Cemal
Tosun
Faculty of Education, Department of Science Education, Bartın University, Bartın, Turkey. E-mail: cemaltosun22@gmail.com
First published on 5th September 2018
The aim of this study was to develop a Scientific Process Skills Test (SPST) in the context of “Matter and its Nature”. It was investigated whether there was a predictive effect of demographical features and participating out-of-school learning opportunities across the 7th and 8th grade students’ Scientific Process Skill (SPS) levels. Quantitative research methods were used in this study. Data were collected from 289 middle school students for the validity and reliability of the test. The SPST consisted of 30 items and entailed three sub-dimensions (“basic scientific process skills, causal scientific process skills and experimental scientific process skills). The reliability coefficient of the test was calculated using the KR-20 formula and was found to be 0.84. The SPST was applied to 472 middle school students with the aim of determining whether there is a predictive effect of gender, grade level, school location, parent's education level and participation in out-of-school learning opportunities across the 7th and 8th grade students’ SPS levels. Multiple regression analysis was conducted to determine the effects of demographical features and out-of-school learning opportunities on students’ SPS levels. As a result, it was determined that the grade level, gender or mother's education level were important predictor variables that affect middle school students’ SPS levels. It was also determined that out-of-school learning opportunities such as participating in science fairs, designing projects or reading scientific journals had an important predictive effect on students’ SPS levels.
There is no consensus with the meaning and content of science literacy. Some definitions focus on facts, concepts and vocabulary, while other definitions emphasize scientific process skills (SPSs) and reasoning skills (Miller, 2006).
Science literacy has become a common vision of today's science education. Reforms are being carried out in this field in the United States, Canada, and Europe (Bybee, 1995; National Research Council (NRC), 1996). The science curriculum's vision in Turkey is to educate all students as science literate regardless of their individual differences (Ministry of National Education (MoNE), 2017). It is stated that individuals who are science literate should have an understanding of the nature of science, the basic concepts of science, the positive attitude, perception and value related to science, the interrelationships of science, engineering, technology, society, and the environment, and the scientific and technical psychomotor skills and SPSs (MoNE, 2017).
The primary goals of science literacy were emphasized by American Association for the Advancement of Science [AAAS] (1993) and the NRC (1996) as an improvement in thinking skills including computation, prediction, manipulation, observation, communication and critical response skills and the ability to engage in research activity including making observations, asking questions, planning research studies, reviewing what is known in the light of experimental evidence, using data collection tools, analyzing data, interpreting data, suggesting answers, and explaining, predicting and communicating results. These skills, which are the primary goals of science literacy, are among the SPS dimensions, and the SPS dimensions and the primary goals of science literacy intersect.
The PISA 2006 definition of science literacy has three dimensions: scientific concepts, scientific processes and scientific situations (Organisation for Economic Cooperation and Development (OECD), 2007a). These scientific processes are related to the recognition of scientific questions, the identification of evidence, the drawing of conclusions, the communication of these conclusions and the demonstration of understanding of scientific concepts (OECD, 2007a). The literacy skills of science students in the learning process can be improved as an alternative way through a process skills approach (Suryanti et al., 2018). Science literate individuals locate, collect, analyze, and evaluate the source of scientific and technological information. They use these resources to solve problems and make decisions (Holbrook and Rannikmae, 2009).
In order for each individual to be trained as a scientist, SPSs must be acquired and used individually. Individuals who acquire SPSs solve daily life problems with the ways, methods, and perspectives that scientists use.
It is not possible to realize the basic aims of the science curriculum without improving the skills of gathering, processing, and generating evidence from data. It is expressed in the literature that data processing skills have a central role in reasoning (NRC, 1996; Next Generation Science Standard (NGSS), 2013). Reasoning with data is important for conducting authentic inquiry in science and involves learning mathematical concepts and procedures and recording, interpreting, and representing data (Zimmerman, 2007).
Science content and SPSs are two primary components of science education. SPSs are very important in science teaching (Kujawinski, 1997; Myers et al., 2004). For many years it has been stated that the main purpose of science education is to gain SPSs (Germann, 1989; Germann et al., 1996). Besides, it is advised that students gain SPSs by means of several activities in science education (Huppert et al., 2002). According to NRC (1996), one of the aims of science education is to use SPSs when making decisions.
SPSs are basic skills that facilitate learning in science, enable students to be active, improve the sense of responsibility in their own learning, and increase the permanence of learning (Çepni et al., 1997, pp. 74–83). SPSs are classified as basic and high-level SPSs by some researchers considering the developmental periods of students (Saat, 2004; Bağcı-Kılıç, 2006; Rezba et al., 2007). Çepni et al. (1997), on the other hand, divide these skills into three as basic scientific process skills (B-SPS), causal scientific process skills (C-SPS), and experimental scientific process skills (E-SPS). The B-SPS dimension includes observing, classifying, measuring, communicating, and recording data sub-dimensions. The C-SPS dimension includes inferring, predicting, defining operationally and identifying variables sub-dimensions. The E-SPS dimension consists of sub-dimensions such as hypothesizing, designing an experiment, changing and controlling variables, modeling and data interpretation (conclusion-decision). Zimmerman (2007) classifies SPSs as specific for a field that states the necessity of knowing certain scientific concepts to be able to solve the problems related to a scientific field and general SPSs.
A literature review reveals that Scientific Process Skills Test (SPST) development studies have been carried out for half a century (e.g.:Tannenbaum, 1971; Molitor and George, 1976; Dillashaw and Okey, 1980; Tobin and Copie, 1982; Burns et al., 1985; Mckenzie and Padilla, 1986; Shahali and Halim, 2010). In terms of SPS studies in Turkey, the SPST developed by Burns et al. (1985) was adapted to Turkish by Geban et al. (1992), and this test was used in numerous studies in Turkey. In later years, several other test development studies for identifying SPSs were encountered (e.g.: Aydoğdu et al., 2012; Feyzioğlu et al., 2012).
