and prospective science teachers having a scientific conception before an argumentationbased guided inquiry course?

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2. Is this argumentation-based guided inquiry course helpful in closing any gaps between these groups? 3. Do both groups develop their scientific reasoning and conceptual knowledge during the instruction?

3. Research context 3.1. Participants Initial sample consisted of 125 prospective science teachers enrolled to an introductory physics course in a mid-western US university. Mostly these students enrolled to this course to fulfill their science credit requirements for graduation. Since only 76 students completed all the instruments used and were able to be categorized under prospective science teachers having a consistent misconception (PROSTEHAM) and prospective science teachers having a scientific conception (PROSTEHAS), results were reported for this sub-sample. At the beginning of the instruction, an argumentation assessment was administered. Two hypothetical students were presented as supporting two alternative explanations related to the mechanism underlying how objects balance in this assessment. One of the hypothetical students was supporting a naïve and the other was supporting a scientific explanation. The naïve explanation was derived from a past research that focused on clarification of undergraduate student responses to written argumentation tests about balancing concept by the use of semistructured interviews. Scientific explanation was constructed by avoiding scientific terminology and using everyday language in order to prevent the effect of wording. Hypothetical students' explanations about balancing can be seen in Table 1. Students were asked to make an argument in favor of one of the hypothetical student explanations or construct another argument in case they did not agree with any of them. Everyday life evidence was also provided. Then students' arguments, counter-arguments and rebuttals were encouraged. For the research aim, students who constructed another explanation than these two were excluded from data. Moreover only students who constructed arguments and rebuttals with referring to data in their arguments were selected. Then students were grouped under having a consistent misconception and a scientific conception for the final version of the data. Examples of student explanations can be seen in Table 2. Accordingly final sample consisted of 35 PROSTEHAM and 41 PROSTEHAS. 3.2. Instruction Students did guided experiments and exercises in Physics by Inquiry (PbI) textbook 1 (McDermott, 1996) during 10 weeks of instruction. Students met twice a week and worked in small groups of three to four members. Each class session lasted in 3 h. Mass, volume, density, buoyancy, heat, and temperature concepts were taught in the course. There were 8 instructors in the course: one physics professor, two instructors, two teaching assistants and three undergraduate students who had taken the course in previous years. Instructors met once a

Table 2 Examples of student explanations showing misconception and scientific conception of balancing. A student's explanation showing a consistent misconception

A student's explanation showing a scientific conception

Argument I agree with student A. In order to balance, masses must be equal on both sides of the balance. The placement of the fulcrum is important. We see in the ruler example and the cup example that when two sides have equal masses the fulcrum is placed in the center. However when one side is more massive than the other side we see, as in the case with the baseball bat, the fulcrum is placed closer to the more massive side.

Rebuttal

I agree with student B. Since not all objects will be equal in masses, this student recognizes the fact that symmetry does not guarantee balancing. In these observations the fulcrum is moved to compensate for the differences in mass. This is done by moving the person's finger on the bat and having the people sit closer to the fulcrum in observation 3 and 4 (referring to data of a baseball bat balancing on a person's finger and three people balancing a huge cup on a seesaw). Student A focuses on symmetry but According to student B's explanation it would mean that the not all symmetrical things need to be equal in mass. While this works side with the people is more massive (referring to a balance of 3 for observations 1 and 2 (referring to data of a tightrope walker people sitting on one side of the balancing on a rope and a ruler seesaw and a huge cup sitting on balanced on a person's finger), if the other side). However the 3 we had symmetrical items on the people are actually equal to the ends of the ruler that differed in mass of the cup which we can mass, they wouldn't balance. observe by the balance.

week to discuss the content which will be taught in the following week. Instructors mostly discussed how to better scaffold student learning and reasoning in these meetings. Instruction had guided inquiry and argumentation aspects. For the former aspect, students were required to construct scientific concepts on their own with appropriate reasoning skills. In accomplishing this aim, mostly learning cycle teaching method was utilized. More clearly, first students did guided experiments and exercises in PbI textbook for a scientific concept that will be taught. Second students were introduced to this concept and finally they were required to apply their learning to novel situations. For the latter aspect, students were intended to justify their arguments with appropriate warrants and evidence when they were doing the experiments and exercises. In addition students were given four written and two oral argumentation exercises to develop their argumentation. These exercises were constructed with the use of the competing theories strategy (Bell & Linn, 2000; Osborne et al., 2004). Hypothetical students were provided as supporting alternative explanations about balancing and buoyancy concepts in these exercises. In addition evidences regarding these concepts were provided. Then students were encouraged to construct their arguments, counterarguments, and rebuttals. Instructors checked both guided inquiry and argumentation activities. Instructors did not provide a direct feedback to students at these encounters. Rather they guided student learning and reasoning with prompting questions. 3.3. Instruments

Table 1 Hypothetical student explanations about balancing. Student A

Student B

Masses should be equal on both sides of the balance. If the object is symmetric, then the fulcrum is at the center which makes the two sides equal and balanced. If the object is asymmetric, then the fulcrum gets closer to the more massive part making both sides of the balance have equal masses.

