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Applying academic analytics: developing a process for utilizing Bayesian networks to predict stopping out among community college students

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Many methodological approaches have been utilized to predict student retention and persistence over the years, yet few have utilized a Bayesian framework. It is believed this is due in part to the absence of an established process for guiding educational researchers reared in a frequentist perspective into the realms of…

Many methodological approaches have been utilized to predict student retention and persistence over the years, yet few have utilized a Bayesian framework. It is believed this is due in part to the absence of an established process for guiding educational researchers reared in a frequentist perspective into the realms of Bayesian analysis and educational data mining. The current study aimed to address this by providing a model-building process for developing a Bayesian network (BN) that leveraged educational data mining, Bayesian analysis, and traditional iterative model-building techniques in order to predict whether community college students will stop out at the completion of each of their first six terms. The study utilized exploratory and confirmatory techniques to reduce an initial pool of more than 50 potential predictor variables to a parsimonious final BN with only four predictor variables. The average in-sample classification accuracy rate for the model was 80% (Cohen's κ = 53%). The model was shown to be generalizable across samples with an average out-of-sample classification accuracy rate of 78% (Cohen's κ = 49%). The classification rates for the BN were also found to be superior to the classification rates produced by an analog frequentist discrete-time survival analysis model.
ContributorsArcuria, Philip (Author) / Levy, Roy (Thesis advisor) / Green, Samuel B (Committee member) / Thompson, Marilyn S (Committee member) / Arizona State University (Publisher)
Created2015
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Developmental pathways to antisocial behavior in early-adolescence: examining changes in aggression and peer exclusion through childhood

Description
This study examined the influence of childhood aggression, peer exclusion and associating with deviant peers on the development of antisocial behavior in early adolescence. To gain a stronger understanding of how these factors are associated with antisocial behavior and delinquency, multiple alternative pathways were examined based on additive, mediation and…

This study examined the influence of childhood aggression, peer exclusion and associating with deviant peers on the development of antisocial behavior in early adolescence. To gain a stronger understanding of how these factors are associated with antisocial behavior and delinquency, multiple alternative pathways were examined based on additive, mediation and incidental models. A parallel process growth model was specified to assess whether early childhood aggression and peer exclusion (in 1st grade) and intra-individual increases in aggressive behaviors and exclusion through childhood (grades 1 to 6) are predictive of associating with deviant peers (in 7th grade) and antisocial behavior (in 8th grade). Based on a sample of 383 children (193 girls and 190 boys), results showed the strongest support for an additive effects model in which early childhood aggression, increases in aggression, increases in peer exclusion and associating with more deviant peers all predicted antisocial behavior. These findings have implications for how children's psychological adjustment is impacted by their behavioral propensities and peer relational context and the importance of examining developmental processes within and between children over time.
ContributorsEttekal, Idean (Author) / Ladd, Gary W (Thesis advisor) / Eggum, Natalie D (Committee member) / Thompson, Marilyn S (Committee member) / Arizona State University (Publisher)
Created2011