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DOI: 10.1039/C8RP00018B (Paper) Chem. Educ. Res. Pract., 2018, 19, 746-754

Can cognitive structure outcomes reveal cognitive styles? A study on the relationship between cognitive styles and cognitive structure outcomes on the subject of chemical kinetics

Elif Atabek-Yigit
Department of Science Education, Sakarya University, 54300, Sakarya, Turkey. E-mail: eatabek@sakarya.edu.tr

Received 15th January 2018 , Accepted 5th April 2018

First published on 6th April 2018

Abstract

Determination of the relationship between individuals’ cognitive styles and cognitive structure outcomes was the main aim of this study. Sixty-six participants were enrolled in the study and their cognitive styles were determined by using the Hidden Figure Test (for their field dependent/independent dimension of cognitive style) and the Convergent/Divergent Test (for their convergence/divergence dimension of cognitive style). An open-ended questionnaire was formed in order to determine participants’ cognitive structure outcomes. The study topic was chosen as chemical kinetics since it is one of the most difficult topics in chemistry according to many students and also there is limited study in the literature on this topic. Key concepts about chemical kinetics were selected and given to the participants and they were asked to write a text by using the given concepts. A flow map technique was used to reveal participants’ cognitive structure outcomes. According to the findings of this study, it can be said that field independent participants tended to be divergent thinkers while field dependents tended to be convergent thinkers. Also, strong positive relationships between participants’ field dependency/independency and some cognitive structure outcomes (extent and richness) were found. That is, field independents tended to have more extended and richer cognitive structure outcomes. However, the convergence/divergence dimension of cognitive style did not show any correlation with cognitive structure outcomes.

Introduction

Everyone is exposed to many stimuli during his/her everyday life, and these stimuli have an effect on perception and learning processes. Different perceptions and learning processes can occur for different individuals even if they receive the same information or are exposed to the same stimuli. Cognitive structure, which has been categorized by Ausubel (1968 as cited in Wilson, 1999) as an intrapersonal factor that affects learning, is defined as a hypothetical structure of participants’ long-term memory. It shows how someone organizes and relates concepts and is the basis for new knowledge (Tsai, 2001; Tsai and Huang, 2002). Cognitive style can be defined as individuals’ collecting, organizing and evaluating way of knowledge. Psychologists consider cognitive style as a part of personality and it does not show drastic change over time (Al-Naeme, 1991; Alamolhodaei, 1996; Bahar and Hansell, 2000). Cognitive style and ability are seemed to have the same meaning. However, ability represents a power to do, whereas cognitive styles are the way the power is used, as reported by Bahar and Hansell (2000).

In the literature, many dimensions of cognitive style have been defined. Field dependency and independency is the dimension that originates from Witkin's (1977) work. According to Witkin and Goodenough (1981), a person who has difficulty in separating an item from its context is described as field dependent, whereas a person who easily separates relevant information from its context is described as field independent. Field independents perceive and process information analytically while field dependents use a more global and holistic way to process the information (Alamolhodaei, 1996). Field independent individuals find more abstract and theoretical information more interesting and tend to focus on them while field dependent individuals are better in social and concrete concepts (Alamolhodaei, 1996). There are many studies in the literature (Tinajero and Paramo, 1997; Bahar and Hansell, 2000; Danili and Reid, 2006; Karacam and Ates, 2010) that examine the relationship between field dependency/independency and students’ academic achievement. Most of them have examined the relationship between cognitive styles of the students and the format of exams. The common finding of these studies says that field independents have higher scores in multiple-choice tests (Al-Naeme and Johnstone, 1991; Alamolhodaei, 1996; Karacam and Ates, 2010), structural grid questions (Bahar and Hansell, 2000), and problem-solving (Tsaparlis and Angelopoulos, 2000; Ates and Cataloglu, 2007).

