Prior to the start of data analysis, I bracketed about my own experiences, ideas, and biases related to A&P courses, the participants and instructors, and the bounds of the study. The full list of prompts are presented in Appendix I. Between January 21 and February 2, 2019, I responded to no more than two prompts per day. In May 2019, I returned to my reflections and analyzed my responses with the research question of What are the assumptions and biases of the researcher? The first round of coding consisted of five open coding passes, with the first four passes focusing on a different area of the 3P Model and the fifth open coding pass looking for important themes that had not been coded in a previous pass. These open codes were then grouped into six overall themes listed below. The original bracketing responses were recoded using axial codes and a summary of each group is presented below. All codes and their definitions and bounds are also presented in Appendix I.
3.6.1
Researcher Assumptions and Biases
The main themes that emerged from analysis of the bracketing responses were student actions, meta-actions, and researcher awareness. In addition, the themes of outcomes, student traits, and R-SPQ-2F were also present. Analysis of these brack- eting responses did not show bias or assumptions about the 3P Model specifically. However, each of the themes identified, with the exception of researcher awareness, have some connection to the 3P Model, and the following section describes areas of which I must be aware to ensure quality analysis.
Throughout my responses, the specific actions and perceived attitudes or ap- proaches of the participants in the study or my own students were noted. For example:
I am seeing students discuss how they work on the class and a mixture of good and bad shows up. So far, I have evidence of a lot of “good” actions. Most participants report going to class, re-watching lectures, taking notes in some form.
Most quotes relating to student actions are clear and unambiguous. However, quotes relating to meta-actions are more dependent on the researcher’s perception. For example:
I think overall, I am looking for a willingness to work and an interest in the topic as “good.”
In both of these groups, I mention or make interpretations about student processes in learning. Many excerpts that were coded to meta-Actions were followed by or joined to excerpts related to researcher awareness. For example:
I have hoped students would use class and lab time as an opportunity for learning. Perhaps I misinterpret a lot of what I see... My perceptions were greatly challenged by the info in the pilot study. My main impression of students has been of people who don’t wish to work or put forth effort and then frustrate me a lot.
Based on these findings, I will need to make sure my interpretations of student meta- actions, those areas that relate to attitudes and feelings, must be grounded in the responses provided by the participant, rather than my own assumptions about the meaning of those attitudes or feelings.
A less common but related theme is my emerging assumptions about the cat- egories of deep, surface, and achieving learners and the ways that students or my participants my be categorized, which specifically connects to the 3P Model traits of approach to learning throughout the course. For example:
I feel like surface and deep groups are tricky and maybe not helpful. The SPQ has issues itself in trying to group students.
Again, this highlights my own assumptions about data that had not been fully ana- lyzed at the time of this reflection. My conclusions about both student feelings and attitudes, as well as established instruments and theories, must be based in data and my processes of analysis should be careful about assigning my own meaning rather than that of the participants.
The theme of outcome, which is a specific area of the 3P Model in Prod- uct, centered on individual or group responses to the class structure and content. The following quote gives an example of a quote describing individual outcomes or responses.
It seems [they] (Caitlyn) has done well in the class with less effort than some participants.
However, more quotes were attributed to group outcomes or responses. For example: I would say they (the instructors) have designed their courses in ways that are encouraging the students both through grades/ assessment and also content delivery methods (showing interest and enthusiasm) to increase/ keep a high level of motivation and engagement.
Based on these findings, I need to listen carefully to the voices of individual par- ticipants rather than be influenced by my own “instructor” role and mindset when viewing the outcomes described by the participants.
The recognition of student traits highlighted several superficial characteristics that are, in most cases, outside of the bounds of this study, but are also connected to the student characteristics in the Presage factors. For example:
She has a small- almost lisp when [they] speaks that is really pronounced on the audio files.
Overall, this code captures impressions based on visual observations during the inter- views or audio observations while verifying the transcripts of each interview. Almost all of these characteristics are not evident or present in the transcripts. However, this is important to note since I need to be aware of these items possibly impacting my analysis choices. The practice of removing real names and masking all pseudonyms from the transcripts prior to analysis should help to minimize biases or assumptions that arise because of positive or negative student traits that were observed.