No Fragile Family Left Behind - Targeted Indicators of Academic Performance
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Academic performance is a key component in the development and subsequent empowerment of youth. It is affected by a large number of different factors, such as inherent ability and socioeconomic circumstance, which can vary widely amongst different individuals. In particular, children from disadvantaged families face unique challenges that do not occur in normal families. We analyze the Fragile Families Challenge (FFC) dataset using data science algorithms, and study the relationship between the features reported and GPA scores. We grouped GPA scores into three groups (top, middle and low) and used a random forest classifier to predict the GPA class for each subject. Then, we used a recently developed algorithm---Local Interpretable Model-Agnostic Explanations (LIME)---to cluster subjects into subgroups based on the factors affecting each individual. We further analyzed the clusters to elucidate the differences occurring within different subgroups in the community of disadvantaged individuals and figure out which set of features matter for each subgroup. Conventional studies seek factors which apply to all members of a population, and often overlook the unique needs of individuals with different backgrounds and characteristics or divide individuals into predetermined subgroups and study which factors affect each subgroup. The approach used here can find correlations which are specific to individual subjects, and can inform the formulation of targeted and effective intervention strategies. Our study contributes to the field of social science by highlighting the differences of indicators of academic performance in different subgroups. Also, our novel data science pipeline contributes to the fields of data science and computational social science.
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