Another cognitive style dimension is convergence/divergence. Convergent thinking is defined as to converge (focus down) to one right answer in order to solve the problem and convergent thinkers are good at intelligent tests which require one specific answer from the given information. Divergent thinking, on the other hand, is defined as diverge responses, i.e., exploring and expanding ideas. The convergence/divergence dimension of cognitive style originated in Hudson's (1966) work. He prepared open-ended tests to measure this type of cognitive style and asked the different meanings of words and different uses of objects. Divergers generate several acceptable responses, and therefore, can get higher scores from this open-ended test while convergers can get higher scores from tests that require one conventionally acceptable answer. He named a high IQ learner as a converger and a high creative learner as a diverger (Bahar and Hansell, 2000). In Danili and Reid's study (2006) they found out that the convergence/divergence dimension of cognitive style had a correlation with students’ test performance where language was an important factor. There are studies in the literature researching the relationship between cognitive styles. According to Bahar and Hansell (2000), field independency and divergency have a positive correlation, that is field independent individuals tended to be divergers. Al-Naeme (1991) found that field independent and divergent was the more effective combination of cognitive styles for students’ performance in practical problem-solving in chemistry (such as mini-projects). These most used dimensions of cognitive styles were also used in this study to identify the participants’ cognitive styles.

There are many methods and techniques representing cognitive structures of individuals. Flow mapping in which both sequential and network features of people's thoughts were represented (Tsai, 2001) is a very valuable technique. It can be formed either from an individual's written narrative or spoken discourse. In the construction of a flow map, first the written narrative or spoken discourse of a respondent is transcribed into sequential sentences. Then linear arrows, which show how a respondent expresses his/her ideas, and recurrent arrows, which show connections among relational ideas, are formed. After that, various parameters can be calculated. For instance, extent, richness, integratedness, and misconception are some of the parameters that can be determined from a flow map. Extent that is the number of linear linkages shows the size of respondent's ideas. The number of recurrent linkages is given as richness and shows how the respondent linked the ideas. The ratio of recurrent linkages to the total number of linkages is defined as integratedness and is an important parameter in order to identify individuals’ conceptualization. Misconceptions can also be detected from flow maps. Moreover, flow maps can be examined from the information processing point of view, which could provide a deeper understanding of an individual's reasoning when he/she is constructing knowledge.

Cognitive structure outcomes can show how individuals’ understand and relate concepts. In this study, it was aimed to explore whether there was a relationship between them and cognitive styles, which affects individuals’ academic achievement, perceptions, and understanding. For this purpose, a correlational research design was carried out and correlation analyses were performed between cognitive structure outcomes of the participants and their cognitive styles. The findings of this study can help to design better learning environments, that is if there were a correlation between cognitive styles and cognitive structure outcomes, educators could take into account learners’ cognitive styles while constructing learning environments in order to obtain better cognitive structure outcomes which eventually leads to getting more meaningful and permanent learning outcomes and better achievement.

Methods

Participants

Participants of this study were 66 undergraduate students (11 male and 55 female) studying Science Education at a public university located in northwest Turkey. They were in their first year at university and taking the General Chemistry II course. Since the study subject was chosen as chemical kinetics and this topic is covered in the General Chemistry II course, these students were asked to participate in the study. All participants participated voluntarily.

Instruments

In this study, data were collected through the Hidden Figure Test (HFT), the Convergent/Divergent Test (CDT) and an open-ended questionnaire.
Hidden figure test (HFT). This test aims to identify participants’ degree of field dependence/field independence. Based on Witkin's (1977) work, the HFT is the most common test over the years to identify individuals’ field dependency and used in many studies (for instance, Danili and Reid, 2006; Mlyniec and Bednarek, 2016) in the literature. The HFT consisted of 18 complex figures. There are six geometric and non-geometric shapes embedded in these complex figures (one shape in each complex figure) and participants were asked to find these shapes. There are explanations of the test and two examples at the beginning of the test booklet and then six shapes, which are needed to be found from complex figures, are given on a separate page. Then 18 complex figures are given one after the other. 15 min is the time limit to complete this test. An example from the HFT is shown in Fig. 1.
image file: c8rp00018b-f1.tif
Fig. 1 A sample from the HFT.
Convergent/divergent Test (CDT). Based on Getzels and Jackson's (1962) original work, Al-Naeme (1991) has developed a convergent/divergent test, which comprises of six mini-tests. In each mini-test, a different ability of the participant is measured. Each mini-test has explanations and examples and a limited time to complete.