A literature review reveals how students’ SPS level changes according to variables such as gender (e.g.: Zeidan and Jayosi, 2015; Güden and Timur, 2016), school location, (e.g.: Zeidan and Jayosi, 2015), grade level (e.g.:Böyük et al., 2011), parents’ education level (e.g.: Karar and Yenice, 2012a; Ocak and Tümer, 2014), family's income level (e.g.: Karar and Yenice, 2012a), the number of brothers/sisters (e.g.: Güden and Timur, 2016), having a computer (e.g.: Böyük et al., 2011; Ocak and Tümer, 2014), having a study room (e.g.: Böyük et al., 2011), school type (e.g.: Ocak and Tümer, 2014), profession of parents, a kindergarten school attended and the frequency of visit to natural areas (e.g.: Güden and Timur, 2016). It is stated in the educational research literature that there are many studies that have been conducted on some factors affecting students’ SPS level. However, it has been seen that these studies are carried out on only a few factors as independent variables. However, not all variables have been looked at in one study, and the degree of importance of some predictor variables affecting students’ SPS level was not examined.
The curriculum of science teaching in Turkey emphasizes the training of science literate individuals. Training individuals with enhanced SPS is among the components of science literacy. To achieve this, taking precautions that will improve the students’ SPS from early ages is of importance. SPSs include skills that can be used at every stage of everyday life by being science literate by understanding the nature of science and thereby increasing the quality and standards of life (Williams et al., 2004). This brings about the necessity of performing test development studies descriptive of the SPSs of middle school students. To this end, in this study a SPST was developed for 7th and 8th grade students by taking the aforementioned drawbacks into consideration. Test development requires a long time period and expertise. The context of the test was based on “Matter and its Nature”, one of the four subjects in the science curriculum in Turkey. This topic includes the units: “Matter and Change of State”, “Matter and Heat”, “Pure Matter and Mixtures” and “Matter and Industry”. Also, the test was designed to be appropriate for 7th and 8th grade students so as to refrain from validity and reliability discussions caused by performing the test on all fields and grades, and disregarding field knowledge while developing test items.
Students’ science literacy is measured through socio-scientific situations in PISA (Programme for International Students Assessment) exams prepared for determining the level of comprehension, mathematics and science literacy by 15 year olds. It is based on the second approaches of measuring students’ science literacy in PISA exams (Bybee and McCrae, 2011). Three qualifications for science literacy in the PISA 2015 science framework text have been identified. These are: explain events scientifically, evaluate and design scientific investigations and interpret data and evidence scientifically. These three qualifications are measured in a socio-scientific context. It is influenced by the individual's knowledge and attitudes (OECD, 2013). The PISA 2006 results reveal that science education at the middle school (5–8 grades) level in the world is inadequate in achieving the targeted knowledge, skills and understanding (OECD, 2007b). In this study, the content of the items in the SPST was in the form of problem scenarios prepared from socio-scientific issues. With these problem scenarios, it was aimed to determine the level of SPS of 7th and 8th grade students. The reason of preference for 7th and 8th grade students was that they were the closest group to taking the PISA exam.
The science course consists of physics, chemistry, biology and earth science concepts in the middle schools in the Turkish curriculum. The topic “Matter and Its Nature” is in the chemistry field, and it has a wide coverage in the science curriculum in grades 5 to 8. Science teaching is usually carried out in three learning environments which are classrooms, laboratories and out-of-school learning environments (Orion and Hofstein, 1994). Out-of-school learning environments are based on the assumption that the learning as an individual activity can be performed in all areas of life not just in class. Practice areas are all kinds of teaching environments that will support formal learning. Science education in middle schools should expand beyond the classroom walls, and out-of-school learning environments offer many opportunities for students to learn science (Carrier, 2009). But the author did not find any research related to the predictive effects of participating in out-of-school learning opportunities (participating in science trips, designing projects, participating in science competitions, reading scientific journals and participating in science fairs) across students’ SPS level. In this study, whether the SPS level of middle school students changed according to participation in scientific activities that differ from the variables aforementioned was identified.
Many studies have found that the level of SPS of students is low (Walters and Soyibo, 2001). It is known that Turkish middle school students’ score is below the international average in PISA and TIMMS (Trends in International Mathematics and Science Study) exams in the field of science literacy (Gonzalez and Miles, 2001; Martin et al., 2008). Knowing what is most effective in improving students’ SPS is important to increase the skill level.
SPSs are acquired during the learning process. Hence the factors affecting SPSs are found in the process itself (Nwosu, 1991). Some factors that influence students’ acquisition of SPSs include the following (Martina, 2007): teacher's competence, the method of teaching, instructional materials, parents’ background and practice, gender, school location, school type, teacher sensitization, students’ cognitive ability, teachers’ experience, cognitive demand of students, and family's socio-economic status.
An investigation of the SPS levels of students and the variables affecting these skills is an important field of study in the process of deciding what needs to be taken into account in improving education. A literature review reveals how students’ SPS levels change according to some factors. However, it has been seen that these studies are carried out based on only a few factors as independent variables. However, there are no studies in the literature regarding the degree of importance of some predictor variables affecting students’ SPS level.
In this study, after confirming the validity and reliability of the developed test to identify the SPS level of middle school students, it was investigated whether there were predictive effects of demographical features (gender, grade level, school location, and parent's education level) and participating in out-of-school learning opportunities across the 7th and 8th grade students’ SPS levels with the analysis of the data collected from a large sample group.
