ASSESSING PRE= -SERVICE ELEMENTARY TEACHERS’ KNOWLEDGE OF TYPES OF MODELS

Susan A. Ever= ett, University of Michigan-Dearborn

Gail R. Luera, University of Michigan-Dearborn

Charlotte A. = Otto, University of Michigan-Dearborn

Abstract

After reviewing several i= nstruments for assessing knowledge about scientific models, we used a portion of the R= ole of Models in Science (Chittleborough, et al, 2005) to determine pre-service elementary teacher growth in knowledge of models. Students showed a statistically significant increase in understanding of examples of models following participation in a Science Capstone course that focused on integrating the unifying theme of models with a practicum experience of teaching elementary school science using models. The instrument was also useful for course instructors in initiating class discussions about models, planning l= ab activities to address misconceptions about models, and providing an interes= ting set of data for students to practice data analysis prior to conducting an a= ction research project.

Both scien= tists and science teachers regularly use models of natural phenomena. Models can be simple representatio= ns of science concepts such as plastic parts that fit together to show a human or= gan or a pictorial diagram of the water cycle.= They can also be extremely complex representations such as statistic= al models or the idea of string theory. As such, scientific models can be different types, serve different purposes, and are used in a variety of way= s.

Recently, = the science education research community has been interested in scientific mode= ls and how teachers and students, especially at the secondary level, use them. Several studies documen= t the knowledge of secondary science teachers’ concerning models in science (Crawford & Cullin, 2004; Van Driel & Verloop, 1999 and 2002). The understanding that secondary students have about models within the context = of learning science has also been of great interest (Grosslight, Unger, Jay &a= mp; Smith, 1991; Harrison & Treagust, 1996; Treagust, Chittleborough & Mamiala, 2002). In addition t= he importance of scientific models is recognized by the science education refo= rm documents as one of the common themes of the Benchmarks for Scientific Literacy (AAAS, 1993) and one of the unifying concepts of the Nation= al Science Education Standards (NRC, 1996).

We have revised our program for elementary education majors (Luera & Otto, 2005) and have created a three-credit Science Capstone course that combines action research with one of the unifying themes from AAAS (1993) or NSES (1996). At present, the cours= e focuses on the unifying theme of models. The goal of the course is to provide content knowledge about models in science = so that students learn about the different types of models, the uses of models= by scientists, and how models are tools for communication. It also provides students with dire= ct teaching experiences in K-8 classrooms using models in support of teaching inquiry-based science lessons from the local school curriculum. In addition, we seek to increase the pedagogical content knowledge (PCK) of our students so that they are able to analyze models used in K-8 classrooms, to learn how students understand the nature of models and to be able to modify lessons for more effective use of= models. Since much research has been conducted on models, we searched for assessments that we could use with our= own students to determine their growth in knowledge about models in science.

Theoretical Framework

Several different types of instruments assessing knowledge about scientific models = were located in the literature. Gr= osslight, Unger, Jay, and Smith (1991) conducted an exploratory study to learn about student conceptions of models by analysis of structured interviews with 33 mixed ability 7^th grade students, 22 honors level 11^th grade students and 4 adult experts on scientific models. The analyses categorized the students’ open-ended responses and examined the criteria students use= d to identify an item as a model. = They identified three levels of understanding about models based on the responses focusing on the relationship of models to reality and the role of ideas in models. Level 1 understanding includes those with ideas that models are simple copies of reality. Level 2 understanding considers ho= w the purpose of the model determines or impacts the construction of the model. Therefore, the model does not have= to exactly correspond to the real world. Level 3 includes the understanding = that models are constructed to develop and test ideas rather than simply copy reality. In this study, 67% o= f the 7^th graders had pure level 1 scores, 18% had mixed level 1/level= 2 scores, and 12% had pure level 2 scores.&n= bsp; While the 11^th graders scored higher (23% at level 1, 36%= at a mixed level, and 36% at level 2), no student scored at level 3 or at a mi= xed level 2/3. The authors recomm= end that students have more extensive experiences with models as tools of inqui= ry rather than a tool for fact memorization.

