When originally formulating the thesis for this research, we did a qualitative analysis of research articles and published works associated with social-emotional learning. From this preliminary work, we utilized deduction to determine the gap of social-emotional learning of middle school students. This gap allowed our primary investigator to formulate our research question. As we continued work within the lab, we ran into challenges, especially within recruitment. It was difficult to get responses from principals and businesses to advertise our research; however, we persevered. Our primary investigator got permission to shift to online recruiting through Facebook. On this platform, we ran into many issues with Facebook groups allowing us to post. To combat this, we decided to start locally, in Butler County, and built repertoire so that Facebook would not shut down our Facebook page. After building repertoire, we discovered a system for targeting our recruitment to be more efficient. Originally, we would target county groups that often got numerous posts and had many members. However, it did not seem as though this method was working very well, so we shifted to targeting groups associated with cities in our target counties. As we move on with the research, we will each work on coding for formulating the social-emotional measure. We will each use our own experiences and perspectives to code to work on eliminating bias as well as provide as much accuracy in the final measure as possible.
The center of our research involves filling a gap of understanding social-emotional learning amongst middle school age kids. When deciding how we would collect our data from participants, we took into many factors, such as average incomes in the school districts, racial groups, and poverty level. From these demographics, our primary investigator narrowed gave us districts to target for recruitment in order to get the most representative data possible. Also, to build a social-emotional learning measure, we hope to incorporate the perspectives amongst teachers, parents, as well as the students themselves. From the annotations of the unique experiences of these individuals, we hope to overall combat social-emotional issues, such as bullying.
Through our involvement in the MUSCRAT Lab, we have learned about programs such as Zotero, OtterPilot, and additional softwares. Zotero is a software that automatically cites articles and websites. OtterPilot is a transcription software that utilizes artificial intelligence in order to transcribe recordings from the research interviews conducted by our primary investigator. Even though this transcription software is very helpful, we have learned the importance of checking this technology as it is not completely accurate. Another aspect of our research is recruiting participants. This has included the use of Facebook as a recruitment platform. We have found barriers of groups not allowing us to join or deleting our posts. It is also difficult to find relevant Facebook groups due to the specificity of our research interests. Despite these challenges, we have learned and grown to constantly reevaluate our abilities within technology to expand our skillset as well as effectively execute our work. Our next steps will include the use of a coding software as a continuation of our skills and experimentation with technology.