In Test 1, participants’ ability to produce as many different words as possible of the same or similar meaning to those given is measured. “Strong”, “dark”, and “clear” were the words given in this test. This test is to be completed in 5 min. Four specific words are given in Test 2 and participants are asked to write as many sentences as possible that covers all given words. 5 min is given for this test. For instance, participants were given “write-words-long-often” and needed to write as many sentences as possible with these words. Test 3 is a pictorial test in which participants are needed to draw up to 5 pictures for a given word or phrase in 5 min. For instance, participants were asked to draw pictures for “happy” and “post office”. In Test 4 participants are asked to write all the things “which are round or round more often than any other shape”. 3 min is the time limit for this test. In Test 5 they are needed to write as many words as possible beginning and ending with a specified letter. These two tests are aimed to measure individuals’ thinking ability about subjects. In the last test, participants’ ability in composition and imagination is aimed to be measured, and they are asked to write as many ideas as possible relating to the given situation (for instance, “crossing the stream”) in 4 min. The total amount of time for the CDT is 25 min. The CDT is originally in English, however the Turkish translation of this test was administered to the participants.

Open-ended questionnaire. This questionnaire was developed by the researcher to reveal participants’ cognitive structure outcomes. The study topic was chosen as chemical kinetics, which is a difficult subject according to students and a big concept on which students have many misconceptions as found by many studies (as reviewed in Bain and Towns, 2016). It is also an important concept since it underlies chemical equilibrium, which is another big concept that students have difficulties in understanding (Tastan Kirik and Boz, 2012). In order to form the questionnaire, a few general chemistry textbooks (Brown et al., 1991; Atkins and Jones, 1997; Hill and Petrucci, 1999) were examined for the main concepts of the chemical kinetics unit. Twenty concepts were written and they were discussed with a professor who teaches chemistry in the science education department for years. After that 13 concepts (chemical kinetics, reaction rate, rate constant, temperature, endothermic reaction, exothermic reaction, concentration, the order of reaction, the half-life of a reaction, catalysis, activation energy, contact surface, and the Arrhenius equation) related to chemical kinetics were determined and an open-ended questionnaire was formed with these concepts. Participants were asked to write a meaningful text that covers all the given concepts. A diagnostic test or any other instrument was not used to gather data since participants' not only misconceptions but their cognitive structures were aimed to disclosure. Also, it was thought that by not restricting participants’ responses with some choices, i.e. like in multiple-choice or matching tests, data with more depth could be obtained. However, some key concepts were given in this questionnaire to structure the study and to interpret the findings. Participants completed this questionnaire in about 30 min.

Data collection and procedure

Data were collected through three instruments. Explanations and examples of each instrument were made at the beginning of each test and by doing so it was aimed to get all the participants to understand the procedure and therefore to collect more appropriate data. Participants’ degree of field dependency was measured with the HFT in which they were asked to find and outline the hidden shape in pen or pencil on the lines of each complex figure.

Each mini-test in the CDT was explained and examples were made together and then mini-tests were administered within a specified time limit.

Lastly, an open-ended questionnaire was given to the participants and then they were asked to write a meaningful text about the chemical kinetics unit while covering all the given concepts. By doing so it was aimed to encourage participants to write down all their knowledge about the given concepts and to show the understanding they have with these concepts.

All data were collected in a classroom with pencil and paper. After all the instruments were administered, participants were then thanked for their contribution.