Frequency | Percent (%) | |
---|---|---|
Gender | ||
Female | 169 | 58.5 |
Male | 104 | 35.9 |
Unspecified | 16 | 5.6 |
Total | 289 | 100 |
Grade level | ||
7th grade | 152 | 52.6 |
8th grade | 137 | 47.4 |
Total | 289 | 100 |
The test was applied to 472 middle school students in Bartın, Kilis, Antalya, Ordu, Kahramanmaraş and Konya in Turkey with the aim of determining whether there are predictive effects of demographical features and participating in out-of-school learning opportunities across the 7th and 8th grade students’ SPS levels. It was not claimed that the sample group represents all 7th and 8th grade students in Turkey. Sampling was selected from the students who study in different regions and have different economic status. Thus, it is provided that the sample group represents the properties of the population partly, though not completely. In addition, the number of observations was increased to allow the normal distribution of the data (Stevens, 2009, p. 221).
This study was carried out in the 2016–2017 academic years. To collect data from the students, necessary permissions were obtained from the school administrations. The SPST was conducted by teachers in the MoNE. The intended purpose of the test was expressed to the students. Students were asked to participate in the study. Participation in the test was based on volunteerism. They were told that they would not have any advantages or disadvantages of participating in the test. Detailed information on this sample group can be seen in Table 2.
Frequency | Percent (%) | ||
---|---|---|---|
Gender | Female | 213 | 45.1 |
Male | 257 | 54.4 | |
Unspecified | 2 | 0.5 | |
Grade level | 7th grade | 249 | 52.8 |
8th grade | 223 | 47.2 | |
School location | Urban location (City center) | 371 | 78.6 |
Rural location (Village) | 93 | 19.7 | |
Unspecified | 8 | 1.7 | |
Mother's education level | Up to primary school (including illiterate and literate) | 181 | 38.3 |
Middle school | 115 | 24.3 | |
High school | 94 | 19.9 | |
University | 71 | 15.1 | |
Unspecified | 11 | 2.3 | |
Father's education level | Up to primary school (including illiterate and literate) | 86 | 18.2 |
Middle school | 87 | 18.4 | |
High school | 144 | 30.5 | |
University | 140 | 29.7 | |
Unspecified | 15 | 3.2 | |
Scientific activities | Science trips | 116 | 24.5 |
Science fairs | 190 | 40.2 | |
Scientific journals | 226 | 47.9 | |
Science competitions | 55 | 11.6 | |
Design projects | 168 | 35.5 |
The topics of Change of State of Matter, Distinctive Features of Matter, Heat and Temperature and Heat Effects Matters are included in the 5th grade curriculum.
The topics of Particulate Nature of Matter, Condensation, Matter and Heat, and Fuels are included in the 6th grade curriculum.
The topics of Particulate Nature of Matter, Pure Matter, Mixtures, Decomposition of Mixtures and Domestic Waste and Recycling are included in the 7th grade curriculum.
Finally, in the 8th grade curriculum, the topics of Periodic System, Physical and Chemical Changes, Chemical Reactions, Acids and Bases, Matter–Heat Interaction and Chemical Industry in Turkey are included. What concepts these items include and which SPS levels they belong to are given in Table 3.
Scenarios | Item | Concepts | SPS levels |
---|---|---|---|
Unmixed waters | 1 | Heat exchange, temperature, density and density difference | Inferring |
2 | Space structure, moving structure, heat, heat exchange, temperature, density and density difference | Conclusion-decision | |
3 | Density and density unit | Recording data | |
4 | Density and temperature | Communicating | |
Science laboratory | 5 | Evaporation and boiling | Classifying |
6 | Heat, boiling, boiling points and temperature | Predicting | |
Dry ice | 7 | Melting and sublimation | Identifying variables |
8 | Melting and sublimation | Identifying variables | |
9 | Melting and sublimation | Hypothesizing | |
Candle | 10 | Melting | Identifying variables |
11 | Melting | Identifying variables | |
12 | Melting | Changing variables | |
13 | Melting | Hypothesizing | |
14 | Melting, domestic solid waste, recycling and reuse | Defining operationally | |
Change of state | 15 | Evaporation, freezing, melting, deposition, sublimation and condensation | Classifying |
Snowman | 16 | Freezing point and melting | Designing an experiment |
Cold winter day in Erzurum | 17 | Freezing point | Inferring |
Emre playing ball | 18 | Evaporation, heat, heat exchange and temperature | Conclusion-decision |
19 | Evaporation, heat, heat exchange and temperature | Conclusion-decision | |
Temperature | 20 | Shrinkage, expansion, heat and temperature | Classifying |
Balloon tour | 21 | Expansion, heat and density | Hypothesizing |
22 | Shrinkage, heat and density | Inferring | |
23 | Shrinkage, expansion and density | Conclusion-decision | |
Bathroom | 24 | Heat exchange and temperature | Measuring |
Water and juice | 25 | Freezing point | Hypothesizing |
26 | Freezing point | Conclusion-decision | |
Circular metal | 27 | Heat conductivity | Predicting |
Oil | 28 | Solid fuels, liquid fuels, gas fuels, renewable and non-renewable energy fuels | Inferring |
29 | Solid fuels, liquid fuels, gas fuels, renewable and non-renewable energy fuels | Communicating | |
Cardboard | 30 | Heat conductivity | Designing an experiment |
Tea | 31 | Homogeneous mixture, solution, dissolution, factors affecting dissolution rate, contact surface, mixing, temperature, solvent and soluble | Hypothesizing |
32 | Factors affecting the dissolution rate | Predicting | |
Ayşe's experiment | 33 | Factors related to heat and core heat | Predicting |
Cabbage juice | 34 | Acid, base, indicator, mixture and Ph | Conclusion-decision |
35 | Ph meter | Measuring | |
Markets | 36 | Physical and chemical changes | Classifying |
Periodical system | 37 | Group, period, and classification of periodical system | Observation |
38 | Nonmetal, metal and inert gas | Observation | |
Imported and exported products | 39 | Imported and exported chemical products | Observation |
Cake | 40 | Acid, base, heterogeneous mixture and formation of chemical reactions | Identifying variables |
41 | Acid, base, and formation of chemical reactions | Identifying variables | |
42 | Acid, base, and formation of chemical reactions | Hypothesizing | |
Atom | 43 | Atom, the nature of scientific knowledge, nucleus and electron | Modeling |
44 | Atom, the nature of scientific knowledge, nucleus, electron and layer | Modeling |
Test items were prepared in a way to include the aforementioned concepts at the middle school level. The items were included with B-SPS, C-SPS and E-SPS dimensions.