Van Driel = and Verloop (2002) designed a 30-item Likert-type scale questionnaire to investigate the use of teaching activities concerning models based on a ser= ies of semi-structured interviews with current secondary science teachers. The questions were developed accord= ing to several categories: discussing and reflecting on models in both teacher-directed and student-directed activities, designing and developing models in both teacher directed and student-directed activities, and students’ views of models and their modeling abilities. The results showed that one group of secondary science teachers reported to use more activities with models but = this could not be related to their experience or subject taught. In an earlier study (1999) Van Dri= el and Verloop investigated experienced science teachers’ knowledge of models through a 32-item Likert-type scale questionnaire. The results showed that teachers h= ad a common definition of models as a simplified representation of reality howev= er, the teachers used many different criteria to determine if an example was a = model or not. Van Driel and Verloop concluded that the teachers’ knowledge = of models was limited and often included inconsistencies.

Treagust, Chittleborough and Mamiala (2002) designed a 27-item Likert-type scale call= ed Students’ Understanding of Models in Science (SUMS), which focused on= 5 themes: scientific models as multiple representations, scientific models as exact replicas, models as explanatory tools, how models are used, and the changing nature of scientific models. They used the SUMS with 228 seconda= ry students and showed that there were no statistically significant differences for any of the 5 themes between grade levels of students. The authors concluded that students needed a greater emphasis on the role and purpose of scientific models in t= heir science studies. In a related study, Chittleborough, Treagust, Mamiala and Mocerino (2005) designed and u= sed an instrument, My Views of Models and Modelling in Science, to assess secon= dary students’ views on models and the role that models play in learning science. The six items in the questionnaire asked students to choose between two alternative statements a= nd explain their reasoning. Resu= lts showed that as the age of students increased, their understanding of scient= ific models in the process of science increased as well as their understanding of models in their own learning of science.

Methodology

Our purpose was to determine if pre-service elementary school teachers’ knowledge about different types of models increased as a result of participating in the Science Capstone course. For one method of assessment, we de= cided to use a portion of the assessment instrument, The Role of Models in Science (Chittleborough, et al, 2005), = as an assessment of our pre-service teachers in the Capstone course. The model assessment questions chos= en focused on the types of models. We used nine questions which required students to analyze an item to determine= if it was an example of a model or not and provide a reason for the decision.<= /p>

Data were collected from 130 capstone students enrolled in the last three semest= ers, who agreed to participate in the study. The students are all elementary edu= cation majors, are typically in the last semester of coursework prior to student teaching and have completed the science methods course or enroll in it concurrently with the Capstone course. Approximately 50% of the students a= re transfers from community colleges, 15% are self-identified minorities, and = most students work off-campus, many for at least 20 hours per week.

We used a = pre and post design: students completed the questionnaire on the first day of class= and again at the end of the semester. Throughout the semester, students participated in several inquiry-based science activities using various type= s of models, practiced evaluating common models used to teach science in K-8, and investigated ways that scientists use models. Students also developed an action research project that included creating and teaching two inquiry-based scie= nce lessons using models to local elementary school students.

Results

The questionnaires were scored by assessing whether students correctly identifi= ed the item listed as a model or not. Then, the reasons provided were ana= lyzed for correct explanations. The= data were analyzed with SPSS 14.0 for Windows (SPSS, Inc., 2005). The results of the students’ classification of each item are listed in Table1 below.

Table 1: Pre/Post Frequencies for Classification of Models

Item

Responses on Pre-Test

n=3D130

Responses on Post-Test

n=3D121

A toy car

Correct - 111 (85.4= %)

Incorrect - 13(10%)=

Missing - 6 (4.6%)<= o:p>

Correct - 121 (100%= )

Incorrect - 0<= /o:p>

Missing - 0

A plastic ear<= /o:p>

Correct -124 (95.4%= )

Incorrect - 2 (1.5%= )

Missing - 4 (3.1%)<= o:p>

Correct - 121 (100%= )

Incorrect - 0<= /o:p>

Missing - 0

A living animal, e.= g., a kangaroo*

Correct - 71 (54.6%= )

Incorrect - 48 (36.= 9%)

Missing - 11 (8.5%)=

Correct - 111 (91.7= %)

Incorrect - 4 (3.3%= )

Missing - 6 (5%)

An experiment of a = metal in acid*

Correct - 49 (37.7%= )

Incorrect - 65 (50%= )

Missing - 16 (12.3%= )

Correct - 68 (56.2%= )

Incorrect - 38 (31.= 4%)

Missing - 15 (12.4%= )

A photograph of a cell taken with an electron microscope*

Correct - 93 (71.5%= )

Incorrect - 32 (24.= 6%)

Missing - 5 (3.8%)<= o:p>

Correct - 114 (94.2= %)

Incorrect - 5 (4.1%= )

