Using attitudes and anxieties to predict end-ofcourse outcomes in online and face-to-face introductory statistics courses

Abstract

An abbreviated form of the Statistics Anxiety Rating Scale (STARS) was administered to online and face-to-face introductory statistics students. Subscale scores were used to predict final exam grades and successful course completion. In predicting final exam scores, self-concept, and worth of statistics were found to be statistically significant with no significant difference by campus (online versus face-to-face). Logistic regression and random forests were used to predict successful course completion, with campus being the only significant predictor in the logistic model and face-to-face students being more likely to successfully complete the course. The random forest model indicated that self-concept and test anxiety were overall the best predictors, whereas separately test anxiety was the best predictor in the online group and self-concept was the best predictor in the face-toface group.