The content of the items in the SPST were in the form of problem scenarios prepared from socio-scientific issues by the researcher. In this stage, the types of questions, correct answers, distractors and what skill level is measured in scientific process skills tests in the related literature were examined. Some problem scenarios prepared in the science teaching and laboratory applications course which the researcher's carried out in previous periods were edited. Four academicians from different universities were consulted for opinion to ensure the suitability of the items. The opinions of five MoNE teachers who were attending courses at the Master's degree level in science education were also taken. The academicians and the teachers assessed the appropriateness of the items in terms of face and content validity. This way, the appropriateness of grade, concept and skill level was ensured. The skill levels of the test questions are determined by the researcher based on the types of questions in the literature. If there was hesitant as to whether some questions measure more than one skill level, and which skill level is more appropriate, these hesitations have been eliminated in the direction of the recommendations of the academicians and teachers. It is accepted that the questions measure a single skill level that is more concentrated. Thus, the final level of skill that the questions aimed at measuring was decided and Table 3 is prepared. Below, some of the items included in the B-SPS, C-SPS and E-SPS dimensions following the edition are given.
The B-SPS dimension sample item:
Item 15. Ayşe is a 5th grade student. Her teacher asks Ayşe to classify the concepts of melting, freezing, evaporation, condensation, deposition, and sublimation, which are in the “State Change of Matter” unit, into two groups according to heat exchange. Given that Ayşe performed this task correctly, which of the below do you think she must have done the classification with?
The C-SPS dimension sample item:
On a hot summer day, Semih feels like having an ice cream after school and he goes to the ice cream shop. He buys his ice cream packed. The ice cream man asks him how long it takes to get home for he will put dry ice in the package (dry ice is solid carbon dioxide. It transforms from solid to gas, in other words, sublimation). Semih says the journey home will take an hour. Upon arriving home, Semih unwraps his ice cream to realize that the dry ice is not there. He thinks the dry ice has melted but he realizes that there is no water in the package.
Item 7. Which of the following is the independent variable in this scenario?
(A) The amount of dry ice
(B) That the ice cream did not melt
(C) The time it took to go home from the ice cream shop
(D) A hot summer day
The E-SPS dimension sample item:
Item 16. Tayfun starts watching cartoons after finishing his homework. In the cartoon, there is the father snowman, snow children and a rabbit. Father snowman takes the rabbit's carrots. The rabbit threatens to spill salt and melt his children to get his carrots back. Father snowman is very afraid… (Snow children are exactly the same in terms of height, weight, etc.)
Tayfun is very surprised by the fact that the rabbit can melt the snow children only with salt and without heating. To test this, he decides to design an experiment for the next day's science lesson. If you were Tayfun, what kind of experiment would you suggest him doing?
(A) He should put the same volume of ice in two identical containers and add different amounts of salt into each container. He should measure the melting point of the mixtures using a thermometer.
(B) He should put different volumes of ice in two identical containers and add different amounts of salt into each container. He should measure the melting point of the mixtures with a thermometer
(C) He should put different volumes of ice in two identical containers and add the same amount of salt into each container. He should measure the melting point of the mixtures using a thermometer.
(D) He should put the same volume of ice in two identical containers and add the same amount of salt into each container. He should measure the melting point of the mixtures using a thermometer.
The SPST was developed in Turkish. Data were collected from 7th and 8th grade students in Turkey. Test items were translated back into English by an English language expert. Test items were re-translated back into Turkish and the back translation was compared with the original (Taber, 2018). The science education expert examined the similarities of each item by comparing each item's original Turkish version and its translation back into Turkish. The science education expert rated the translation validity.
Sub-dimensions | Item | Difficulty | Discrimination | Sub-dimensions | Item | Difficulty | Discrimination |
---|---|---|---|---|---|---|---|
a An item which has appropriate item difficulty and discrimination values. | |||||||
Observation | 37 | 0.25 | 0.19 | İnferring | 1a | 0.47 | 0.42 |
38a | 0.37 | 0.38 | 17 | 0.46 | 0.29 | ||
39a | 0.37 | 0.33 | 22a | 0.40 | 0.36 | ||
Measuring | 24 | 0.25 | 0.12 | 28 | 0.41 | 0.23 | |
35a | 0.56 | 0.51 | Defining operationally | 14a | 0.56 | 0.53 | |
Classifying | 5a | 0.65 | 0.46 | Hypothesizing | 9a | 0.41 | 0.36 |
15a | 0.61 | 0.58 | 13a | 0.63 | 0.67 | ||
20a | 0.49 | 0.46 | 21a | 0.44 | 0.38 | ||
36 | 0.29 | 0.26 | 25a | 0.39 | 0.35 | ||
Communicating | 4a | 0.39 | 0.42 | 31 | 0.24 | 0.18 | |
29a | 0.49 | 0.36 | 42a | 0.44 | 0.31 | ||
Recording data | 3a | 0.53 | 0.58 | Designing an experiment | 16a | 0.37 | 0.26 |
Predicting | 6a | 0.43 | 0.37 | 30 | 0.33 | 0.29 | |
27 | 0.31 | 0.28 | Modeling | 43 | 0.47 | 0.27 | |
32a | 0.45 | 0.33 | 44a | 0.39 | 0.29 | ||
33 | 0.41 | 0.28 | Conclusion-decision | 2 | 0.39 | 0.24 | |
Identifying variables | 7a | 0.51 | 0.41 | 18 | 0.31 | 0.18 | |
8a | 0.44 | 0.50 | 19a | 0.46 | 0.31 | ||
10a | 0.51 | 0.56 | 23a | 0.62 | 0.68 | ||
11a | 0.29 | 0.44 | 26a | 0.33 | 0.33 | ||
40a | 0.41 | 0.33 | 34 | 0.45 | 0.26 | ||
41a | 0.36 | 0.51 | Changing variables | 12a | 0.35 | 0.19 |
As shown in Table 4, since the discrimination indexes of item 37 in the observation sub-dimension, item 24 in the measuring sub-dimension, item 36 in the classifying sub-dimension, items 27 and 33 in the predicting sub-dimensions, items 17 and 28 in the inferring sub-dimensions, item 31 in the hypothesizing sub-dimension, item 30 in the designing an experiment sub-dimension, item 43 in the modeling sub-dimension, and items 2, 18 and 34 in the conclusion-decision sub-dimension are below 0.30, these questions cannot be regarded as adequately distinctive (Ebel and Frisbie, 1986) and they were excluded from the test. On the other hand, item 16 in the designing an experiment sub-dimension, item 44 in the modeling sub-dimension and item 12 in the changing variables’ sub-dimension were kept in the test considering their distractors’ operational statuses and the fact that the test includes items related to these sub-dimensions.
