The image to the left is a graphic representation of my data analysis plan for the pre-action and post-action data collection. The observation and interview data will be analyzed via qualitative data methods. Using the inductive method, I will code students responses and my informal notes to find themes or patterns (Phillips, 2010). Through this data analysis process, I will be able to see if any thoughts or experiences were common for my students. For example, if a high volume of students said that they did not understand how certain tools worked, I may report that a common theme was, "Inexperience/Difficulty with Tech Tools." I could compare the themes generated before and after the intervention to see if/how they changed. Similarly, I would collect and score students' rubrics both before and after the intervention was implemented. Because the rubric converts students achievement into a numerical score, this data will be completing using quantitative methods. By finding a class average before and after the implementation of the intervention, I will be able to see any change that the intervention caused. I will also look specifically at focus students' qualitative and quantitative measures to see if these specific students were impacted by the intervention in a positive or negative way. Noting change through multiple, distinct, and triangulated methods will help me to see if my teacher-focused reflective intervention was successful in improving students' experiences with technology.