Analysis of data

The HFT and CDT were both translated into Turkish before administration, and then analysis was also done in Turkish.
Determination of participants’ cognitive styles. Data obtained through the HFT were analyzed as follows. Firstly all participants’ answers, i.e., drawings, were evaluated and a total score, which can be a maximum of 18, for each participant was calculated. Then mean and standard deviation for these scores were calculated. Participants’ field dependency was determined according to the criterion proposed by Al-Naeme (1991), that is participants who scored more than half a standard deviation above the mean score were categorized as field-independent (FI), while participants who scored less than half a standard deviation below the mean score were considered as field-dependent (FD). An intermediate category as field-intermediate (FInt) was formed for the participants who scored between ±0.5 standard deviation.

For the calculation of CDT scores, participants’ scores from each of the mini-tests were calculated and summed. Each correct answer was given 1 point, and the maximum score that can be obtained from the CDT is 130. The mean and standard deviation were calculated from this data and participants were classified according to Hudson's (1966) criterion that considers 0.5 standard deviation (SD) as the critical point. That is, participants who had scores higher than mean score + 0.5 SD were classified as divergent thinkers (DIV) while participants who had scores lower than mean score − 0.5 SD were classified as convergent thinkers (CONV). Participants who had scored between ±0.5 standard deviation were considered as all-rounders (All-R).

Determination of participants’ cognitive structure outcomes. Data obtained from open-ended questionnaires were analyzed according to the flow mapping method, which shows sequential and multi-relational ideational frameworks of participants (Tsai, 2001). Firstly, participants’ texts were re-written as consecutive sentences and flow maps for each participant were formed. All statements were then numbered and linear and recurrent linkages were formed. Linear linkages show the flow of the text and recurrent linkages show re-visited ideas among the statements. A sample flow map is shown in Fig. 2. Linear arrows were drawn between consecutive statements. Recurrent arrows were drawn between statements that have common, i.e. revisited, ideas. In Statement 2 in Fig. 2, for instance, “temperature is one of the factors that affects reaction rate” there was a revisited idea “reaction rate”, therefore a recurrent arrow was drawn back to Statement 1 where “reaction rate” was first appeared. After examination of all the flow maps from this point of view, four parameters, i.e. extent, richness, integratedness, and misconception, were calculated from them. Extent shows the total number of statements. There were 13 statements, for instance, in Fig. 2, therefore 13 was recorded as this flow map's extent. The number of recurrent linkages is given as richness, e.g., 14 for the sample flow map presented in Fig. 2. Integratedness is the proportion of recurrent linkages, that is (number of recurrent linkages)/(number of recurrent linkages + number of linear linkages). Integratedness was calculated to be 0.52 for instance for the flow map given in Fig. 2. The number of scientifically incorrect statements is shown by misconceptions.
image file: c8rp00018b-f2.tif
Fig. 2 A sample flow map.

Data were further analyzed according to the information processing modes point of view and categorized into four categories i.e., defining, describing, comparing, and inferring. If a statement gives a definition of a concept, for instance, Statement 7 in Fig. 2 “catalyst is a matter that increases the reaction rate,” was categorized as the defining mode. Describing is picturing a phenomenon or a fact. The first statement in Fig. 2 “reaction rate is shown by k” was categorized as the describing mode since it portrays the reaction rate. If a statement includes a comparison of two or more concepts, they are categorized as the comparing mode. Statements that explain what will happen under certain conditions, for instance, Statement 6 in Fig. 2, “Formula of half-life changes if reaction rate changes,” were categorized as the inferring mode. After categorizing all the data, each participant's factors, as well as their HFT and CDT scores, were entered in SPSS 20.0 and the data were analyzed.

