Interdependent versus independent research: An overdue shift in perspective
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https://doi.org/10.70116/2980274131Keywords:
Postmodern analysis, network analysis, AI, machine learning, student achievementAbstract
Traditionally, research in the social sciences has focused on the role of individual attitudes, skills, and dyadic relationships in shaping educational outcomes. However, less attention has been paid to the influence of broader patterns of social interaction, particularly within school contexts. This study demonstrated how interaction-based data could be generated and analyzed to better capture these dynamics. Drawing on data collected from 2,682 students and 118 teachers across 10 schools, we applied an AI-driven machine learning algorithm to examine the effects of interactive dynamics on student achievement. Results indicate that while socioeconomic status (SES) remains a consistent predictor of student test scores, the most significant effects stem from interactions related to social capital, the diversity of information each person has access to, and to the degree of effort one invests in network dynamics. These findings highlight the value of incorporating social network structures into educational research and suggest that interactive dynamics within school communities may play a pivotal role in shaping student achievement.
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