Predict and Prevent Bullying via Technology

Abstract

Bullying in schools is spreading like cancer and there is an immediate need to predict and prevent bullying. The paper discusses the project undertaken to research the phenomena of predicting and preventing bullying via technology in K-12 education. The predictive bullying data for the research was obtained from publicly available US education department databases and other sources. The data for both traditional bullying and cyberbullying was examined for school enrollment demographics, personal characteristics of students who were bullied and perpetuators. Valid predominant values for predictors such as race, gender, locale, enrollment size and household income were identified. The implications of these findings are used to provide design recommendations for technology solutions and prevention strategies that use technology. The research addresses the question of whether combining predictive bullying data and personal characteristics of students to tailor bullying prevention programs and solutions using technology at schools will increase the chances of successfully predicting and preventing bullying.