The relationships between cognitive styles, i.e., participants’ field dependency/independency and convergency/divergency, cognitive structure outcomes, i.e., extent, richness, integratedness, and misconceptions, and information processing modes, i.e., defining, describing, comparing, and inferring, were analyzed through correlation analysis. In order to examine if there were any relationship between participants’ cognitive styles and cognitive structure outcomes, correlation analysis between HFT and CDT scores with cognitive structure outcomes and information processing modes was performed. Due to the observation of the normal distribution of the data, Pearson's r correlation coefficients were reported. Misconceptions of the participants were also listed, and a frequency table was formed.

Validity and reliability

The HFT and CDT are both valid and reliable tests that were used in several studies (for instance, Al-Naeme, 1991; Alamolhodaei, 1996; Bahar and Hansell, 2000; Danili and Reid, 2006) over the years. Since translated versions of these instruments were used for this study, their reliabilities were calculated for checking the internal consistency for the present population. Cronbach's alpha reliability coefficient for the CDT was found to be 0.733, and the split-half reliability for the HFT was calculated to be 0.708. Both of them (>0.7) can be interpreted as acceptable (George and Mallery, 2003).

For the validity and reliability of the open-ended questionnaire, at the determination of concepts stage, a professor who holds a PhD degree in chemistry and has given chemistry courses for several years at university examined and discussed the concepts. For the reliability of the flow map method, another researcher was asked to examine 15 random flow map data and an inter-coder agreement of 83%, which shows the reliability of this method (Miles et al., 2014), was calculated.

Results

Relationship between cognitive styles

Descriptive statistics of participants’ HFT scores are given in Table 1. Participants were classified as field-independent (FI), who scored more than half a standard deviation above the mean score, field-dependent (FD), who scored less than half a standard deviation below the mean score, and field intermediate (FInt), who scored in between these points as suggested by Al-Naeme (1991). The number of participants for each category is given in Table 2.
Table 1 Descriptive statistics of HFT scores
N Minimum Maximum Mean Std. deviation
HFT score 66 2.00 17.00 11.485 2.862

Table 2 Classification of participants in terms of field dependence/independence
Field dependent (FD) Field independent (FI) Field intermediate (Fint) Total
Number of participants 22 35 9 66
33.3% 53% 13.7% 100%

According to Table 2, it can be said that a majority of the participants were field independents (53%).

Table 3 shows the descriptive statistics of CDT scores. In order to classify the participants according to their convergent/divergent thinking styles, a formula suggested by Hudson (1966) was used. Participants who scored more than half a standard deviation above the mean score was classified as divergents, and participants who scored less than half a standard deviation below the mean score was classified as convergents. All-rounders had scored in between them. The number of participants in each category are seen in Table 4.

Table 3 Descriptive statistics of CDT scores
N Minimum Maximum Mean Std. deviation
CDT score 66 34.00 85.00 56.046 11.912

Table 4 Classification of participants in terms of convergent/divergent thinking styles
Convergent (CON) Divergent (DIV) All-rounder (All-R) Total
Number of participants 25 30 11 66
37.9% 45.4% 16.7% 100%

The result of correlation analysis between participants’ HFT and CDT scores, which show their cognitive styles, is given in Table 5. A weak negative relation (r(66) = −0.295, p < 0.05) was observed between HFT and CDT scores of the participants. That is, field independent participants tended to be divergent thinkers while field dependents tended to be convergent thinkers.

Table 5 Correlation between participants’ field dependency/independency and convergent/divergent thinking
HFT ConvDiv
a Correlation is significant at the 0.05 level (2-tailed).
HFT Pearson correlation 1 −0.295a
Sig. (2-tailed) 0.016
N 66 66
ConvDiv Pearson correlation −0.295a 1
Sig. (2-tailed) 0.016
N 66 66

