Misconceptions about the Particulate Nature of Matter. Using

Research: Science and Education edited by

Chemical Education Research

Diane M. Bunce The Catholic University of America Washington, D.C. 20064

Misconceptions about the Particulate Nature of Matter

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Using Animations To Close the Gender Gap Ellen J. Yezierski* Chemistry Department, Grand Valley State University, Allendale, MI 49401-9403; *[email protected] James P. Birk Department of Chemistry and Biochemistry, Arizona State University, Tempe, AZ 85287-1604

Understanding the particulate nature of matter is critical to understanding chemistry. Consequently, there is a rich and broad base of works that has identified students’ misconceptions about the particulate nature of matter (PNM) (1–11). The importance of the PNM is also apparent in the National Science Standards, which list structure and properties of matter, transfer of energy, conservation of energy, increase in disorder, structure of atoms, and interactions of energy and matter in the Physical Science Content Standards (12). A comprehensive understanding of particle behavior is necessary to understand the topics outlined in the physical science content standards, especially at the 9–12 grade levels. Students’ chemistry misconceptions about the particulate nature of matter may be due to poor visualization ability (4). Luckily, visualization skills can be enhanced through practice (13). Regardless of the source of the PNM misconceptions, there have been many studies to determine the effectiveness of various types of instruction, interventions, or treatments to remediate them (14–19). Several of these found that computer animations or simulations increased conceptual understanding of particle behavior (14, 16–19), although the success of such treatments also has been recently questioned (19). In general, computer animations may help students to understand chemistry by increasing their ability to visualize particle-level processes. If students see animations that show particle behavior, draw pictures of systems of particles, and discuss the connections between the particulate, symbolic, and phenomenological domains, they are more likely to construct more scientific conceptions (20). Although there exists a broad array of identified PNM misconceptions (3, 8), the dynamic nature of phases of matter and phase changes on a particle level may lend itself well to computer animations as a descriptive tool. In this study, we investigated the effectiveness of computer animations to help students better understand the particulate nature of matter as it relates to phases of matter and phase changes. Additionally, we examined whether the animations affected males and females differently. Gender played a prominent role in this study, since much attention has been given to gender differences in science achievement, and most recently to achievement in organic chemistry (21). From a developmental perspective, differences in science achievement between males and females are seemingly nonexistent until adolescence. The gender gap in science 954

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achievement tends to widen and favor males as students get older. Although Howe and Doody (22) claim that sex differences favoring males was documented “so often and so well that it was not an artifact of testing or testing procedures” (p 703), reviews by Linn and Hyde (23) show sex differences in science and mathematics are decreasing. The cause for the differences in science achievement among males and females is complex, because of the array of possible genetic, environmental, and social factors. Of the social factors, schooling has rightfully received a great deal of attention. Equity research over the past 15 years revealed that content, pedagogy, and climate in science and mathematics classrooms disadvantage females (24–26). Another difference that may explain the gender gap in chemistry is spatial ability. Differences between males and females in spatial ability are well-documented. Similarly to differences in science achievement, the gap appears at adolescence and tends to widen as people age (27, 28). Some have found that interventions improved the spatial abilities of women over men (29, 30), while others found no significant differences (31, 32). Overview of Study Although gender differences in science achievement and spatial ability have been examined, it seems necessary to determine whether chemistry interventions are helpful to all students, particularly to females. The purpose of this study is to determine whether computer animations can produce a change in students’ conceptions about the PNM and whether gender plays a role in the animations’ effectiveness.

Research Questions 1. Do computer animations depicting the particle behavior of water help students to overcome particulate nature of matter misconceptions related to phases of matter and phase changes? 2. Does the intervention work equally for males and females?

We determined the effects of the intervention (treatment) by comparing pretest and posttest scores on a newly developed instrument, known as the Particulate Nature of Matter Assessment (ParNoMA), using a quasi-experimental pretest– posttest design.