Item no. | B-SPS | C-SPS | E-SPS | |||
---|---|---|---|---|---|---|
t-Value | Error variances | t-Value | Error variances | t-Value | Error variances | |
3 | 8.64 | 0.71 | ||||
4 | 4.59 | 0.91 | ||||
5 | 6.67 | 0.82 | ||||
15 | 8.10 | 0.74 | ||||
20 | 7.02 | 0.80 | ||||
29 | 2.83 | 0.96 | ||||
35 | 3.86 | 0.94 | ||||
38 | 3.63 | 0.94 | ||||
39 | 2.92 | 0.96 | ||||
1 | 5.16 | 0.89 | ||||
6 | 4.97 | 0.90 | ||||
7 | 6.12 | 0.85 | ||||
8 | 5.63 | 0.87 | ||||
10 | 9.25 | 0.68 | ||||
11 | 5.72 | 0.87 | ||||
14 | 7.52 | 0.78 | ||||
22 | 4.42 | 0.92 | ||||
32 | 2.80 | 0.97 | ||||
40 | 1.78 | 0.99 | ||||
41 | 4.93 | 0.90 | ||||
9 | 2.52 | 0.97 | ||||
12 | 2.01 | 0.98 | ||||
13 | 9.21 | 0.68 | ||||
16 | 2.43 | 0.98 | ||||
19 | 6.18 | 0.85 | ||||
21 | 5.85 | 0.86 | ||||
23 | 8.75 | 0.71 | ||||
25 | 3.85 | 0.94 | ||||
26 | 2.08 | 0.98 | ||||
42 | 2.09 | 0.98 | ||||
44 | 2.05 | 0.98 |
An examination of Table 5 shows that item 40 is not significant. It can be seen that items 9, 12, 16, 26, 42, and 44 are 0.05 significant while all remaining items are 0.01 significant. At this point, item 40 was excluded. While there are numerous fit statistics preferred in structural equation modeling, the results of fit statistics used frequently are presented in Table 6.
χ 2/sd | GFI | AGFI | RMSEA | CFI | NNFI | RMR | SRMR |
---|---|---|---|---|---|---|---|
2.01 | 0.84 | 0.82 | 0.0059 | 0.84 | 0.83 | 0.017 | 0.070 |
In Table 6, the χ2/sd ratio is below 3, which means a good fit index (Kline, 2005). The fact that the RMSEA value is below 0.08 indicates an acceptable fit index (Jöreskog and Sörbom, 1993). The fact that the GFI and AGFI indexes are below 0.90 indicate that the fit index is poor (Hooper et al., 2008). The fact that the NNFI and CFI fit indexes are below 0.90 shows a poor fit index. RMR and standardized RMR values below 0.05 mean a good fit index (Brown, 2006) while these values being below 0.08 means acceptable fit indexes. It is seen that the results of the analysis give a good fit index RMR value and an acceptable fit index SRMR value. According to fit statistics, it can be considered that this test is a good model with all fit statistics (excluding GFI, AGFI, CFI, and NNFI) and that it is a valid test with its factor structures.
According to Table 7, there is a significant relationship of 0.01 between the sub-dimensions of the SPST.
Sub-dimensions | Skewness | Kurtosis |
---|---|---|
B-SPS | 0.422 | −0.173 |
C-SPS | 0.393 | −0.301 |
E-SPS | 0.354 | −0.371 |
T-SPS | 0.696 | 0.146 |
When Table 8 is examined, it is acknowledged that the data is within the normal distribution range as the skewness and kurtosis coefficients for the overall test and the sub-dimensions are between −1 and +1 (Morgan et al., 2004, p. 49).
Gender | Grade level | School location | MEL | FEL | Trips | Project | Fair | Competitions | Journal | |
---|---|---|---|---|---|---|---|---|---|---|
Mother's education level = MEL; father's education level = FEL. | ||||||||||
Gender | — | 0.009 | 0.078 | −0.066 | 0.020 | 0.046 | 0.155 | 0.033 | −0.032 | 0.117 |
Grade level | −0.028 | −0.003 | 0.007 | 0.046 | −0.110 | 0.197 | 0.017 | 0.038 | ||
School location | 0.220 | 0.281 | 0.026 | 0.026 | 0.189 | −0.051 | −0.028 | |||
MEL | 0.446 | 0.030 | 0.134 | 0.008 | −0.009 | 0.137 | ||||
FEL | 0.085 | 0.123 | 0.123 | 0.049 | 0.127 | |||||
Trips | 0.093 | 0.013 | 0.159 | 0.036 | ||||||
Project | −0.044 | 0.089 | 0.095 | |||||||
Fair | 0.008 | −0.058 | ||||||||
Competitions | −0.025 | |||||||||
Journal | — |
There is a multi-correlation between variables with high correlations such as 0.80 to 0.90 (Field, 2005, p. 224). When Table 9 is examined, it is seen that there is no high correlation between the predictive variables.