Relationship between cognitive styles and cognitive structure outcomes

Another correlation analysis was performed between HFT and CDT scores, and cognitive structure outcomes and results are given in Table 6. Strong positive relations between HFT scores and extent (r(66) = 0.445, p < 0.01) and HFT scores and richness (r(66) = 0.345, p < 0.01) were found. That is, field independents tended to have more extended and richer cognitive structure outcomes. CDT scores did not show any correlation with cognitive structure outcomes (p > 0.05).
Table 6 Correlation between HFT and CDT scores with cognitive structure outcomes
Extent Misconception Richness Integratedness
a Correlation is significant at the 0.01 level (2-tailed).
HFT Pearson correlation 0.445a −0.091 0.345a 0.170
Sig. (2-tailed) 0.000 0.469 0.005 0.173
N 66 66 66 66
ConvDiv Pearson correlation −0.139 −0.008 −0.096 −0.023
Sig. (2-tailed) 0.266 0.950 0.444 0.852
N 66 66 66 66

Table 7 shows the results of correlation analysis performed to see if there were any relation between HFT and CDT scores with information processing modes of participants’ cognitive structures. There were no significantly meaningful correlations between participants’ cognitive styles and information processing modes (p > 0.05).

Table 7 Correlation between HFT and CDT scores with information processing modes
Defining Describing Comparing Inferring
HFT Pearson correlation −0.146 −0.081 −0.017 0.067
Sig. (2-tailed) 0.241 0.517 0.893 0.591
N 66 66 66 66
ConvDiv Pearson correlation 0.192 −0.049 0.016 −0.045
Sig. (2-tailed) 0.123 0.695 0.898 0.723
N 66 66 66 66

Participants’ misconceptions about chemical kinetics

Participants’ flow map analysis also revealed their misconceptions about chemical kinetics and a total of 24 misconceptions were found and they were classified under similar categories. Among them, the most frequent category was about the difficulties in defining half-life of a reaction (30%). Some participants stated, “half-life of a reaction is half of the time required for all reactants to react”. This misconception probably arose from the misunderstanding of the reaction rate. They may have thought that reactions occur with a constant rate, that is they are not aware of the instantaneous reaction rate or the initial reaction rate. Below is a direct excerpt from a participant that had this misconception:

Half-life of a reaction is half of the time required for all reactants to react. For instance, if a reaction completed in 2 seconds its half-life would be 1 second.

Other common misconceptions were classified under difficulties in understanding and interpreting reaction rate (26.7%) category. Many participants had the idea that they should take into consideration the stoichiometric coefficients when writing the reaction rate equation. An example of a direct excerpt is:

We can write the rate of the reaction according to the coefficients that were given in the reaction equation.

The relationship between temperature and reaction rate was another common misconception category. Many participants have the misconception that “as temperature increases reaction rate increases”. They mostly confused the relation between the energy needed for a reaction to occur, i.e., activation energy, and temperature as an indicator of energy. Here is an example explanation from one participant:

An increase in temperature would increase the rate of the reaction because reactions need energy to occur and increase in temperature can give this energy.

Misconceptions of the participants are given in Table 8.

Table 8 Misconceptions of the participants about chemical kinetics (N = 66)
Misconception Number and frequency (in %)
Difficulties in defining half-life of a reaction 18 (30)
Half-life of a reaction is half of the time required for all reactants to react
If the amount of reactants increases, the half-life of the reaction decreases
Half-life of a reaction is the time required for half of the reaction to complete
If the amount of reactants increases, the half-life of the reaction increases
Half-life of a reaction is related to the amount of reactants in the middle of the reaction
Half-life of a reaction is the time required to form half of the reactants
Difficulties in defining and interpreting reaction rate 16 (26.7)
Reaction stoichiometry should be taken into consideration when writing the reaction rate
If the amount of reactants increase, the reaction rate increases
Order of the reaction is important when writing the rate constant
Rate of the reaction is shown by k
If the amount of reactants increase, the rate constant decreases
Relation between temperature and reaction rate 12 (20)
As temperature increases, the reaction rate increases
The only factor affecting the reaction rate is temperature
In endothermic reactions, the reaction rate increases due to the decrease in temperature of the reaction
In exothermic reactions the reaction rate decreases due to the decrease of the temperature of the reaction
Temperature does not affect the rate constant
Difficulties in understanding the function of the catalyst 9 (15)
Catalyst does not affect the reaction rate
A catalyst can initiate a reaction
Catalyst decreases the reaction rate
Catalyst increases or decreases the activation energy
Catalyst increases the activation energy of the reaction
A catalyst initiates a reaction by lowering its activation energy
Contact surface and reaction rate relation 5 (8.3)
As the contact surface increases the rate of the reaction decreases
As the contact surface decreases the rate of the reaction increases