Balancing depends on the distance of the sides from the fulcrum and the masses on each side. If the mass of one side is bigger than the other side then that side should have less distance from the fulcrum compared to the other side.

3.3.1. Scientific reasoning test To assess student scientific reasoning skills, revised version of CTSR (Lawson, 1978, 2000) was administered as pre and posttest. This test consisted of 12 two-tier multiple choice items assessing reasoning skills such as conservation of mass, control of variables, proportional reasoning, correlational reasoning, probabilistic reasoning, combinatorial reasoning, and hypothetical reasoning. Each item consisted of two-tier questions: a content and a reasoning question. Student responses were coded 1 for each test item if students answered both content and reasoning questions correctly. Otherwise 0 was coded for student responses because reasoning questions were not independent questions

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but rather dependent on content questions. Cronbach's alpha estimate of internal consistency for 12 items was found as .70 for the pretest (n = 121). In addition, Cronbach's alpha was found as .68 for the posttest (n = 118). 3.3.2. Conceptual knowledge test This test was used as pretest and posttest to assess student conceptual knowledge development related to the concepts taught in the course. There were 16 items with regard to the concepts of mass, volume, density, balancing, uncertainty, buoyancy, interpretation of algebraic expressions and graphs, heat, and temperature. Pretest administration of this test to 125 students resulted in .47 Cronbach's alpha estimate of internal consistency. Besides, posttest administration of this test to 116 students resulted in .55 Cronbach's alpha. Principal component analysis (PCA) was performed on posttest to determine if there is any subscale existed within the test. PCA was performed only on posttest because student conceptual knowledge might have been fragmented at the pretest since they may not have been so much familiar with the concepts before instruction. PCA showed two subscales existed within the test. However 5 items did not contribute to either of the subscales. After removing these items, Cronbach's alpha was computed as .60 for the first subscale which consisted of 4 items and .47 for the second subscale which consisted of 7 items. These two subscales explained 27.24% of the total variance. The author of this paper analyzed the items in each subscale and recognized that items that loaded on the first subscale were recall questions, i.e., questions were similar to the ones students did in the class. Furthermore he realized that items that loaded on the second subscale were transfer questions, i.e., these questions required application of knowledge to novel situations. A physics professor who was the principal instructor of the course was asked to categorize the items under recall and transfer questions. His categorization was consistent with the result of the PCA for all the items except than one item which was about heat and temperature. A discussion was held between the author and this faculty about this item. After this discussion, physics faculty agreed that this item has also transfer features. As a conclusion, this item was included in the factor that constituted transfer questions. Following a study by de Jong and Ferguson-Hessler (1996), first subscale was named as declarative knowledge, i.e., the knowledge that includes recalling facts or formulas. The second subscale was named as situational knowledge, i.e., the knowledge that includes application of knowledge to novel situations. Items' loading on each subscale can be seen in Tables 3 and 4. To examine the predictive validity of the conceptual knowledge test, correlations were computed between the pretest subscales and student final grades. Result showed that both declarative knowledge and situational knowledge pretest subscales significantly correlated with student final grades (r = .19, p b .05; r = .25, p b .01; n = 117, respectively). 3.3.3. Achievement measures The final grade was a weighted average of three midterm exams and other assignment grades. Other assignments included homework, weekly journal entries and question of the day. During 8 weeks of the instructional period, students answered to conceptual questions, named as homework assignment, weekly about the concepts they learned previous Table 3 Items that loaded on the first principal component for the conceptual knowledge posttest (Acar, 2008, p. 62). Item

Loading

Knowledge

Cognitive process

3 4 5

.70 .68 .67

Balancing Uncertainty Conservation of mass

7

.52

Volume

Applying m1 ∗ d1 = m2 ∗ d2 equation Finding the range of uncertainty Recalling that mass conserves and volume can change Applying m/d = v

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Table 4 Items that loaded on the second principal component for the conceptual knowledge posttest (Acar, 2008, p. 63). Item Loading Knowledge 12

