Blog 4: Final Blog Post Report

As both a peer mentor and a researcher, I was able to fully immerse myself and learn from both the experience I had directly with the student-athletes and what we found from the data gathered. Before I began this journey, I was completely unaware of all the work that needed to be invested in the research process. I believe that this research experience has allowed for me to grow as a well-rounded Dyson scholar. I am a Global Studies major with a concentration in Political Science and I have never had to conduct investigative research that spanned over two years during my studies here at Pace University. The project was also somewhat relatable to what I was able to do in my everyday life. In that, I was taking the practices I used as a mentor and I was able to evaluate them from a research based standpoint. I think it made me more passionate about the work I was doing with the students I was meeting with on a weekly basis. 

From the research aspect, I have a newfound respect for the process of collecting data and processing it. I was fortunate enough to have enough data that spanned over two different years making our conclusions more reliable and giving us the ability to compare the two semesters side by side. I think that that process allowed for us to be more aware of the flaws we might have had in data collection. The discrepancies in the data gave us more of an understanding of why it might have come out this way. Two years gave us a better understanding of why there might be flaws in the data because we were pulling information from a larger group of students rather than simply one group of first-year students. This also gave us more data to analyze which was more time consuming. 

Overall, we found a significant difference between those who attended a majority of the sessions versus those who did not. Thus, we can conclude that academic peer mentoring had an affect on the outcome of the mentee’s GPAs. For nearly all the areas that we have conclusive data for currently, we have found that mentee’s participating in the program did have certainly positive experiences. Further review on behalf of the mentors participating in the program needs to take place. We wanted to do focus groups on students who had already participated in the program during the Spring 2020 semester, but with the current impending pandemic, we were unable to finalize it. 

Being able to collect data from two separate years was very beneficial for us and our analysis since we could compare the two years. We had five main questions that we asked the mentees and we kept referencing back to their progess made throughout the semester with regards to their planner. The five questions were: how many textbooks did the student have? how much time did the student average on social media per day? how many final exams do they have? Do they or do they not get nervous prior to exams? Did you start studying for finals?   Based on the answers to these questions and keeping a planner we were able to analyze their answers with respect to their GPAs; it is important to note that the conclusions made from the planner data is still pending and hopefull will be finished by research day. The research findings reflect student-athletes that were simultaneously receiving mentoring with regards to their academics. Since the mentors were typically able to meet with their mentees once a week, the mentors were able to ask them one question each time they met with the students.  The questions the mentors asked were relevant to topics that might help encourage them to prepare ahead of time academically with respect to the timeline of their classes. Therefore, the following five questions were asked consecutively. 

The first question we analyzed was: how many textbooks did the student have? We found that in 2018, the more textbooks the students had, the higher the GPA of the student. On average, those who had 2.5 or more textbooks, had higher GPAs than those who did not. In 2019, students with the highest percentage of textbooks, or those who had every book per class, show a GPA higher than those who didn’t get a majority of the books. Another way we chose to approach the data was to look at the number of textbooks per GPA. The top 50% of students by GPA have an average use of 3 books while the lowest 25% had less than 2 textbooks in total.

The next question we asked the students was how much time they averaged on social media per day. The activity that the mentors did with the mentees showed not only how much time they spend on social media networks per day, but also how that number amounts to the amount of time they spend a year. The exercise was conducted by the mentors helping the students add together the number of hours spent on social media networks such as Instagram, Facebook, iMessage, Twitter, Youtube, TikTok, and Snapchat in the last 7 days. The total number of hours was added up and divided by 7, so the student could see the number spent per day on these social medias. Some of the strongest data came from this area; as students spending more time on social media their average GPA goes down for both years. In 2018, 25 students who were ranked with the highest GPA have the lowest time spent on social media per day of 1h 58min. The time spent by the top 25 students versus the bottom 25 students differs by nearly an entire hour. What we found to be particularly interesting was the difference in time between the fall of 2018 and of 2019. The highest GPAs in 2019 spent an average of 3h and 48 min and those with the lowest GPA spent an average of 4h and 29. The highest GPAs in 2018 only spent 1h 58 min and the lowest GPAs spent 2h 57min. If we were to compare the highest GPAs in 2018 versus the highest GPAs in 2019, we would see a difference of about an hour more spent on social media. This trend can be attributed possibly to the evolving culture of technology in society. We have found that between the years of 2018 and 2019, the use of the social media platform TikTok has spiked tremendously with the first-year students of 2019, whereas in 2018 we had hardly any reports of usage on that application. We can also note that social media is a fairly relevant platform for politicians, such as our own POTUS to use to reach a large number of his followers or supporters. 

