Abstract Application of social network analysis to education has revealed how social network positions of K-12 students correlate with their behavior and academic achievements. However, no study has been conducted on how their social network influences their academic progress over time. Here we investigated correlations between high school students’ academic progress over one year and the social environment that surrounds them in their friendship network. We found that students whose friends’ average GPA (Grade Point Average) was greater (or less) than their own had a higher tendency toward increasing (or decreasing) their academic ranking over time, indicating social contagion of academic success taking place in their social network.

Citation: Blansky D, Kavanaugh C, Boothroyd C, Benson B, Gallagher J, Endress J, et al. (2013) Spread of Academic Success in a High School Social Network. PLoS ONE 8(2): e55944. https://doi.org/10.1371/journal.pone.0055944 Editor: Sergio Gómez, Universitat Rovira i Virgili, Spain Received: September 3, 2012; Accepted: January 7, 2013; Published: February 13, 2013 Copyright: © 2013 Blansky et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This material is based upon work supported by the US National Science Foundation under Grant No. 1027752. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Introduction Application of social network analysis [1] to educational systems is a promising yet unexplored research area. A small number of studies investigated how the positions of K-12 students in their social network are correlated with their behavior and academic achievements. Farmer and Rodkin’s work [2] was among the earliest of its kind, in which they studied correlations between social network centralities of elementary school children and their behavioral traits, such as cooperativeness, leadership, popularity, athleticism and aggressiveness. More recently, Bishop [3] found, through statistical analysis of the Add Health data [4], that students in positions with higher centrality had significantly better grades than those with lower centrality. Similar correlations between academic achievements and centrality or popularity in social networks were also found in different data sets [5], [6], though it was also reported that the correlations depended significantly on ethnic and other social contexts [5]. These earlier findings naturally leads one to ask another question that may be more relevant to educators and students themselves: How does a student’s social network environment influence his or her academic progress over time? Unfortunately, none of the studies mentioned above provides a direct clue to this question, because their statistical analyses were all applied to static snapshots of students’ social networks and their grades, without any temporal changes taken into consideration. To gain insight into the aforementioned question, we investigated, for the first time, the correlations between high school students’ academic progress over one year and the social environment that surrounds them in their friendship network. The students’ social network was reconstructed based on the results of an electronic survey asking them about their friendships, while the data about their academic progresses were obtained directly from their school’s official academic records. Our results showed that students whose friends’ average GPA (Grade Point Average) was greater (or less) than their own had a higher tendency toward increasing (or decreasing) their academic ranking over time, indicating social contagion of academic success taking place in their social network.

Methods Ethics Statement The study was approved by the Binghamton University Institutional Review Board with permission from the Maine-Endwell Central School District. All data were obtained with written informed consent reviewed by the Binghamton University Institutional Review Board (IRB). According to the research protocol reviewed and approved by the Binghamton University IRB, the researchers are not allowed to share the data with third parties outside the research team. Contact the corresponding author for more details. We reconstructed a social network of the eleventh grade class at Maine-Endwell High School, Endwell, NY, USA, by developing an online survey and administering it on January 11, 2011. We received 160 responses (response rate: 92%), two of which were incomplete and therefore excluded from the statistical analysis (N = 158). In the survey, the students were given a list of all the other students taking the survey, and asked to decide whether each of them were a best friend, a friend, an acquaintance, someone they did not know, or if they were related. The first three categories were used for social network reconstruction and analysis. Those self-reported friendships were represented as directed links in the reconstructed network. The survey data were supplemented by data from the School’s student record database that contained information about GPA (at two time points: January 2011 and January 2012), attendance, disciplinary action, and gender for each student. All the data were anonymized and stored using randomized ID’s so that no personally identifiable information was kept in our records. The GPA data were used to measure each student’s academic progress over the year. The original GPA distributions were highly skewed negatively, and there was an overall trend of GPAs moving upward over the year. We therefore transformed the raw GPA scores into academic rankings within the class. Rankings were calculated by subtracting the student’s position in the class from the total number of students, so that greater ranking values mean higher academic ranks. We then characterized the social environment surrounding a student by calculating the difference in ranking between each student’s self-reported neighbors’ average GPA and his/her own GPA (x i , where i is the type of the network: acquaintance, friend, or best friend). The student’s academic progress (y) was characterized by the increase or decrease of his/her academic ranking in the period spanning January 2011-January 2012.

Discussion The results presented above have some important implications. Firstly, the positive correlation between the neighbors’ average GPA and the student’s academic progress indicates that social contagion of academic success may be taking place in the students’ social network, similar to those reported on obesity [9], emotions [10] and other cognitive or behavioral traits [11], [12]. While most educators already know from their experience the importance of social environment for a student’s academic success, our study presents the first quantitative supporting evidence for such empirical knowledge, especially from a social network viewpoint. Secondly, the regression analysis revealed that the correlation was most significant at the friendship level (Table 1), while the correlations at acquaintance and best/close friendship levels were not as significant. This may be understood in that students tend to choose their best or close friends primarily using a homophilic mechanism on personalities, interests, tastes, favorites, and so on, so the interaction with them would not cause much changes to the student (and also that acquaintances would not cause much changes either, obviously). This finding, that an intermediate level of friendship has the highest influence on an individual, has an interesting similarity to Granovetter’s well-known ‘weak tie’ observation [13]. Thirdly, our research suggests the possibility of a quick test to predict a student’s academic progress that does not require large-scale surveys or complicated social network analysis. The key information used is a student’s self-reported friends’ average GPA relative to the student’s own GPA. This information can be easily collected from a single individual student. One could ask a student who are his/her friends in the class, and test if those self-reported friends’ average GPA is higher or lower than the student’s. If our finding is validated through more extensive studies, this test might serve as a simple, handy predictor of the student’s future performance in various educational settings. We note that our work is still limited in several aspects. First, the size of the subject population was very small (N = 158). It would be desirable to replicate the same study at different schools to test the robustness and generalizability of our observations. Second, the students’ background did not involve much cultural or ethnic diversity. Our subjects were predominantly white, living in a suburban or rural area in Upstate New York. We did not have data to control potential confounding effects of those socio-cultural variables. Lastly, the students’ social network was reconstructed using their self-report responses to the survey, without any other more objective or observational data (e.g., email or text exchanges, physical contacts). We currently do not have capability or resource to collect such data, but recent social science research [14], [15] has demonstrated that emerging technologies are enabling researchers to capture more detailed social dynamics of students at school. It will be very interesting to compare such high-resolution social network data with official school records and analyze correlations between the students’ socio-behavioral patterns and their academic trajectories.

Acknowledgments We thank Rita Spathis for reviewing the draft of this manuscript, and Benjamin James Bush and Jin Akaishi for their help in conducting the initial survey and data analysis.

Author Contributions Conceived and designed the experiments: DB CK CB BB JG JE HS. Performed the experiments: DB CK CB BB JG JE HS. Analyzed the data: DB CK CB BB HS. Contributed reagents/materials/analysis tools: JE HS. Wrote the paper: HS.