.65

11

.58

15

.52

2

.42

1

.42

13

.40

10

.32

Cognitive process

Mass vs. volume graph and density Sinking & floating and density

Using m/v for a heterogeneous object and interpretation of mass vs. volume graph Reasoning involves sinking and floating behavior of a heterogeneous object will depend on density of its component objects Heat and Contrast of 1 g vs. whole object's heat temperature and temperature by applying heat and temperature knowledge Conservation of mass Application of conservation of mass knowledge to a place where gravity is different Balancing Application of moment knowledge to a seesaw where fulcrum is not in the middle Volume, mass Interpretation of volume vs. mass graph using mass and volume knowledge Sinking & floating Reasoning that sinking and floating behavior and density of two objects will depend on objects' and liquids' densities

week. Students reflected on their journals four times during the course about the questions evolved around their opinion of their learning in the course. Question of the day assignment was administered in class and reviewed the concepts students learned in previous sections of the instruction. Only first midterm and final grades were selected to represent the start and end achievement measures. Pearson correlations were computed between achievement measures and scientific reasoning pretest scores. Results showed that first midterm and final grade had significant correlations with scientific reasoning scores (r = .43, p b .001; r = .47, p b .001; n = 119, respectively). 4. Results 4.1. R.Q. 1: What are the scientific reasoning, conceptual knowledge and achievement characteristics and differences between prospective science teachers having a misconception and prospective science teachers having a scientific conception at the beginning of the instruction? Pretest and posttest mean and standard deviations of scientific reasoning, declarative and situational knowledge, and achievement can be seen in Table 5. As can be seen, scientific reasoning of prospective science teachers having a misconception (PROSTEHAM) seems to be lower than that of prospective science teachers having a scientific conception (PROSTEHAS) (M = 6.17, SD = 2.18; M = 8.00, SD = 2.17 respectively). In addition PROSTEHAM's situational conceptual knowledge scores are below than PROSTEHAS's (M = 1.49, SD = 1.09; M = 2.15, SD = 1.59 respectively). Moreover achievement scores of PROSTEHAM are lower than that of PROSTEHAS (M = 83.67, SD = 12.21; M = 90.43, SD = 8.76 respectively). However declarative knowledge scores of PROSTEHAM and PROSTEHAS seem to be similar (M = 1.14, SD = .81; M = 1.17, SD = .89 respectively). To examine a possible scientific reasoning difference before instruction between PROSTEHAM and PROSTEHAS, analyses of variance (ANOVA) was performed on student scientific reasoning pretest scores. For this analysis, student categorization, i.e., students having a misconception or a scientific conception, was the fixed factor and scientific reasoning pretest scores were the dependent variable. Results showed that PROSTEHAS scored higher than PROSTEHAM on scientific reasoning pretest scores (F (1, 74) = 13.39, p b .001). In the next analysis, multivariate analyses of variance (MANOVA) was performed on student conceptual pretest declarative and situational subscale scores. Student categorization was the fixed factor and knowledge subscale scores were the dependent variables in this analysis. Result showed that PROSTEHAM and PROSTEHAS did not differ on the set

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Table 5 Descriptive statistics of scientific reasoning, declarative knowledge, situational knowledge, and achievement of PROSTEHAS and PROSTEHAM at pretest and posttest. PROSTEHAS pretest

Scientific reasoning Declarative knowledge Situational knowledge Achievement a a

PROSTEHAS posttest

PROSTEHAM pretest

PROSTEHAM posttest

M

SD

M

SD

M

SD

M

SD

8.00 1.17 2.15 90.43

2.17 .89 1.59 8.76

8.71 3.73 3.90 94.16

1.83 .71 1.55 3.19

6.17 1.14 1.49 83.67

2.18 .81 1.09 12.21

7.31 3.63 3.51 91.34

2.41 .73 1.70 4.57

The first midterm and the final grades were pretest and posttest achievement measures respectively.

of pretest subscale scores (Wilks' λ was utilized, F (2, 73) = 2.19, p N .05). Follow-up ANOVA results showed that PROSTEHAS performed higher than PROSTEHAM on pretest situational knowledge subscale (F (1, 74) = 4.30, p b .05). However no significant difference between groups was found at pretest declarative knowledge subscale (F (1, 74) = .02, p N .05). Initial achievement measure was the first midterm which was administered at the 4th week of the instruction. ANOVA was performed on student first midterm scores with student categorization as being the factor. Result demonstrated that PROSTEHAS scored higher than PROSTEHAM (F (1, 74) = 7.83, p b .01). In sum, several differences were found between PROSTEHAS and PROSTEHAM. More clearly PROSTEHAS scored higher than PROSTEHAM on scientific reasoning, situational conceptual knowledge, and achievement measures.

