Predicting Graduation Rates at 4-year Broad Access Institutions Using a Bayesian Modeling Approach

This study models graduation rates at 4-year broad access institutions (BAIs). We examine the student body, structural-demographic, and financial characteristics that best predict 6-year graduation rates across two time periods (2008–2009 and 2014–2015). A Bayesian model averaging approach is utilized to account for uncertainty in variable selection in modeling graduation rates. Evidence suggests that graduation rates can be predicted by religious affiliation, proportion of students enrolled full-time, socioeconomic status of the student body, enrollment size and institutional revenue and expenditures. Findings also demonstrate that relatively fewer variables predict institutional graduation rates for Latina/o and African American students at 4-year BAIs. We conclude with implications for policy and key recommendations for research focused on 4-year BAIs.     

Working memory,cognitive style,and behavioural predictors of GCSE exam success

This study investigates the interplay of working memory, cognitive style, and behaviour. Year 8 (aged 13 years) students (n = 205) at a UK urban secondary school were tested to ascertain predictors of General Certificate of School Education (GCSE) achievement. Assessment included Riding's cognitive style dimensions, working memory capacity, and a profile of school behaviour. A stepwise multiple regression analysis indicated that behaviour, working memory, verbal–imagery style by working memory, wholist–analytic style by working memory, and verbal–imagery style by wholist–analytic style predicted GCSE outcome, accounting for 58% of the variance. Generally, poor GCSE grade points were predicted by low behaviour scores, and by low working memory capacity in analytics and verbalisers. The results are discussed in terms of possible interventions that may improve student GCSE performance.