Missing - 2 (1.7%)<= o:p>

A chemical equation *

Correct - 76 (58.5%= )

Incorrect - 44 (33.= 8%)

Missing - 10 (7.7%)=

Correct - 110 (90.9= %)

Incorrect - 11 (9.1= %)

Missing - 0

A diagram of the in= side of an atom

Correct - 120 (92.3= %)

Incorrect - 6 (4.6%= )

Missing - 4 (3.1%)<= o:p>

Correct - 119 (98.3= %)

Incorrect - 2 (1.7%= )

Missing - 0

A computer image of= a rat dissection*

Correct - 107 (82.3= %)

Incorrect - 17 (13.= 1%)

Missing - 6 (4.6%)<= o:p>

Correct - 120 (99.2= %)

Incorrect - 1 (0.8%= )

Missing - 0

A graph showing the= energy changes in a reaction*

Correct - 96 (73.8%= )

Incorrect - 30 (23.= 1%)

Missing - 4 (3.1%)<= o:p>

Correct - 117 (96.7= %)

Incorrect - 4 (3.3%= )

Missing - 0

* p<0.05

The signif= icance of the difference between the number of students answering correctly for ea= ch item from pre to post-testing was determined by identifying the 95% confide= nce interval (C.I.) for each item. Once that was calculated, the pre and post C.I.’s were compared. If the pre and post C.I. for an item overlapped, then the difference between the pre and post responses was not significant at the .05 level. The C.I’s could not be determined for the correct post responses for the = toy car and the plastic ear since the standard deviation was zero for the post-tests. Six items were significant at p < 0.05 level.

The correc= t responses on the pre-test were summed to analyze the questionnaire as a whole. Out of a possible total score of 9, pre-test scores ranged from 2 to 9. These totals are shown in figure 1 below. The mean was 6.78 and the standard deviation was 1.33.

= ;

Figure 1: Sum Scores on Pre-test

The number= of correct responses were also totaled on the post-test. These results are shown in Figure = 2. The mean score was 8.24 and the st= andard deviation was 0.78.

= ;

Figure 2: Sum Scores on Post-test

We used a Wilcoxon Signed Ranks Test to test for a significant difference between the pre-and post sum scores (z =3D -6.972, p= < .000).

We also an= alyzed subpopulations of the pre-service teachers to determine if there were factors which indica= ted a difference in knowledge of types of models. We compared the elementary science majors/minors with the non-science majors/minors. Using a nonparametric test (Kruskal-Wallis Test) because the scores were not normally distributed, we found no significant difference on either pre-test sum score (p =3D 0.30) or post-test sum score (p =3D 0.69). We also analyzed the difference between students who took all three of our inquiry-based science courses designed for elementary education majors and those students who took none of them because they transferred in traditional introductory science courses. There were no significant differences on either the pre-test sum score (p =3D 0.4= 3) or the post-test sum score (p =3D 0.81).

Discussion

The questions from Chittleborough, et a= l, (2005) were a useful way to analyze the growth in the pre-service elementary teachers’ knowledge of the types of models. It is not surprising that although students did not have formal instruction on models, they did have prior knowledge about some types of models. The majority of students correctly identified the two examples of concrete models, the toy car and the plastic ear. Students often have expe= rience of using a variety of concrete models in school, especially in science clas= ses. The diagram of the inside of = an atom was also recognized as a model by 92% of the students on the pre-test.=

Other exam= ples of models were not as obvious to students on the pre-test such as a chemical equation or a graph. These two models are more abstract than the concrete or pictorial models. Students wh= o did not identify these as models gave reasons such as: “can be seen & observed”, “students can perform it” or “Chemical science, which can be visually seen” as responses to the chemical reaction item. Students’ reasons for not including graphs as models included: “It’s only information”, “A graph is simply a way to show/demonstrate information. The graph isn't a model for anything”, or “It's on= ly information”.

The two it= ems that were not examples of models in the list of items included the living animal (kangaroo) and the experiment of metal in an acid. On the pre-test, we found that thes= e two items had the highest percentages of missing responses, 8.5% and 12.3% respectively. It appears that= many students were not even willing to guess but instead left the response blank= . While there was significant improve= ment on each of these items on the post-test, it was interesting to analyze the reasoning students provided. = Students, who identified these as models on the post-test, created scenarios in which= the item could be considered to be a model. These led us to realize that they ‘read’ more into the question than the developers of the assess= ment had probably intended. In some cases, the additional information students added on their own led to an appropriate classification that we did not initially score as ‘correct.’ For instance, if a student responded that the kangaroo was a model and explained that it represented marsupials then, we coded the answer as correct even th= ough we did not view living organisms as models.