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Research: Science and Education

Methods

Setting and Sample This study took place at a research-intensive state university and an adjacent K–12 district in the southwestern United States. The participants (N = 719; 350 males, 369 females) in the study were eighth-grade middle school students; tenth-, eleventh-, and twelfth-grade high school chemistry students; and college general chemistry students. Instrument The 20-item multiple-choice Particulate Nature of Matter Assessment used in this study is a revised version of a previously developed, unpublished 12-item instrument. When we piloted the 12-item test, we found that 25% or more of the college students held misconceptions about the particulate nature of matter that related to phases of matter and phase changes. We subsequently designed a 20-item test to target misconceptions about phases of matter and phase change topics prevalent in the chemical education literature. Three general chemistry instructors and two general chemistry teaching assistants validated the test. There was 100% agreement on the correct answers between the reviewers. We piloted the ParNoMA at the beginning of a summer 2002 second semester general chemistry class (N ⫽ 77, Cronbach α ⫽ 0.83). The mean score was 15.2 out a possible 20 (78.0%). It is important to note that the ParNoMA was piloted with students who successfully completed one additional semester course in chemistry, since no first semester general chemistry students were enrolled during the pilot phase of this study. The purpose of piloting the second version of the ParNoMA was to ensure that the test was reliable; therefore, the elevated mean was not a concern. The reviewers who validated the second version indicated that the difficulty was appropriate for a student’s first semester of college chemistry. Intervention Design Roy Tasker’s animations, published in a general chemistry text CD-ROM (33), were used for the intervention. The intervention consisted of four animations of water that run in QuickTime format without an accompanying soundtrack. The animations show liquid water, solid water (ice), water vapor or steam, and water changing phase from solid to liquid (melting). The first animation shows a particulate view of liquid water. Space-fill models of water move about in the frame, tumbling around in a seemingly random fashion against a black background. The orientation of each water molecule model accurately depicts the hydrogen bonds that exist between an oxygen atom of one water molecule and a hydrogen atom of another molecule. The shading and perspective give the viewer the illusion of a three-dimensional view. The second animation represents water molecules in the same manner, this time as ice. The water molecules vibrate and show that there is motion in solids. The perspective is fixed at first, then looks as if the viewer travels through the structure showing the crystalline pattern of the ice and the relative space between the water molecules from various views. The third animation is of water in the gaseous phase. Here the molecules of water are very far apart and seem to be whizzing in and out of the frame. www.JCE.DivCHED.org

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The fourth and final animation begins with water in the solid phase. It begins similarly to the solid water (ice) animation with the viewer moving through different areas within of the structure. The molecules are shown vibrating in their positions and then the vibrations become more fervent and change to faster rotational vibrations. Lastly, the molecules appear to break away from their positions and move slightly closer together into a more random arrangement. The view at this stage resembles the liquid water animation. This series of changes models the particulate behavior of ice melting. The animation lasts 32 seconds, and does not loop; however, it can be easily played multiple times. How the animations were incorporated into the instructional design of the intervention is explained in the next section.

Data Collection The college students took the ParNoMA during their second week of classes in the fall semester. The test was administered by the teaching assistants in their weekly recitation–laboratory meetings. We administered the test to the middle school and high school students during the fourth week of school, since it took a number of weeks to get parental consent for the minor participants in the study. The test administrators gave the participants as much time as they needed to complete the test. Students who chose to participate in the study but who were absent on a test day were given opportunities to make up the ParNoMA. With the exception of the high school honors chemistry classes, students were enrolled in particular periods of the day as their schedules allowed. For this reason, intact classes were assigned to a condition (treatment or control). For the middle school and high school students, their classes were assigned in order to control for teacher, time of day, and academic performance. For academic performance, the teachers identified classes that were somewhat equivalent in ability as measured by their course grades thus far in the semester. Two classes noted by the teachers to be equivalent were not assigned the same condition. For the college students, their classes (or sections) were assigned in order to control for lecture instructor, teaching assistant, time of day, and day of week, since they met for recitation–laboratory only once each week. Additionally, we conducted individual semi-structured interviews with five college students within a week of administering the pretest. The protocol called for students to elaborate on and justify their ParNoMA responses; these interviews lasted approximately 15–20 minutes each. We hoped that they might provide data to help us explain quantitative findings from the pretest and posttest results. We commenced the intervention three weeks after administrating the pretests and finished three weeks later. The control classes spent 25 minutes filling in a worksheet that posed questions relating to the particulate nature of matter as it applies to phases of matter and phase changes to ensure an equivalent time spent on task as compared to the treatment group. In addition, the worksheet required students to share their answers with peers and discuss them in small groups. The worksheet questions are shown in Text Box 1. Designed around the four animations, the intervention lasted approximately 25 minutes in each of the 17 classes assigned to the treatment condition. The animations were run on a laptop computer connected to a LCD projector and pro-