The second aim is to investigate whether there are any predictive effects of demographic features (gender, grade level, school location, mothers’ education level and fathers’ education level) and participating in out-of-school learning opportunities (participating in science trips, designing projects, participating in science competitions, reading scientific journals and participating in science fairs) on the 7th and 8th grade students’ B-SPS, C-SPS, E-SPS and T-SPS level. Multiple linear regression analysis was conducted and the data obtained are given in Tables 10–13.
Variables | B | Std. error | β | t | p | Zero-order (r) | Partial (r) |
---|---|---|---|---|---|---|---|
Constant | 2.150 | 0.253 | 8.492 | 0.000 | |||
Gender | 0.470 | 0.182 | 0.114 | 2.582 | 0.010 | 0.154 | 0.123 |
Grade level | 0.798 | 0.181 | 0.194 | 4.398 | 0.000 | 0.192 | 0.206 |
School location | 0.302 | 0.234 | 0.059 | 1.287 | 0.199 | 0.139 | 0.062 |
Mothers’ education level | 0.675 | 0.209 | 0.158 | 3.227 | 0.001 | 0.249 | 0.153 |
Fathers’ education level | 0.430 | 0.210 | 0.102 | 2.047 | 0.041 | 0.234 | 0.098 |
Trips | −0.409 | 0.209 | −0.085 | −1.953 | 0.051 | −0.032 | −0.093 |
Project | 0.726 | 0.190 | 0.170 | 3.821 | 0.000 | 0.205 | 0.180 |
Fair | 0.330 | 0.187 | 0.079 | 1.764 | 0.078 | 0.129 | 0.084 |
Competitions | 0.073 | 0.275 | 0.012 | 0.266 | 0.790 | 0.011 | 0.013 |
Journal | 0.590 | 0.181 | 0.143 | 3.256 | 0.001 | 0.205 | 0.154 |
Variables | B | Std. error | β | t | p | Zero-order (r) | Partial (r) |
---|---|---|---|---|---|---|---|
Constant | 2.868 | 0.258 | 11.126 | 0.000 | |||
Gender | 0.857 | 0.185 | 0.213 | 4.622 | 0.000 | 0.233 | 0.216 |
Grade level | 0.414 | 0.185 | 0.104 | 2.241 | 0.026 | 0.120 | 0.107 |
School location | −0.291 | 0.239 | −0.059 | −1.220 | 0.223 | 0.006 | −0.058 |
Mothers’ education level | 0.500 | 0.213 | 0.120 | 2.350 | 0.019 | 0.130 | 0.112 |
Fathers’ education level | 0.076 | 0.214 | 0.019 | 0.356 | 0.722 | 0.100 | 0.017 |
Trips | −0.174 | 0.213 | −0.037 | −0.814 | 0.416 | 0.001 | −0.039 |
Project | 0.449 | 0.194 | 0.108 | 2.322 | 0.021 | 0.151 | 0.111 |
Fair | 0.457 | 0.191 | 0.113 | 2.397 | 0.017 | 0.121 | 0.114 |
Competitions | 0.162 | 0.280 | 0.026 | 0.579 | 0.563 | 0.026 | 0.028 |
Journal | 0.437 | 0.184 | 0.109 | 2.368 | 0.018 | 0.160 | 0.113 |
Variables | B | Std. error | B | t | p | Zero-order (r) | Partial (r) |
---|---|---|---|---|---|---|---|
Constant | 2.424 | 0.274 | 8.851 | 0.000 | |||
Gender | 0.894 | 0.197 | 0.203 | 4.540 | 0.000 | 0.212 | 0.213 |
Grade level | 0.719 | 0.196 | 0.163 | 3.663 | 0.000 | 0.167 | 0.173 |
School location | 0.405 | 0.254 | 0.074 | 1.596 | 0.111 | 0.152 | 0.076 |
Mothers’ education level | 0.790 | 0.226 | 0.173 | 3.495 | 0.001 | 0.228 | 0.165 |
Fathers’ education level | 0.390 | 0.227 | 0.086 | 1.716 | 0.087 | 0.213 | 0.082 |
Trips | −0.494 | 0.227 | −0.096 | −2.183 | 0.030 | −0.033 | −0.104 |
Project | 0.384 | 0.206 | 0.084 | 1.866 | 0.063 | 0.138 | 0.089 |
Fair | 0.309 | 0.202 | 0.069 | 1.528 | 0.127 | 0.128 | 0.073 |
Competitions | 0.936 | 0.298 | 0.139 | 3.146 | 0.002 | 0.126 | 0.149 |
Journal | 0.213 | 0.196 | 0.048 | 1.087 | 0.277 | 0.108 | 0.052 |
Variables | B | Std. error | β | t | p | Zero-order (r) | Partial (r) |
---|---|---|---|---|---|---|---|
Constant | 7.442 | 0.605 | 12.291 | 0.000 | |||
Gender | 2.221 | 0.435 | 0.219 | 5.101 | 0.000 | 0.248 | 0.238 |
Grade level | 1.931 | 0.434 | 0.192 | 4.449 | 0.000 | 0.199 | 0.209 |
School location | 0.415 | 0.561 | 0.033 | 0.741 | 0.459 | 0.125 | 0.036 |
Mothers’ education level | 1.965 | 0.500 | 0.188 | 3.931 | 0.000 | 0.253 | 0.185 |
Fathers’ education level | 0.897 | 0.503 | 0.086 | 1.784 | 0.075 | 0.228 | 0.085 |
Trips | −1.077 | 0.501 | −0.092 | −2.151 | 0.032 | −0.027 | −0.103 |
Project | 1.560 | 0.455 | 0.149 | 3.430 | 0.001 | 0.204 | 0.162 |
Fair | 1.096 | 0.448 | 0.107 | 2.449 | 0.015 | 0.156 | 0.117 |
Competitions | 1.172 | 0.658 | 0.076 | 1.781 | 0.076 | 0.070 | 0.085 |
Journal | 1.239 | 0.433 | 0.123 | 2.862 | 0.004 | 0.194 | 0.136 |
The results of the analysis reveal that (see Table 10) gender, grade level, school location, mothers’ education level, fathers’ education level, participating in science trips, designing projects, participating in science fairs, participating in science competitions and reading scientific journals show a statistically significant relationship (R = 0.451; R2 = 0.204) with students’ B-SPS level (F(10-435) = 11.123; p < 0.05). The 10 variables together explain 20.4% of the change in B-SPS scores. Grade level (β = 0.194, p < 0.05), designing projects (β = 0.170, p < 0.05), mothers’ education level (β = 0.158, p < 0.05), reading scientific journals (β = 0.143, p < 0.05), gender (β = 0.114, p < 0.05) and fathers’ education level (β = 0.102, p < 0.05) are significant predictors of the B-SPS level.