Two items = that seemed to represent misconceptions about models for some students included = the photograph and the experiment. Throughout the semester, instructors noticed that many students continued to think of a photograph as a target, rather than a model. They had difficulty accepting the = idea that the photograph was a representation of an object. The other misconception focused on= the experiment or other similar processes in science. Once again, during the semester, va= rious types of science processes were continuously identified as models by some s= tudents rather than as actual processes. Some examples of these included a simple circuit of batteries, bulbs and wires, = and an exothermic chemical reaction in a plastic baggie.

The studen= ts showed significant improvement from the pre-to post-test sum score results. The students were mo= re accurate in identifying models as well as more confident since there were f= ewer missing responses on the post-test. Aside from the items noted above which caused difficulty for some students, their overall growth in types of models was apparent from participation in the capstone course.

The analys= is of the science majors/minors compared to the non-science majors/minors showed = no significant difference on either the pre- or post-test scores. In addition, there were no signifi= cant differences for students completing more of our inquiry-based science cours= es designed for elementary education majors vs. those who transferred in traditional introductory science courses. We believe that these results show = that models cannot be taught in an implicit manner in science courses if we want students to become knowledgeable about models in science. In the required inquiry-based scien= ce courses for our elementary education majors, models are commonly used in ma= ny laboratory activities. Instru= ctors often have students compare the model with the target to note similarities = and differences. However, our res= ults show that students with more of these courses did not have statistically significant differences on their prior knowledge of models. We believe that as students are us= ing models in science, they also need to learn about the nature of models in science. One student captured= this idea in the final report: “This action research project helped me lea= rn that models are a ‘big idea’ because they explain or predict scientific ideas in different science disciplines. The project cleared up my own misconceptions about models.”

The pre-te= st questionnaire was also useful for instructors. We planned activities to address so= me of the items incorrectly classified by many students. For example, we were surprised that correct responses for some items were low such as the experiment. This area has continued to be addre= ssed more significantly each semester. We notice that students need more experience with chemistry models, which has not been a focus of the course.&= nbsp; The questionnaire was very beneficial at the beginning of the semest= er to engage students in the topic of models.= Students admitted that they felt confident that they knew quite a bit about models before the course started but then realized from the pre-assessments that they lacked knowledge. The questionnaire generated much discussion amongst the students as they reflected on their responses at the beginning of the semester. We= also used the pre-assessment results as a sample data set to introduce and prepa= re our students for their own data analysis in their action research projects. The students practiced data analys= is, categorization of responses, and tallying frequency of responses in prepara= tion for the pre-assessments that they would give to elementary school students = in their practicum experience.

Our results expand on previous studies with teachers (Crawford & Cullin, 20= 04; Van Driel & Verloop, 1999 and 2002) since our students were pre-service elementary teachers rather than secondary teachers. We believe that it is important to consider how elementary teachers view models since they are commonly used in K-8 science education. Even if teachers are able to use models in teaching, without sufficient introductio= n to the nature of models, they may not be able to help elementary students understand models in science.

References

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

Chittleborough, G., Treagust, D.F., Mamiala, T.L. & Mocerino, M. (2005). Students’ perceptions of the role of models in the process of science and in the proc= ess of learning. Research in Science & Technological Education, 23(2= ), 195-212.

Grosslight, L., Unger, C., Jay E., & Smith, C.S. (1991). Understanding models and t= heir use in science conceptions of middle and high school students and experts. = Journal of Research in Science Teachin= g, 28(9), 799-822.

Luera, G. & Otto, C. (2005). Development and evaluation of an inquiry-based elementary science teacher education program reflecting current reform movements. Journal of Science Teacher Education, 16, 241-258.

National Research C= ouncil. (1996). National Science Education Standards. New York: Nation= al Academy Press.

Treagust, D.F., Chittleborough, G., & Mamiala, T.L. (2002) Students’ understanding of the role of scientific models in learning science. International Journal of Science Educa= tion, 24(12), 357-368.

Van Driel, J.H. & Verloop, N. = (1999). Teachers‘ knowledge of models and modelling in science. International Journal of Science Education, 21(11), 1141-1153.

Van Driel, J.H. & Verloop, N. = (2002). Experienced teachers’ knowledge of teaching and learning of models and modelling = in science. International Journal of S= cience Education, 24(12), 1141-1154.