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Particulate Nature of Matter Questions Posed to Control Group Students 1. Draw a molecular-level picture of what happens when water evaporates. Remember, this describes what occurs on a level that we cannot see. Be as explicit as possible. When you are done, examine the pictures drawn by other members of your group. What are some similarities? What are some differences? Be prepared to defend your picture and explanation. 2. Draw a molecular-level picture of what happens when dry ice is placed on the counter. Remember, this describes what occurs on a level that we cannot see. Be as explicit as possible. When you are done, examine the pictures drawn by other members of your group. What are some similarities? What are some differences? Be prepared to defend your picture and explanation. Text Box 1. Particulate nature of matter worksheet questions used with control group students.

jected onto a large screen at the front of the classroom. We presented the intervention in a large group instruction format posing questions and mediating student responses after each animation. At the end, the students were asked a series of follow-up questions that applied to one or more of the animations. We did not explain the animations or provide feedback to the students. We acted solely as mediators calling on students who volunteered to respond. The intervention questions are shown in Text Box 2. Within 5–7 school days after the intervention and control group worksheet activity, we administered the ParNoMA

a second time to all of the participants. A number of students did not take both pretest and posttest tests; their scores were excluded from this study. Data Analysis and Results

Parametric Techniques We analyzed the data with SPSS (Statistics Package for Social Sciences) for students who took both the pre- and postParNoMA test (N ⫽ 719). To select the appropriate set of techniques for analysis, we considered group (cell) size. Groups with more than 15 cases ensure that p-values obtained from parametric tests are fairly accurate (34). In other words, with moderate to large sample sizes, p-values are robust to violations of the normality assumption associated with analysis of variance (ANOVA) and other parametric techniques. For our study the smallest cell size was 48; therefore, we chose to use parametric statistical methods to analyze the data. Equivalence between Treatment and Control Groups Since students were not randomly assigned to a condition (treatment or control), but rather assigned as intact classes, we conducted analyses to check for equivalence between the treatment and control groups. It was important to know whether the groups were equivalent because group equivalence determined how the data were to be analyzed. If a priori differences were found, measures to control for those differences would be necessary. To determine whether there were initial differences between the treatment and control groups on the pre-ParNoMA, we conducted a one-way ANOVA with condition (treatment and control) as the independent variable, and pre-ParNoMA score as the dependent variable. The overall mean (N = 719) on the pre-ParNoMA was 10.31 out of a possible 20 with a standard deviation of 4.616. The ANOVA was nonsignificant, F (1, 717) = 2.013, p = 0.156, and the strength of the relationship between the

Particulate Nature of Matter Questions Posed to Treatment Group Students during the Intervention

Computer Animation of H2O Phases

Corresponding Discussion Questions

Liquid water

1. What visual cues tell you that this animation shows liquid water?

Solid water (ice)

2. How is this animation similar to the previous one (liquid)? 3. How is this animation different from the previous one (liquid)?

Gaseous water (steam or water vapor)

4. How is this animation similar to the previous ones (liquid and solid)? 5. How is this animation different from the previous ones (liquid and solid)?

Phase change (ice melting)

6. How does the state or phase of the water change? 7. When does the phase of the water change? How do you know? 8. Do the molecules appear to change size, shape, or weight as a result of changing phases? Explain.

Follow-up (no animation)

9. Does changing the phase of the water change its identity? Explain. Text Box 2. Particulate nature of matter questions used with treatment group students while they watched four QuickTime animations of water in liquid, solid, and gas phases, as well as a phase change from solid to liquid.

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means, as indicated by the η2 index, η2 = 0.003, was very small, demonstrating that the ParNoMA scores for the treatment and control groups were initially equivalent. Since the pretest scores were not significantly different for the treatment and control groups, it was appropriate to proceed with ANOVA rather than a technique that accounts for a priori differences.

