Blog Post 2: The Effects of Peer Mentoring on First-Year Student Athletes

For the project, I have needed help both from the faculty member and others working in the tutoring center with me. The biggest issue I faced was ensuring that all the Peer Mentors were inputting data uniformly. Initially, some were using notations different from the structured format. Another less consequential issue was collecting data from students who had missed their mentoring session.  Mentoring sessions were conducted weekly, hence collecting missing data did not require finding or searching for the participants. Fortunately, the mentors were able to meet with the students weekly so if they missed one week then they were able to gather data the following week. The data validation was based on errors found; there is a structured format for the data sheet where we collect the data, thus there is a finite amount of answers that can be given for each of the questions posed. Other than this, the collection of data has been done rather smoothly. During each session the mentors conduct with the mentees, they incorporate asking the quantitative question into the lesson. Early on, we discovered the most challenging aspect was determining the overall effectiveness of the mentoring program.  We relied on the self-reporting of the students who participated in the program. As with all self-reporting, the results were subjective and hardly quantifiable. We sought another method to see if there were any way to quantify how the strategies we were sharing had an impact on the academic success of the participants. We determined eight areas where we could use objective data to see how much time students used to employ the strategies for academic success.

Every Wednesday I meet with Professor Buffone and we discuss how the research is being collected, if there are any discrepancies in the data, what kind of data we should be taking moving forward, and also what I have been noticing when observing the data. Throughout the week, he checks in with me if he finds an issue with the collection of data or if he thinks I should be working on certain parts of the research more than others. Professor Buffone keeps me motivated to stay on track and timely with the work needing to be done because he is the one who sets the deadlines and holds me accountable for the work I do and for meeting benchmarks. 

We have found that there might be a correlation between 100% attendance and 0% attendance; those with either of these traits might have higher GPAs, but we have to wait until after the semester is over to test this theory. According to this theory, some students might have come in with very competent skills, thus they are independent learners without the need for assistance from any outside parties. We have discussed what can affect GPA and when we come back from winter break, we will be able to draw conclusions from the data.  We have already discussed various methods of analyzing the data that can be used to extract conclusions. 

We have asked how the data collected affects the GPA of the student-athletes. Since we have conducted a preliminary review of last fall’s data, we have been able to preview what types of outcomes we expect to find after this semester. Some speculation on an outcome has been produced from the preview. Unfortunately, we do not have both semesters to base our outcomes on yet, so we are unable of what type of conclusion we are expected to find.

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