Was the achievement gap between PROSTEHAM and PROSTEHAS at the final grade lower than the gap at the first midterm? This inquiry would reveal if this argumentation-based inquiry course helped to lessen the achievement gap between these groups during the instruction time. MANOVA with repeated measures was performed for this purpose. Time was the within-subject factor, student categorization was the betweensubject factor, and first midterm and final grades were the dependent variables in this analysis. Results showed a significant effect of time on the set of dependent variables (F (1, 74) = 41.44, p b .001). In addition the interaction term between time and student categorization was found to be significant (F (1, 74) = 4.94, p b .05). Since both time and conception type had two levels and keeping in mind the descriptive statistics given in Table 5, this result demonstrates that the achievement gap between these groups at the final grade was statistically lower than the gap at the first midterm.

4.2. R.Q. 2. Is this argumentation-based guided inquiry course helpful in closing any gap between these groups?

4.3. R.Q. 3: Do both groups develop their scientific reasoning and conceptual knowledge during the instruction?

To examine if the scientific reasoning difference between these two groups still existed at the scientific reasoning posttest, ANOVA was performed. For this analysis, student categorization was the fixed factor and scientific reasoning posttest score was the dependent variable. Result showed that PROSTEHAS still scored higher than PROSTEHAM (F (1, 74) = 8.17, p b .01). Results showed that PROSTEHAS scored higher than PROSTEHAM both on scientific reasoning pretest and posttest. Although there was a significant difference between these groups at the posttest, instruction may have helped to reduce the gap occurred at the pretest through the posttest. To investigate this inquiry, MANOVA with repeated measures was performed. For these analyses, scientific reasoning pre and posttest scores were the dependent variables, time was the withinsubject factor and student categorization was the between-subject factor. Results showed that within-subject factor time had a significant effect on the set of dependent variables (Wilks' λ was utilized, F (1, 74) = 18.92, p b .001). On the other hand, it was found that the interaction term between time and student categorization was not significant (F (1, 74) = 1.05, p N .05). Taking into account both time and student categorization had two levels, this result implies that scientific reasoning difference of PROSTEHAS and PROSTEHAM at the pretest was not different from the difference of these groups at the posttest. To investigate if there is any conceptual knowledge difference existed between these groups at the posttest, MANOVA was performed on the conceptual knowledge posttest subscale scores. Results showed that student categorization did not make any difference on the set of posttest subscale scores (Wilks' λ was utilized, F (2, 73) = .79, p N .05). In fact follow-up ANOVA results showed PROSTEHAS and PROSTEHAM did not differ for their scores on both declarative knowledge subscale (F (1, 74) = .39, p N .05) and situational knowledge subscale (F (1, 74) = 1.08, p N .05). Another ANOVA was performed on student final grades to investigate whether there was an achievement difference between the groups at the end of the course. Result showed that final grades of PROSTEHAS were significantly higher than those of PROSTEHAM (F (1, 74) = 9.94, p b .005).

Separate pair-wise t tests were performed to examine if both PROSTEHAM and PROSTEHAS increased scientific reasoning scores from pretest to the posttest. Results showed that PROSTEHAM increased their scientific reasoning scores significantly (t = 3.28, p b .005). PROSTEHAS also developed these scores during instruction (t = 2.75, p b .01). To investigate the development of conceptual knowledge subscale scores from pretest to the posttest, separate pair-wise t tests were performed. Results showed that PROSTEHAM enhanced their declarative and situational knowledge subscale scores (t = 14.15, p b .001; t = 7.68, p b .001, respectively). Moreover PROSTEHAS also developed declarative and situational knowledge subscale scores (t = 13.85, p b .001; t = 5.63, p b .001, respectively). 5. Discussion Scientific reasoning, conceptual knowledge and achievement differences were found between prospective science teachers having a misconception (PROSTEHAM) and prospective science teachers having a scientific conception (PROSTEHAS). More clearly results showed that PROSTEHAS scored significantly higher than PROSTEHAM on scientific reasoning, situational knowledge, and achievement at the beginning of the instruction. These results imply that the difference between PROSTEHAM and PROSTEHAS was not trivial. On the contrary, the findings suggest that the difference between PROSTEHAM and PROSTEHAS can be explained by scientific reasoning, conceptual knowledge, and achievement which are cornerstone variables in science education. These results are in alignment with the scientific reasoning literature. The literature shows more skilled scientific reasoners hold fewer misconceptions than the less skilled scientific reasoners (Lawson & Weser, 1990). However the students were grouped under PROSTEHAM and PROSTEHAS just for their comprehension about one concept in this study. Then, the question of how comprehension about one concept could make such a difference poses challenge. The author thinks that both naïve and scientific explanations of balancing provided a mechanism of how objects balance which had an explanatory power on

