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Asia-Pacific Forum on Science Learning and Teaching, Volume 17, Issue 2, Article 7 (Dec., 2016) |
We attempted to identify university students’ cognitive structures about environmental education, climate change and consumption concepts. We utilized qualitative data collection of Environmental Education (EE) majors, and of students not in the EE major. Qualitative data deals with meanings, whereas quantitative data deals with numbers. The findings from qualitative studies can provide a rich quality of results that quantitative data cannot (Miles & Huberman, 1994).
The sample size consisted of 52 (28 females and 24 males) and was comprised of two groups of students who were attending two different sections of the same course about a general introduction to outdoor education. Both groups were in their first or second year of study. The students were attending the University of Minnesota Duluth, U.S.A. during the fall term 2013. Out of the participants, 24 (12 females and 12 males) were outdoor & environmental education majors and 28 students (16 females, 12 males) were from various majors such as finance, criminology, political sciences, environment & sustainability, statistics, math, communication, and biology. Since this was a general introductory course, students from different majors were mixed within both sections. The study groups were a convenience sample and were chosen because of the course topic and were assumed to be similar in their knowledge about the environment.
The word association test (the organization and relationships between the concepts in the mind) was used to determine what concepts the students associated with the key concepts they were provided about environmental education, climate change and consumer consumption to disclose the their personal concept map which indicated their current understanding of the primary environmental concept being provided. The word association test is a data collection technique that is used to analyze the conceptual structure of an individual or a group of people about a certain subject. The technique is based on the assumption that giving a stimulus word and asking the respondents to freely associate what ideas come to their mind gives relatively unrestricted access to mental representations of the stimulus term (Bahar, Johnstone & Sutcliffe, 1999; Hovardas & Korfiatis, 2006; Sato & James, 1999). In the simplest form of this technique, a word or a series of words are presented to participants either in written form or orally. The participants were asked to respond by providing whatever came to their minds as response words in association with the stimulus word. After the content was analyzed and the frequency of response words were calculated, it was possible to make conclusions on the associative meanings of the stimulus word and so to define the conceptual structures of the participants. This kind of application of the free word association task that reveals the associative meanings of various concepts has been used in numerous studies (Sato & James, 1999; Torkar & Bajd, 2006; Dikmenli, 2010). This methodis a reliable technique used as a procedure for measuring the number, direction and strength of connections (Nelson, McEvoy & Schreiber, 1998; Novak & Govin, 1984; Mervis & Rosh, 1981). When scoring, we counted the same associated words between two concepts and then calculated the ratio of the same associated words to the maximum words in the association. Using sentences/phrases to determine the concept relatedness ratios between each pair of concepts validated the associated words. The total concept relatedness ratios of different pairs of concepts became the score for this test.
Data collected from the word association test was analyzed as described above. Response words with the same meanings were categorized together under the heading of the response word with the highest frequency: response words that were used three or less times. Irrelevant words that were not associated with the other words were excluded. The words were categorized using a criterion of semantic relationship. The frequency of the words in each category was calculated. We found that several other studies using this type of data analysis provided reliable results (Sato & James, 1999; Torkar & Bajd, 2006; Dikmenli, 2010; Aydın, 2015).
We used the word association test (WAT) as a data collection instrument. In order to construct the test, three key concepts were selected. A sample WAT page is as follows:
Environmental education………………………..
Environmental education ……………………….
Environmental education ………………………..
Environmental education ………………………..
Environmental education ………………………..
Environmental education ………………………..
Environmental education ………………………..
Environmental education ………………………..
Environmental education ………………………..First, the participants were informed about the word association test. For each key concept in the test, the students were requested to write the concepts that came to mind in the relevant gaps. They were asked to use environmental education, climate change and consumer consumption as key concepts while they were working on the association task. They were allocated 30 seconds for each gap. According to Bahar et al. (1999), 30 seconds is the optimum response time period used in many academic studies. We checked the time constantly and guided the participants accordingly. At the end of each 30-second time period, the participants were asked to proceed to the next key concept. Following the administration of the test, the results were assessed through a cut-off point, a technique used by Bahar et al. (1999). During the assessment process, the first step was to calculate the frequencies of the concepts for each key concept provided by the respondents. Second, a frequency table was prepared. Third, by using the frequency table, a concept map was developed. To draw a concept map, the highest frequency in the table was identified and three to five frequencies below the highest frequency were accepted as the cut-off point. The cut-off points were lowered step by step. The concept maps were formed in accordance with the cut-off points determined through frequency ranges. Thus, a concept map constructed for the first breakpoint set the starting point of the next concept map to be constructed. In other words, concept maps tended to be constructed as attached to each other when there were fewer breakpoints. The next step was to consider the total number of answers provided by the respondents for each key concept (Shavelson, 1974).