Table 1. Comparing Students’ ParNoMA Scores by Control and Treatment Group Classification LGroup

Particulate Nature of Matter Assessment Scores Pre-Intervention Test

Effectiveness of Animations To determine whether the intervention succeeded for students of both genders, we conducted a two-way repeated measures ANOVA to evaluate the relationship between condition (treatment or control) and ParNoMA scores. The independent variable and between-subjects factor was condition with two levels (treatment and control). The within-subjects factor was ParNoMA scores with two levels (pre and post). Means and standard deviations are reported in Table 1. The ParNoMA main effect was significant, Λ = 0.652, F (1, 717) = 382.270, p < 0.001. By referring to the “Total” row in Table 1, one can see that there was a significant increase, from 10.31 to 12.85, in the mean ParNoMA scores for all the students in the study. The condition ⫻ ParNoMA interaction was also significant: Λ = 0.856, F (1, 717) = 120.183, p < 0.001. The pretest group means for control and treatment, 10.55 and 10.06 respectively, were already found to not be significantly different. An examination of the posttest means from Table 1 show that the mean posttest score for the control was 11.67, compared to 14.04 for the treatment group. This interaction demonstrated that the treatment was effective in producing significant gains in performance on the ParNoMA.

Post-Intervention Test

Mean (n)

SD

Mean (n)

SD

LControl

10.55 (362)

4.706

11.67 (362)

4.944

LTreatment

10.06 (357)

4.516

14.04 (357)

4.473

LTotal

10.31 (719)

4.616

12.85 (719)

4.859

Table 2. Comparison of Students’ ParNoMA Score Gains by Gender and Group Classification LG r o u p

ParNoMA Post-Intervention Score Gains Males

Females

Mean (n)

SD

Mean (n)

SD

LControl

1.07 (183)

3.485

1.17 (179)

2.919

LTreatment

3.33 (167)

3.733

4.54 (190)

3.691

LTotal

2.15 (350)

3.733

2.91 (369)

3.738

standard deviations are in Table 2 and ANOVA results in Table 3. The gender main effect was significant, F (1, 717) = 6.380, p = 0.012, η2 = 0.009. This showed that there were significant differences between male and female gain scores on the ParNoMA across all levels of condition. The condition main effect was significant, F (1, 717) = 118.072, p < 0.001, η2 = 0.142. For both gender groups, students in the treatment group made significantly higher gains than control group students. This is not surprising since the repeated-measures ANOVA on pre- and post-ParNoMA scores by condition was significant. The effect size for the condition main effect was large, 0.142. This suggests that condition accounted for 14% of the variance in gain ParNoMA scores. The gender ⫻ condition interaction was significant for the ANOVA on gain scores. A plot of the means for the gender ⫻ condition inter-

ParNoMA Performance, Condition, and Gender To determine whether the intervention worked equally well on males and females, we conducted a two-way ANOVA, with gain score (post-ParNoMA score ⫺ pre-ParNoMA score) as the dependent variable and two between-subjects factors: gender with two levels (male and female), and condition with two levels (treatment and control). Since we determined that the treatment and control groups have no significant differences on pretest scores, a typical approach would be to use posttest scores as the dependent variable. We selected gain scores here in order to specifically address gains made as a result of the treatment. Answering the research questions required that we determine the extent to which the treatment increased ParNoMA scores within gender groups. Means and

Table 3. Main and Interaction Effects on ParNoMA Score Gains by Gender (A) and Group Classification (B) SS

df

MS

F

pa

η2

OGender (A)

00076.868

001

0076.868

006.380

0.012

0.009

OCondition (B)

01422.661

001

1422.661

118.072

0.000 b

0.142

OInteraction: A ⫻ B

00055.865

002

0055.865

004.636

0.032

0.006

OWithin

08615.098

715

3194.846

OTotal

10212.173

718

OVariable

a

Critical values of 0.05 were used to determine statistical significance;

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b

p < 0.001.

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A

15

pretest posttest 14

4

Mean ParNoMA Score

ParNoMA Gain Score

female male

3

2

1 control

13

12

11

10

treatment

Condition

9

Figure 1. Plot of the means of ParNoMA gain scores by condition and gender.

