Teacher and Learner Interviews

Analysis for both learner and teacher interviews was done using a more or less rough combination or “hybridization” of the processes of analytic induction (Murcia & Schibecci, 1999), sequential analysis (Harwell, 2000), and interpretational analysis (Gall, et al., 1996) as done by Vhurumuku, Holtman, Mikalsen and Kolsto (2006). However, sequential analysis was used differently in this study because a qualitative analysis software, Atlas.ti version 6.2 was employed. Analytic induction involved continued reading of learner and teacher responses to unveil common patterns. Using Atlas.ti, version 6.2, learners’ responses were treated as a family and teachers’ responses were treated as another family. Clusters of common responses were placed into similar categories. The emerging patterns were then used to develop categories. Responses were then classified on the basis of the formed categories. Frequency counts were made for each category (see, Appendix H). According to Harwell (2000), sequential analysis is a slight variation to analytic induction and involves the procedure of reading through the responses from all participants for each question. The responses are re-read and remarks and interpretations written in the margins for each response to a question. However, in this study, interpretations for each response to a question were written as memos and comments (if the researcher is to use Atlas.ti

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language). Memos and comments are methods used to record one’s ideas and observations about codes, quotations, and the Hermeneutic Unit (HU).

Formed comments and memos were reduced to clusters based on the responses. Cluster phrases emerged from the responses. The reading and re-reading was continued and clustering and sub-categories formed and this was enabled by merging of codes and quotations where necessary. Responses were quantified using frequency counts in primary document tables (see, Appendix H). Since Atlas.ti version 6.2 indexes all quotations under a specific code, cluster or category, statements exemplifying clusters or categories were then selected or picked from a list. In sequential analysis after clustering and categorization another person looked at the data relating to each possible cluster. From the discussion concerning the evidence of the existence of a cluster, adjustments to the categorization was made and final categorization done on the basis of consensus. Interpretational analysis was about getting meaning out of the data. The researcher asked the question: What does this mean? Meaning was found by going beyond the face value of words or phrases. Insight was required.

In analyzing learners’ responses to open-ended and interview questions the following sequence6 was followed.

1. All responses to each open-ended or interview question were continuously read through and phrases and sentences making reference to the nature of scientific inquiry and teacher practices of scientific inquiry underlined.

2. Each response to each protocol was read through and the sub-category in which the learner’s was expressing the identified NOSI aspect (phrase or sentence) classified as

6_{The data analysis sequence followed in the current study was largely based on the analysis done by} Vhurumuku, Holtman, Mikalsen and Kolsto (2006) in their analysis of Zimbabwean High School Chemistry students’ responses to interview questions. They were studying laboratory work-based Images of the Nature of Science. Details on the ‘hybridisation’ analysis can be found in Vhurumuku, Holtman, Mikalsen and Kolsto (2006). An Investigation of Zimbabwe High School Chemistry students’ Laboratory Work-Based Images of the Nature of Science. Journal of Research in Science Teaching, 43 (2) 127-149.

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based on the learner’s Grade 11 Chemistry practical investigation laboratory experiences or non-laboratory experiences.

3. Responses to each protocol were continuously read through again and from the common patterns that emerged clusters of common ideas were used to form categories. Each formed category was given a code and this was done in Atlas.ti version 6.2 to capture the main ideas expressed by the learners. For example the code ‘teacher frames research question’ was assigned to represent the idea the teacher framed research questions which learners would attempt to answer during practical investigation.

4. Atlas.ti version 6.2 then quantified responses as frequency counts. Each NOSI issue raised by the learner was counted as a frequency. Using Atlas.ti version 6.2, learners’ probe responses, LUSSI open-ended responses and interview responses were loaded as different primary documents and were further grouped as different families and this enabled the response to be counted only once in each family for that learner even if the learner raised it several times e.g. in probes and LUSSI open-ended and interview responses.

5. For each of the formed categories a comment was written to capture the general ideas expressed by the learners for that category. For example the category ‘imagination and creativity in scientific investigations’ consisted of learners’ views, which generally ranged from naive to informed views. The naive view, for example, said “Imagination and creativity are not used during investigations because they are in conflict with objectivity”.

6. The categorized responses were sorted out according to the broad NOSI aspects explored by the protocol items namely: roles of laws and theories in science, the nature of scientific observations in science, methods used to conduct investigations in science, ways of validating new knowledge in science, the creation of scientific knowledge, the purpose of practical investigations in science.

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7. Atlas.ti version 6.2 grouped teacher and learner quotations under each formed category hence the required quotations (statements), which could be used to illustrate and exemplify the formed categories and the interactions between learners’ NOSI views and participation in instructional practices, could be selected from a group.

As a tool for indexing data, use of Atlas.ti version 6.2 aided analysis of learner and teacher interviews. The software was used to uncover and systematically analyze complex phenomena hidden in text. The program provided tools that enabled the researcher to locate, code, and annotate findings in primary data material as well as to weigh and evaluate their importance and as such to visualize complex relations between them.