The results of the analysis reveal that gender, grade level, school location, mothers’ education level, fathers’ education level, participating in science trips, designing projects, participating in science fairs, participating in science competitions and reading scientific journals show a statistically significant relationship (R = 0.357; R2 = 0.128) with students’ C-SPS level (F(10-435) = 6.360; p < 0.05). The 10 variables together explain 12.8% of the change in C-SPS scores. Gender (β = 0.213, p < 0.05), mothers’ education level (β = 0.120, p < 0.05), participating in science fairs (β = 0.113, p < 0.05), reading scientific journals (β = 0.109, p < 0.05), designing projects (β = 0.108, p < 0.05) and grade level (β = 0.104, p < 0.05) are significant predictors of the C-SPS level.
The results of the analysis reveal that gender, grade level, school location, mothers’ education level, fathers’ education level, participating in science trips, designing projects, participating in science fairs, participating in science competitions and reading scientific journals show a statistically significant relationship (R = 0.431; R2 = 0.186) with students’ E-SPS level (F(10-435) = 9.928; p < 0.05). The 10 variables together explain 18.6% of the change in E-SPS scores. Gender (β = 0.203, p < 0.05), mothers’ education level (β = 0.173, p < 0.05), grade level (β = 0.163, p < 0.05), participating in science competitions (β = 0.139, p < 0.05) and participating in science trips (β = −0.096, p < 0.05) are significant predictors of the E-SPS level.
The results of the analysis reveal that gender, grade level, school location, mothers’ education level, fathers’ education level, participating in science trips, designing projects, participating in science fairs, participating in science competitions and reading scientific journals show a statistically significant relationship (R = 0.493; R2 = 0.243) with students’ T-SPS level (F(10-435) = 13.932; p < 0.05). The 10 variables together explain 24.3% of the change in T-SPS scores. Gender (β = 0.219, p < 0.05), grade level (β = 0.192, p < 0.05), mothers’ education level (β = 0.188, p < 0.05), designing project (β = 0.149, p < 0.05), reading scientific journals (β = 0.123, p < 0.05), participating in science fairs (β = 0.107, p < 0.05) and participating in science trips (β = −0.092, p < 0.05) are significant predictors of T-SPS level.
When predictive effects of all variables were examined on middle school students’ SPS level, it is understood that the predictive effect on T-SPS scores (%24.3) is maximal and the predictive effect on C-SPS scores (%12.8) is minimal.
In the current study, the predictive effects of demographic features on the students’ B-SPS, C-SPS, E-SPS and T-SPS level was investigated. Within this aim, it was determined that gender in favor of female students is a significant predictor of B-SPS, C-SPS, E-SPS and T-SPS level. This finding is consistent with the results of Zeidan and Jayosi (2015)'s study which they conducted with middle school students, and reported that the SPS level of female students was higher on average than male students. Similar findings were reported by Karar and Yenice (2012a), who carried out a study with 8th grade students, and used the scientific process skills test that was developed by Burns et al. (1985). Conversely, it has been also reported in the literature that the SPS level of middle school students was not statistically different in terms of gender (Walters and Soyibo, 2001; Böyük et al., 2011; Güden and Timur, 2016).
In addition, the predictive effect of the grade level on the students’ B-SPS, C-SPS, E-SPS and T-SPS level was the other aim of the study. It was found that being at 8th grade was a statistically significant predictor of B-SPS, C-SPS, E-SPS and T-SPS level. In the study of Güden and Timur (2016), the researchers investigated the effect of grade level on the middle school students’ SPS, and reported that the SPS level of 5th, 6th and 8th grade students was higher than those of 7th grade students. Another study by Böyük et al. (2011) stated that there was a significant difference between 6th, 7th and 8th grade students’ SPS in favor of the 8th graders.
Another finding of this study was that school location did not have a predictive effect on the students’ B-SPS, C-SPS, E-SPS and T-SPS level. This result is not consistent with Zeidan and Jayosi (2015)'s study that was conducted with middle school students and reported that the SPS level of students in rural middle schools was higher on average than those in urban middle schools.
The results of this study revealed that mothers’ education level, in the case of the mother graduating from high school or university, was a significant predictor of students’ B-SPS, C-SPS, E-SPS and T-SPS level for 7th and 8th grade students. Böyük et al. (2011) also reported a significant difference between the SPS level of the students whose mothers are high school or university graduates and those of the students whose mothers are primary school graduates. A similar result was also put forward by Ocak and Tümer (2014). The studies by Karar and Yenice (2012a) and Germann (1994) also reported that the SPS of students differed according to their parents’ education level.