During the eighth week of the semester, the students were asked: how many final exams do you have? The question forced the students to begin to think about the fact that final exams were less than a month away. The highest GPAs came from students who had either 1 or 5-6 exams in 2018. We can attribute this data to the fact that those with only one exam only had to focus on that single exam. During 2019, we found that the complete opposite happened to these students. Those who fell in the range of 2-4 had the highest GPAs whereas those who fell on the outer margins, only having 1 or 5-6 exams, had the lowest recorded GPAs.Those who knew the number of exams they had should be more prepared than those who didn’t. Overall, it was indicated that the number of exams is not permitted to accurately determine how well a student does due to the change of assessment over the years. 

For the following week, the students were asked whether or not they get nervous prior to exams. The idea was that this question might further the students’ process of studying ahead of time in regards to their final exams. The 2018 data was collected a bit differently than that of 2019; 2018 was measured on a scale of 1-4 (1 being never and 4 being always) whereas 2019 was measured by yes or no question. This specific section shows how the GPA for those claiming they are nervous (3-4) is higher by 0.071565934 than those who claim to not get as nervous. This may be because those students who get nervous are those who actually care about the outcome of their exams. Those who never got nervous prior to exams were the next highest because they had the confidence to succeed. Using yes/no answers, we found in 2019  those who did get nervous achieved 0.364230769 higher of a GPA than those who were not. 

In week 10 of the semester, or 2 ½  to 3 weeks from their fms, the students were asked if they had started studying for their finals. This data shows how the average GPA of students who claim to have started studying prior to their finals show a slight improvement on GPA than those who responded that they didn’t study ahead for both the years of 2018 and 2019. However, the difference in GPA between yes and no in 2019 is greater than that of 2018 with the amount of 0.384503676.

Lastly, we were able to analyze the attendance of students in relation to their GPAs. We can conclude that the attendance shows fairly consistent data. As soon as we reach below 69% of attendance we see a decrease with the average GPA of 0.31633 in 2018. This means that students who stop attending the sessions either care less about their grades and/or struggle more. Those students with more than 89% attendance clearly show the best GPA. Here is where the data between 2019 and 2018 are nearly exactly the same. 2019 demonstrated that those who attended less than 69% of sessions had the lowest GPA of those attending mentoring while those attending more than 89% were amongst the highest GPAs in the study. Here we saw that attendance showed value to the overall mentoring program; those who showed up were responsible and actively took steps to improve their academic standing. Those that were committed to the program saw academic growth according to their GPA. 

As for our preliminary results, we have found that there were 3 categories in their planner involvement that are highly active in student success. In keeping a planner, the mentees were able to see active engagement with their academics. In 2018, we got 65 out of 98 mentees to adopt using a planner for 4 or more detailed items weekly by the end of the semester. All the items Students were more successful when they added grades, objectives, and achievements to their planners. Those who added their grades averaged a 3.34 GPA, objectives were a 3.5 GPA, and achievements were 3.59 GPA. This data is based on 113 students who participated in mentoring in the fall of 2019 . We are still in the process of analyzing the data over the coming weeks, thus this is the rough outcome of it. The purpose of mentoring was to get a student to actively take part in where they were with their academics each week. 

Due to the pandemic that impeded our plans for further research, we did not get to finalize the remainder of the topics covered during our mentoring sessions. One of the most important being how students utilized their planner. Hopefully, we will be able to have preliminary data by research day. We have seen with the data presented that there are some effects seen by peer mentoring on first-year students. 

The biggest issue I faced during the first semester of the research was ensuring that all the mentors were inputting data uniformly; initially, some were using notations different from the structured format. I chose to speak with the mentors individually since sometimes the message could get lost in translation through email. 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  they were then able to gather data the following week or during a make-up session within that same week.

Besides the occasional procrastination on my part, the second semester seemed to flow a bit easier without any issues prior to the pandemic. Before the University closed, Professor Buffone and I were able to meet at least once a week, if not more, to conduct research and dissect it. Since I have moved back to Hawaii during this trying time and Professor Buffone remained in New York, we have been able to make zoom calls but the time difference has been difficult to plan around. I have been fortunate enough to be able to work around his schedule and vice versa. 

I got past the procrastination with the help of my faculty mentor and the promise of finding new information that might help future incoming first-year student athletes. I found that if we were able to find valuable information that might help that next class, maybe we could help everyone’s GPAs to rise in the coming years. I also found that I had initiative to help my fellow mentors because I want them to be the most prepared as they can. 