8 female

male

Gender

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B

15

pretest posttest 14

Mean ParNoMA Score

action is shown in Figure 1. Here one can see the interesting relationship between the variables of condition and gender. These results suggest that the treatment produced significantly greater gains for females than for males on the ParNoMA. According to the results in Table 2, treatment females improved their ParNoMA score by a mean gain of 4.54 points, compared to the treatment males who gained 3.33, on average. Control males’ scores increased by 1.07 points on average, compared to the control females’ mean gain of 1.17. Referring to Figure 1, the steeper slope of the line segment for females between control and treatment suggests that the females benefited more from the treatment as measured by gain scores. Although the treatment produced significantly higher gain scores for females than it did for males, it was important to determine whether there were gender differences for the treatment group on the pretest and posttest scores. A oneway ANOVA on the treatment group with gender (male and female) as the independent variable and pre-ParNoMA score as the dependent variable revealed a significant difference between treatment males and females before the intervention, F (1, 355) = 10.357, p < 0.001. The mean pretest score for treatment males was 10.87 and 9.35 for treatment females. Next, a one-way ANOVA on the treatment group with gender (male and female) as the independent variable and postParNoMA score as the dependent variable was conducted and revealed no significant difference between treatment males and females after the intervention, F (1, 355) = 0.423, p = 0.516. Since there was no significant difference between the treatment posttest scores for males and females, the treatment seemed to close the gender gap that existed at the start of the study as evidenced by significantly higher pretest scores of the males. The effect of the treatment on ParMoMA scores by condition is illustrated in Figure 2.

13

12

11

10

9

8 female

male

Gender Figure 2. Pre- and post-ParNoMA scores for (A) treatment and (B) control groups by gender illustrating the significant gains of females in the treatment group as well as the equivalency of treatment males and females on the post-ParNoMA.

responses on the paper-and-pencil version of the test. The data were coded using two sets of criteria. The first code (S or D) was assigned to each interview question for each student. The code S was given to responses in the interview that matched the students’ responses on the paper-and-pencil test. The code D was given when their responses did not agree. The second code (V, M, or R) applied to questions that the students missed. The code V was for students who misunderstood a vocabulary term. The code M was for responses that closely matched the research-based misconception on that topic. Lastly, the R was for answers that represented a clear and scientifically sound conception, but showed that the student somehow misread or misunderstood the question itself.

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After coding and going over the data, it was found that about three-fourths of the students’ responses in the interviews matched their responses when they took the paper-and-pencil version. The most relevant findings from the interviews provide possible explanations as to the nature of students’ misconceptions and how the intervention played a role in raising ParNoMA scores. These findings are outlined below. Discussion

Effectiveness of Animations The significant gains made by students in the treatment group can be explained in two ways. First, students who are able to visualize chemical phenomena at the atomic or molecular level tend to develop good conceptual understanding (20, 36). Perhaps the animations assisted the students in creating mental models of particle behavior that they could use to answer the conceptual questions on the posttest. If students previously had inadequate particle models or did not even have particulate models, the animations could have filled that void. The incomplete ideas revealed by several of the students interviewed are perhaps explained by their incomplete particulate models of phase changes and phases of matter. Their explanations gave credence to diSessa’s notion of phenomenological primitives (p-prims), otherwise known as knowledge fragments (37). It seemed that three of the college students interviewed may not have possessed well-formed conceptions (scientifically accurate or not), but rather were expressing disconnected knowledge fragments. They were perhaps in transition between two mutually exclusive conceptual frameworks, and therefore presented fragments from each. A second explanation is that conceptual change occurred. If the students previously had scientifically incorrect mental models of water in various phases, and the animations showed contrary but believable representations of phases of matter and phase changes, the students could have reconstructed their knowledge to be consistent with the scientifically accepted explanation. Although either or both of these explanations are plausible, precisely how the treatment worked is unknown. What is important from the result is that computer animations modeling the phases of matter on a particulate level significantly increased the mean score on the ParNoMA. In such a large study, it is critical to consider whether the effects of the intervention are not only statistically significant but also empirically important. The difference between pre- and post- ParNoMA scores for the treatment group was an average gain of 4 points, as compared to the control group, which had a gain of 1 point, seemingly due to a practice effect. Since the test had 20 questions, the average improvement for the treatment group over the control group was 3 questions or 15% of the total points possible. Although an average improvement of 15% does not seem particularly large, when considering that the treatment was less than 30 minutes, the gain is substantial in light of such a modest intervention. The effects of multiple interventions in one chemistry course remain to be determined and ought to be studied. Gender Differences The intervention seemed to work unequally well on males compared to females. Females benefited significantly from the treatment over males as demonstrated by Figure 2 www.JCE.DivCHED.org

