Data Analysis

4.3.1 Quantitative analysis. Each participant was assigned a unique code, which was

used to identify them across the three Phases of the study. All S-STEM pre-post survey

responses were inputted into Microsoft Excel, following the numerical Likert scale responses. As a validity measure, some questions are reversed within the S-STEM survey. The numerical

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values for those items are flipped. That is, 4 is entered as 2, 5 is entered as 1 and vice versa. There was variation in the wording across certain statements to capture participants’ attitude from different angles (Figure 8).

Strongly Disagree Disagree Neither Argee nor Disagree Agree Strongly Agree 4. I am the type of student who does

well in math. 8. I am good at math.

Figure 8. Variation in wording of S-STEM statement for the mathematics construct.

The mean across participants was calculated for each statement within the scale, and then the responses were collapsed to show a mean for each point of data collection to illustrate a single set of means.

4.3.2. Survey scales. The statements analyzed in this thesis to explore the research

questions pertaining to attitude towards and interest in STEM subjects as well as self-efficacy in STEM are listed below.

1. Attitude and interest in STEM subjects

Engineering & Technology

▪ I like to imagine creating new products;

▪ If I learn engineering, then I can improve things that people use every day; ▪ I am curious about how electronics work; and

▪ I would like to use creativity and innovation in my future work.

Mathematics

▪ Math has been my worst subject [reversed]; ▪ I would consider choosing a career that uses math;

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▪ I can handle most subjects well, but I cannot do a good job with math [reversed]; and ▪ I am sure I could do advanced work in math.

Science

▪ In general, I enjoy science; ▪ I believe science is interesting; ▪ I would consider a career in science;

▪ I can handle most subjects well, but I cannot do a good job with science [reversed]; ▪ I am sure I could do advanced work in science;

▪ I am involved in science activities outside of school; and ▪ I would like to be a scientist.

2. Self-efficacy in STEM

Engineering & Technology

▪ I am good at building and fixing things; and,

▪ I believe I can be successful in a career in engineering.

Mathematics

▪ Math is hard for me;

▪ I am the type of student who does well in math; ▪ I can get good grades in math; and,

▪ I am good at math.

Science

▪ I am confident in my ability to do science; ▪ I am confident when I do science;

66 ▪ I know I can do well in science; and, ▪ I am good at science.

21st _{Century Learning Skills}

▪ I am confident I can lead others to accomplish a goal; ▪ I am confidence I can encourage others to do their best; ▪ I am confident I can help my peers; and,

▪ I am confident I can include others’ perspectives when making decisions.

4.3.3 Qualitative analysis. Workshop observations, interviews, and open-ended

questions constitute the qualitative data used in the thesis. The workshop observations underwent an analysis slightly different than the interviews and open-ended questions. However, all

qualitative data was thematically analyzed to account for patterns across different participants during the study.

The workshop observations were inputted directly into Microsoft Word using the observation protocol (Appendix A) by research assistants. The data identifies and reports on recurring patterns and themes while also providing rich and detailed descriptions of the

experiences (Braun & Clarke, 2006). Although the original protocol examined students, teachers and workshop facilitators, the analysis in this thesis focuses on the interactions between students and workshop facilitators. The workshops integrated ELLs with their non-ELL peers, and as a result, the observations show data from mainstream classes. The STOp workshops used a general script and, therefore, followed the same format regardless of changes in the value of facilitator, schools, and phase. Despite the integration, there is significance in concurrently comparing the observations of students to workshop facilitators as they interacted in the workshops as it demonstrates the pedagogies that support learning and those that are disengaging.

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The open-ended questions and interviews also underwent thematic analysis, which began with inputting open-ended questions into an Excel spreadsheet. These items were colour-coded based on recurring themes relative to the research questions. The interview questions were inputted the same way and thematically organized using NVivo Version 11.4.0 – a software program that is "designed to help you organize, analyze, and find insights in unstructured or qualitative data like interviews, open-ended survey responses, articles, social media, and web content" (QSR International, n.d.). NVivo was used to analyze the interviews in terms of word frequencies by generating a word cloud that depicts repetitive words across participants and illustrates those words’ frequencies. Specifically, the word cloud makes the words used most often larger than less common words. Frequency identification offers aid in developing common themes within the case study. In addition, NVivo develops word trees, which considers typical ways in which words were used across participants. These tools provided a context for exploring self-efficacy in both STEM and SLA.

4.3.4 Quantified qualitative data. Both the open-ended questions and interviews were

quantified (Cohen, Manion & Morrison, 2011) through binary coding (0 = theme not present, 1 = theme present, X = student left section blank) to explore thematic frequencies (Figure 9).

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Figure 9. A sample of quantified open-ended questions using Microsoft Excel

The themes generated were highlighted using a mix of pie charts and bar graphs to illustrate thematic change over time in the findings section.

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