Discussion and conclusion

Based on the findings of this study, it can be said that there was a correlation between HFT and CDT scores of the participants (r(66) = −0.295, p < 0.05), that is field independent participants tended to be more divergent and field dependents tended to be more convergent. This correlation between field dependency/independency and the convergent/divergent dimension of cognitive style was also found by some other studies (Al-Naeme, 1991; Alamolhodaei, 1996; Bahar and Hansell, 2000; Danili and Reid, 2006) in the literature.

This study was aimed to determine if there was a relationship between cognitive styles of participants and their cognitive structure outcomes. Although there were studies in the literature related to the determination of cognitive styles and cognitive structure outcomes separately, to my knowledge this study is the one that researches the relation between them. A strong correlation between HFT scores of participants and extent (r(66) = 0.445, p < 0.01) and richness (r(66) = 0.345, p < 0.01) of their cognitive structure outcomes was found. That is, field independent participants tended to have a greater extent and richness of their cognitive structures. Extent, i.e., the number of linear linkages in the flow map, and richness, i.e., the number of recurrent linkages, of students’ cognitive structures were found to be correlated with academic achievement in previous studies (Tsai, 1998; Anderson et al., 2001; Tsai, 2001; Atabek-Yigit, 2015). Students, who recalled more knowledge can respond to a question with many ideas and thoughts, i.e. have a greater extent, and show higher academic achievements in examinations. They also can have more links in their thoughts, that is they have more richness in their knowledge.

Moreover many studies in the literature concluded that field independent students show greater academic achievements. Al-Naeme (1991) for instance has reported that field independents have had greater achievement in performing practical problem-solving in chemistry. Tsaparlis (2005) has explored the relationship between non-algorithmic quantitative problem-solving in physical chemistry and some cognitive factors, such as scientific reasoning, working memory capacity, functional M capacity and disembedding ability (field dependency/independency), among them disembedding ability showed a high positive correlation, that is field independents are higher achievers in that study. According to Alamolhodaei (1996), field independency has a positive effect on achievement in calculus. Many other studies have found positive correlations between academic achievement and field independent style. Tinajero and Paramo (1997) concluded that field independent students show greater academic achievements than field dependent ones whether the assessment is of specific discipline or across the board. The findings of this study combine those (the relation between some cognitive structure outcomes and academic achievements and the relation between field independent cognitive style and academic achievement) and suggest that field independents have a greater extent and richness of their cognitive structure outcomes. To be more clear, field independent participants tended to have a greater extent and richness in their cognitive structure, which probably leads them to show a higher academic achievement.

According to the findings of this study, there was no correlation between the convergence/divergence dimension of cognitive style and cognitive structure outcomes of the participants. In the design of this study, participants were asked to write a meaningful text covering all the given keywords. The type of assessments has an effect on the relationship between cognitive styles and performances of participants. One specific type of assessment may favor a cognitive style. According to Danili and Reid (2006), the convergent/divergent characteristics correlated with students’ performance in assessment where language was an important factor, but not in algorithmic types of questions or in questions where there is a greater use of symbols and less use of words. This may explain why there was no correlation between the convergent/divergent cognitive style of participants and their cognitive structure outcomes. Another type of assessment, rather than writing a text, may lead to different results.