On the other hand, based on the results it was found that fathers' education level, in case of father graduated from high school or university, was a significant predictor of students' B-SPS level. The fathers’ education level did not have a predictive effect on the students’ C-SPS, E-SPS and T-SPS level. Böyük et al. (2011)'s study also expressed that there was a significant difference between the SPS level of the students whose fathers are university graduates and those of the students whose fathers are primary school graduates. Ocak and Tümer (2014)'s study reported similar results, stating that the SPS level of the students with university or high school graduate fathers was higher on average than those whose fathers are primary or middle school graduates.
Another question in this study was to investigate the predictive effects of participating in out-of-school learning opportunities (participating in science trips, designing projects, participating in science competitions, reading scientific journals and participating in science fairs) on the 7th and 8th grade students’ B-SPS, C-SPS, E-SPS and T-SPS level. The results of the study revealed that reading scientific journals or designing projects were significant predictors of B-SPS, C-SPS and T-SPS level. In addition, participating in science fairs was a significant predictor of C-SPS and T-SPS level. Karar and Yenice (2012b) suggested that various projects should be organized in order that the SPS level of the students be improved. This suggestion can be interpreted as the indicator of the difference between the SPS level of the students that design projects or not. It was reported that the improvement of students’ scientific discussion skills contributes to learning difficult topics in science centers (Guisasola et al., 2005, p. 549). It has been stated that trips to museums contribute to the improvement of the students’ observing, collecting data, inference and comment abilities (Chin, 2004; Griffin, 2004; Guisasola et al., 2005).
Based on the findings, the most important predictive variables on student's SPS level were gender, grade level and mothers’ education level from the demographical features when all variables were considered. The significant predictive effect of the grade level on the SPS level of the students can be interpreted as an indicator that any test cannot be applied to every age group. This result emphasizes the necessity of not ignoring the field knowledge while developing the skill test. From the studies conducted on the effect of gender on students’ acquisition of SPS, it was observed that the findings were inconsistent. While some studies reported a significant gender difference in the levels of SPS (Onukwo, 1995), others reported no significant difference in students’ acquisition of SPSs with respect to gender (Ofoegbu, 1984). Therefore, the effect of the gender variable on students’ level of SPS should be investigated in other studies in order to contribute to the debate. It has been also reported in the literature that the parents’ education background enhanced the rate of acquisition of the students’ SPS (Martina, 2007).
Additionally, the effects of out-of-school learning opportunities such as reading scientific journals, designing projects or participating in science fairs were important predictors of the students’ SPS level. The study of McMillan and Schumacher (2010) stated that scientific research was the process in which data are collected and the collected data are analysed in order to reach certain objectives. Thus, students learn the scientific research process when doing scientific research. The result of this study revealed that the students who read scientific journals, participate in science fairs and design projects were more engaged with SPS. Nnachi (1988) also reported that the students improve their SPS while visiting various interesting places (zoo, museum, botanical gardens, etc.) with their families. In Sullivan (2008)'s study, it was investigated how students utilized their thinking skills and SPS characteristics to solve robotics challenges. And, it was emphasized that learning robotics was related to thinking skills, SPS, and science literacy. The aforementioned study also expressed that the technological design and computer programming activities that are inherent in robotics projects as an out-of-school activity can provide an applied method of learning skills and SPSs.
It has been stated in the educational research literature that there are many studies which have been conducted on some factors affecting students’ acquisition of SPSs. However, these studies were only conducted with a few factors as independent variables. Also, the modeling studies that can explain the predictors of students’ SPS level in a holistic manner were very limited. In addition to the educational research literature, the degree of importance of some predictor variables affecting students’ SPS level has been determined in this study in a holistic manner.
Teachers should take students’ grade level and subject knowledge into account in the selection of data collection tools that will be used to determine students’ SPS levels. Because of the significant predictive effect of parents’ educational level on skills, family activities that will enhance the skill level of students are important. Basic skills are the prerequisite of higher level ones (Rambuda and Fraser, 2004). While basic skills can be earned from the pre-school period, high level skills can be gained from the middle school level. If parents are insufficient, this should be determined by teachers and these deficiencies should be accomplished by their teachers.
Schools should provide opportunities for students to do research. For this, teachers should encourage their students to do research, prepare a project and encourage them to participate in science fairs. Students should be offered learning environments to participate in such out-of-school learning opportunities.
Lavinghousez (1973) puts forward the idea that the optimum way of identifying the SPS level of individuals was through observations, presentations and laboratory practices. In order to be able to explain the change in the students’ SPS, it is necessary to show how much they understand and how much they can apply (Buck et al., 2008; Pyle, 2008). The reasons behind this situation can be detailed through interviews with students as well as through observation in their learning environment, which will reflect their skills. Educational policy-makers are interested about the factors that have the most effect on the development of students’ SPS. If policy-makers know what is most effective in improving students’ SPS, then would this help in their planning policies around these most influential factors. In this study, visiting science fairs from out-of-school learning activities was found to be among the most significant predictors of students’ SPS levels. Based on the findings, policies could be developed so as to include more extracurricular activities for students. Educational institutions are only responsible for the factors they can control. Therefore, for example, it is important to know which factors originate from institutions and which originate from students or other factors. There may be a predictive effect of other variables on students’ SPS level. The results of this study are limited to the predictive effects of demographic features and out-of-school learning opportunities on students’ SPS level. The predictive effect of the success level and the frequency of students’ using the laboratory, and affective factors such as attitude, perception and motivation on students’ SPS level can be investigated. It is difficult to develop a universal model that determines the predictors of SPS. More comprehensive studies on the predictors of SPS will be helpful for educational policy-makers, managers, researchers and teachers.
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