The relationship I had with my faculty mentor was crucial in the success of this project. Being able to meet weekly was beneficial to myself as the researcher and for the research itself. If I was lost one week on what point I should have been at, Professor Buffone was able to correct me when I was wrong or challenge me to further analyze the data after it had been processed. Since I have worked with my faculty mentor on other work before, it was really easy to contact him and he would respond rapidly. Without Professor Buffone, I would have most likely not been so punctual with my work. He expected me to have these assignments and data collected ahead of time, so I was really focused on not letting him down. Professor Buffone made sure that I was pushing myself at every step of this journey. Besides his rapid responses via email, his knowledge on the collection and analysis of the data was unparalleled. Teamwork with my faculty member was definitely needed since my strong suit is mostly the assembly of words rather than comprehensive analysis of numbers and data.

 

Blog Post 3:The Effects of Peer Mentoring on First Year Student Athletes

As I described in my previous blog post, the research up until this point has not only demanded the help of my faculty mentor but also some of my peers in the Learning Assistance Center. I needed assistance aggregating the data we have collected from the previous semester and from the Fall of 2018. At one point, the data was lost in the Google Drive so it took us a little while to retrieve it.  We had no idea where or how it got misplaced and as can be imagined we experienced no small amount of stress. Once we were able to retrieve the data sheets, however, we transferred the information to a Microsoft Excel worksheet. Now that we have aggregated the data from both fall semesters, we are in the process of determining what the information tells us about the students and the effects peer mentoring might have had on them. 

Since the last blog post, Professor Buffone and I have been working on analyzing the data and processing it. Finally finding and putting both the data from last semester and the year prior, or 2018 and 2019, we have been able to draw more accurate conclusions from the years combined. Since the different areas we wanted to discuss were objective and hard to quantify, we input the data as number quantities. For instance, for the question “have you been studying for finals?,” it was first collected as either yes or no responses. When we sifted through the data again, we transformed a yes or no response to a numbered, 1 and 0. We did the same thing with the question, “do you like studying in a group?” and “do you get nervous prior to exams?” The  latter question offered the students a choice of 1-4, 1 being the least nervous and 4 being the most nervous,. In spite of the discrepancy with the other numbered questions, we were still able to extract information from a fairly impartial question. 

To date, we have been able to take the average, variance, and standard deviation of the responses to each question asked for the following questions for both semesters: “ how many textbooks do they have compared to how many should they have?,how many finals do they have?, and, how many sessions did they attend?”  Most of the data that we have sifted through up until this point has been the data from Fall 2019. When my faculty  

Throughout the semester the mentors prompted the first-year students with an exercise of computing how much time they spent on social media. Doing the exercise once towards the beginning of the semester and once again towards the end, the goal of the exercise was to make the students more aware of the time they might waste on social media websites throughout the week when they could be spending it more productively working on their academics. As an example of the data we collected and looked at, in  the fall of 2018, the average GPA of the first year student athletes was a 3.24 respectively while the average time on social media was 4 hours and 2 minutes. 

After placing all the data for the attendance of Fall 2019 into a scatterplot where we could see the affect GPA had on overall attendance, we can confidently conclude that attending more sessions positively affected overall GPA. Students who attended 8 or more sessions, had an average GPA of 3.31. Though we are still working on the social media data and a majority of the other topics discussed throughout the semester, we have been able to run regressions on the data and are currently in the process of analyzing the data from both years.

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.

Blog 1: Effects of Peer Mentoring On First-Year Student Athletes

The working title for my research project is “The Effects of Academic Peer Mentoring on First-Year Student Athletes.” The goal of my research project being conducted with Professor Buffone is to assess and gather two year’s worth of data as a means to determine the effectiveness of small group mentoring. My role as a student researcher will be divided into two parts: peer mentor and data analyst. I will be gathering data from the several groups of first-year athletes with whom I work. In addition, with the help of other peer mentors in the Learning Assistance Center, I will be collecting the data they gather from their first-year student athlete groups. Once the data is collected, I will be able to examine it fully. 

The research project is expected to produce data on how these small group peer mentoring meetings correlate with student success.We are using the GPA as an indicator of academic success. From this project, I expect to learn more about how individual topics introduced during mentoring sessions, for example using planners for time management, communicating with professors, etc., assist first year students. In particular, I expect this research to help the peer mentors that might come after me. I hope that this research can be used at the Learning Assistance Center here to assist future first year student athletes and to also possibly used as reference for tutoring centers at other universities. 

To answer my research questions, I will generate data from the small mentoring groups like recording how much time the students spend on social media, whether or not they enjoy studying in groups, information about what they put in their planners, etc. Data has been collected on Google Sheets for the past three years, and each year a new file has been made and edited. Based on this quantitative data, I will also be generating a descriptive design where hybrid qualitative and quantitative information will be analyzed together. I will be working with a majority of the students receiving the peer mentoring services from this year and have worked with a large number of students from previous years, I will be able to examine and draw conclusions from the data.