There was no correlation between cognitive styles of participants and their cognitive structure outcomes when examined from the information processing point of view. Again, the type of assessment has an effect on cognitive structure outcomes of participants from the information processing modes of view. Some assessments may require recognition and some may require recalling information, therefore participants need to use different types of information processing modes. In a study by Atabek-Yigit (2015), it was concluded that students who mostly describe and/or compare information are higher achievers in multiple-choice tests, while students who infer information get high scores from true–false tests. Tsai (2001) also reported that there was a correlation between students’ achievement and their information processing modes. Therefore the type of assessment, e.g., writing a text here in this study, may lead to this result.

As for the misconceptions of participants about chemical kinetics, the most common misconception category was about the definition of the half-life of a reaction. Many participants claimed, “Half-life of a reaction is half of the time required for all reactants to react”. Cakmakci et al. (2006) examined students’ ideas about the reaction rate in which high school students and undergraduate students were enrolled. They stated that students have an understanding, as the reaction rate is constant during a reaction. That is they have difficulties in providing explanations about “the initial rate”, “the instantaneous rate”’ and “the average rate”. The misconception about the half-life of a reaction found by this study may be caused by another misconception about the reaction rate, i.e., as participants had difficulties in constructing the meaning of the reaction rate, they thought reactions progress linearly from the beginning to the end. Therefore, they may think half-life was the half-time for all reactants to react. Another common misconception category was difficulties in defining and interpreting the reaction rate. Many participants think that they can use stoichiometric coefficients of a reaction when writing the rate of the reaction. This misconception shows that they have difficulties in understanding the reaction mechanism. This finding is similar to the findings of the study by Cakmakci et al. (2006). The relationship between temperature and reaction rate was another common category of misconceptions. Many participants stated that temperature is a factor that causes the reaction rate to increase. When participants’ explanations were examined in more detail, it can be concluded that they have difficulties in understanding activation energy, which is the energy that a reaction needs to have in order to initiate, and temperature as an indication of energy. In studies by Cakmakci (2010), Cakmakci and Aydogdu (2011), and Kurt and Ayas (2012), this misconception was also found. As Bain and Towns (2016) stated that understanding of students’ misconceptions and application of effective teaching methods for chemical kinetics is crucial. Because it is an important, complex, and prominent unit in chemistry and it brings together an observable phenomenon with theoretical aspects of chemistry (Cakmakci et al., 2006) and is related to many other units like chemical equilibrium and chemical thermodynamics (Bain and Towns, 2016).

Overall, this study examined the relation between cognitive styles and cognitive structure outcomes, and it can be concluded that there was a relationship between them. Cognitive structure outcomes of individuals may reveal their cognitive styles. By identifying cognitive styles of students, educators and instructors can better organize instructional materials (Hrin et al., 2018) and learning techniques, learning environment, and assessment materials. Moreover, a more detailed explanation could be given on cognitive styles of individuals as they are related to cognitive structure outcomes, and the cognitive structure outcomes show how individuals relate the concepts. However, some data in this study were collected through an open-ended questionnaire and participants needed to write a meaningful text with the given concepts. This is one of the limitations of this study. That is if another test, i.e. a multiple-choice test or, a matching test, etc., is applied to the participants, different results, i.e. no relation or a greater relation, could be obtained since the type of test has an effect on cognitive structure outcomes. Also, this study was limited to the chemical kinetics unit, which requires students understand complex mathematical expressions and graphs. In this study, field dependent/independent and convergent/divergent type of cognitive styles and extent, richness, integratedness, and misconceptions for cognitive structure outcomes were taken into consideration. Although they are the most common styles and parameters used in the literature, this is still another limitation of this study, that is different types of cognitive styles and different parameters for cognitive structure outcomes would lead to different results. Therefore, future work can be accomplished by using different types of data collection tools and another unit of study.

Conflicts of interest

There are no conflicts to declare.

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