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evidence. Thus these explanations could not be labeled as just misconceptions or scientific conceptions but theory-like structures in which conceptions are organized. Needless to say if one has a low level of conceptual understanding on an issue, he/she would have several misconceptions about that issue. However no effort has been taken in the literature to investigate the relation of having a misconception with different types of conceptual knowledge. Results in this study indicated that PROSTEHAS's situational conceptual knowledge, i.e., knowledge related to transfer of procedural knowledge, was higher than that of PROSTEHAM. On the other hand, the literature on scientific reasoning shows a positive significant relation between scientific reasoning and achievement (Coletta & Philips, 2005; Johnson & Lawson, 1998). From this perspective, if PROSTEHAS's scientific reasoning scores were higher, which was found in this study, then their achievement should have been higher. The achievement result of PROSTEHAS found in this study supported this expectation. For the second research question, it was inquired whether scientific reasoning, situational knowledge, and achievement gaps between PROSTEHAS and PROSTEHAM would close at the end of the course. Results regarding scientific reasoning showed PROSTEHAS still scored higher than PROSTEHAM at the posttest. In depth analysis also indicated the scientific reasoning gap between PROSTEHAS and PROSTEHAM did not lessen from the pretest to the posttest. In sum, it can be concluded that the instruction did not help to close the scientific reasoning gap between these groups. Results regarding situational conceptual knowledge demonstrated no posttest situational knowledge difference. Considering pretest situational knowledge difference, it can be inferred that the situational knowledge gap between groups closed. Finally, it can be inferred from achievement results that the achievement gap between PROSTEHAS and PROSTEHAM reduced during the instruction. These findings are also in alignment with the literature in that by inquiry and argumentation instruction, it is possible to close the gaps between student groups (Akkus et al., 2007; Zohar & Dori, 2003). Examination of the third research question revealed that both groups enhanced their scientific reasoning, declarative and situational conceptual knowledge during the course. Enhancement of scientific reasoning of both groups in such a short period of time was surprising. However the success of inquiry-based courses in developing of reasoning skills is not new to research (Acar and Patton, 2012; Johnson & Lawson, 1998). Thus this result is tolerable given the effect of both argumentation and guided inquiry. 5.1. Limitations The author has two methodological concerns with this study. First, the conceptual knowledge test used in this study did not have wellestablished reliability. The internal consistencies indexes found for the subscales were below the intended level. The second concern is related to the research design. To have precise results with regard to the effect of argumentation-based guided inquiry instruction, experimental design might have been utilized. However the findings of the reduced gaps between groups and enhancement of scientific reasoning and conceptual knowledge of both groups are thought to compensate this limitation. 5.2. Implications Prospective science teachers were grouped under two categories based upon their arguments for a naïve or a scientific explanation. Results showed that this categorization is meaningful in explaining scientific reasoning, situational knowledge, and achievement differences. With the caution that the explanations used in this study were not just misconceptions but theory-like structures (have the mechanism to explain the phenomena and evidence), research can use this categorization to examine the effect of instruction by comparing students with naïve and scientific conceptions. Argumentation-based

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guided inquiry course utilized in this study helped to close the situational knowledge gap and reduce the achievement gap between PROSTEHAS and PROSTEHAM. Therefore it can be concluded that argumentationbased guided inquiry courses are appropriate for science teacher education programs in reducing the learning gaps among future science teachers. The findings of Kaya (2013) support this conclusion. With these promising findings, however, results indicated that the scientific reasoning gap between PROSTEHAS and PROSTEHAM did not close. One possible explanation to this result may be that over years of traditional teaching, in which reasoning skills have not been appreciated, the gap between these groups widened and ten weeks of reform-based instruction was not enough to close this gap. The author recommends that argumentation and guided inquiry approaches should be incorporated to school curriculum as early as in primary education (Lewis & Lewis, 2008; Zembal-Saul, 2009).

Acknowledgment The author thanks Professor Dr. Bruce R. Patton for helping him to collect the data.

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