Lesson observations

Three of the five teachers were observed teaching the same practical work content areas whilst two were observed teaching different practical work content areas. Before each practical investigation-related session observation, the teacher in question informed the researcher (almost a week in advance) and gave the researcher a copy of the planned work that was to be done. The teacher also briefed the researcher on the approach he/she was going to use. The researcher sat at the back of the class taking field notes for all pre- laboratory and post-laboratory sessions while a hired assistant (a colleague in the Mathematics division) would do the video- recording. For the practical investigation lesson, the teacher in question informed the researcher (about two days in advance) and gave a copy of the practical work. In all five teachers’ classes, the practical investigation immediately followed the pre-laboratory session. Post-laboratory sessions were conducted after the teachers had finished marking learners’ laboratory reports. All five teachers also provided the marking scheme by which they were going to assess the learners’ laboratory reports. After each lesson observation, the researcher (on the same day whilst things were still fresh in mind), read through and summarized the laboratory session observation notes. Table 3.3 gives a summary of the practical work content areas that were covered by each of the five teachers for those laboratory-related sessions that were observed. The observation was done consecutively, that is, one lesson after the other except for the post-laboratory sessions which were conducted after the teachers had completed marking learners’ reports and the period varied from teacher to teacher.

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Table 3.3: Observed practical investigation related-sessions shown by title of content

Teacher Code School Pre-Lab. / Lab. / Post-Lab.

Practical investigation-related content areas

T1 T2 T3 T4 A B C D Pre-Lab. ______ Lab. ______ Post-Lab Pre-lab. _______ Lab. _______ Post-Lab. Pre-Lab. ______ Lab. ______ Post-Lab Pre-Lab _______ Lab. _______ Post-Lab.

Real and ideal gases, Kinetic molecular theory

______________________________________________ Practical investigation based on Boyle’s Law

_____________________________________________

Revision of practical on Boyle’s Law and calculations based on Boyle’s Law.

1.Solving Oxidation and Reduction practice problems _____________________________________________ 1.The Reduction of Copper Oxide with Hydrogen gas 2.Interpreting results of an investigation of the displacement reactions of the halogens

_____________________________________________ Revision of practical on reduction of Copper Oxide by Hydrogen gas

Introduction to the practical (Charles’ Law)

____________________________________________ Practical based on Charles’ Law

______________________________________________ Revision of practical on Charles’ Law

Real and ideal gases, Kinetic molecular theory

______________________________________________ Practical investigation based on Boyle’s Law

_____________________________________________

Revision of practical on Boyle’s Law and calculations based on Boyle’s Law. T5 E Pre-lab. _______ Lab. _______ Post-lab

Kinetic molecular theory (re-visited), real and ideal gases ______________________________________________ Practical investigation based on Boyle’s Law

______________________________________________ Revision of practical on Boyle’s Law and calculations based on Boyle’s Law.

139 3.8 Data Analysis and Presentation

Both quantitative and qualitative approaches were used to analyze the data. This was done to generate the most rigorous description of the participant’s conceptions of the nature of scientific inquiry and learners’ perceptions of the extent to which they were engaged in scientific inquiry. Quantitative data analysis was done using both descriptive and inferential statistics. For qualitative data, inductive analysis was utilized (Hatch, 2002; McMillan & Schumacher, 2006; Miles & Huberman, 1994). Through this process, particular pieces of evidence lead to meaningful wholes. Inductive analysis involved coding and categorization of data, observing patterns and themes, and making reasoned conclusions. Coding began with reading the data in each data set and identification of frames of analysis (Hatch, 2002). Frames of analysis are levels of specificity within which the data was examined.

Qualitative data was analysed using Atlas.ti version 6.2, a tool for indexing data. The software aided analysis of learner and teacher interviews, LUSSI and Probes’ open-ended responses. 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.

Quantitative data analysis was performed with the Statistics Package for the Social Sciences (SPSS) software version 16 for Windows. Descriptive statistics such as frequencies, means, and standard deviations were computed to summarize the participant’s responses to questionnaire assessments. The same software was utilized for the drawing of graphs for data presentation. All statistical correlation tests are two-tailed and alpha was set at .05. However, some Regression analysis tests are one-tailed and alpha was set at either .05 and/or .10. The Independent samples t-test was employed to determine whether there was a difference between being male or female and scores obtained on the LUSSI and LPCI. Tests for correlations between learners’ scores on the LUSSI and scores on the LPCI were done using the Pearson correlation coefficient. The Multivariate Inferential Statistical

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Test, Multivariate analysis of variance (MANOVA) was used to test the significance of group differences for open-ended LUSSI responses and LPCI responses and alpha was set at .05. Cronbach’s alpha was used to determine the reliability or internal consistency of the LPCI instrument. The Pearson correlation coefficient was used to perform Crude item analysis.