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and the significant gender ⫻ condition interaction from the 2 ⫻ 2 ANOVA on gain scores. It is possible that this result can be explained by differences in spatial ability. One difference between solids, liquids, and gases on a particle level is attributed to the arrangement or spatial relationships between the molecules. Particle-level mental models require that students envision molecules moving such that the relative distances between the molecules are appropriate for each phase. It could be conjectured that students with greater spatial visualization abilities can better create these mental particle models. This is supported by Baker and Tally (38) who found that success in chemistry is more strongly correlated to visualization abilities than to general academic ability. Gender differences in spatial ability are small among young children and tend to increase with age, with males outperforming females on spatial tasks (27, 28). A recent survey of the literature regarding spatial ability found studies that examined the impact of interventions on the spatial abilities of males and females (13). Their findings of this survey were varied, although they cited studies in which interventions improved the spatial abilities of women over men (29, 30). Most importantly, this survey did not reveal a large body of evidence for spatial ability differences between males and females being based on intellectual differences and concluded that observed gender differences in spatial ability and performance can be attributed to experiential differences between males and females. Most relevant to the study at hand, they cite the importance of gender differences in spatial ability being eliminated through “relatively minor treatments” (ref 13, p 10). Their plausible explanation is that the treatments are somehow providing background information previously possessed by males and not by females. The animations in the treatment in this study perhaps provided information previously known by males and not by females. Treatment males scored significantly higher than treatment females on the pretest, while there was no significant difference between treatment males and females on the posttest. This showed that the treatment closed a previously existent gender gap. Not only did the females in the treatment group make significantly higher gains than the males, but they also increased their posttest scores to be equivalent to treatment males. This result is consistent with Piburn, et al. (13), in which the gender gap is closed following relatively minor treatments. Even if the gender ⫻ condition interaction has nothing to do with spatial ability, conclusions from Piburn, et al. (13) are still meaningful. If the background information in the treatment is providing females with information that is allowing them to score as well on the posttest as males, the treatment is a necessary addition to chemistry classrooms. Implications There are two major implications and consequent recommendations based on the findings of this research. First, particle-level animations of water in various phases and water melting improved students’ scores on a new, valid, and reliable instrument called the Particulate Nature of Matter Assessment. The extent to which gains on this test represent conceptual change is unknown, although the significant gains made by the treatment group over the control group show

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that the animations are beneficial. Particle-level animations should be used frequently in chemistry classrooms to help students visualize particle-level behavior. In conjunction with showing these animations, students should be given opportunities to discuss and interpret the animations as they relate them to macroscopic phenomena that they have observed. Second, as more and more classroom teachers become aware of gender equity research in science education and modify their curricula and instruction to better meet the needs of all students, we recommend that it be common practice to incorporate particle-level animations and accompanying discussions. The treatment in this study closed the previously existent gender gap and enabled the females and males to achieve equivalent post-ParNoMA scores. The a priori differences in ParNoMA scores between males and females could have been due to differences in science achievement, spatial ability, or even as distant from the scope of this study as exposure to video games. Regardless of the cause for the initial differences, the treatment leveled the playing field and allowed males and females to perform equally on the ParNoMA. Increasing females’ conceptual understanding of chemical phenomena could play a role in shrinking the achievement gap between genders in chemistry. The findings of this study should be applied in methods courses with pre-service and in-service physical science and chemistry teachers. Teachers need to be trained to diagnose students’ misconceptions, design interventions using particlelevel animations, access the animations through textbook publishing companies, free digital libraries, or commercial software suppliers, and make them accessible to their students. This can only be possible if teachers are fluent in teaching methods built around a constructivist framework and have mastered the content to a high degree such that their own misconceptions are extremely limited. W

Supplemental Material

The Particulate Nature of Matter Assessment instrument is available in this issue of JCE Online. Acknowledgments We sincerely thank the middle school and high school teachers, chemistry teaching assistants, and students for their enthusiastic participation. We also thank the reviewers for their insightful comments. This material is based upon research partially supported by the U. S. Department of Education under grant number OPE P336B990064. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the Department of Education. Literature Cited 1. Novick, S.; Nussbaum, J. Sci. Educ. 1981, 65, 187–196. 2. Osborne, R.; Cosgrove, M. J. Res. in Sci. Teaching 1983, 20, 825–835. 3. Garnett, P.; Garnett, P.; Hackling, M. Studies in Sci. Educ. 1995, 25, 69–95. 4. Gabel, D.; Samuel, K.; Hunn, D. J. Chem. Educ. 1987, 64, 695–697.

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