USING ATTITUDES AND ANXIETIES TO PREDICT END-OF-COURSE OUTCOMES IN ONLINE AND FACE-TO-FACE INTRODUCTORY STATISTICS COURSES

Authors

  • WHITNEY ALICIA ZIMMERMAN The Pennsylvania State University
  • STEFANIE R. AUSTIN The Pennsylvania State University

DOI:

https://doi.org/10.52041/serj.v17i2.159

Keywords:

Statistics education research, Course completion, Online education, Statistics attitudes, Statistics anxiety

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-to-face group.

First published November 2018 at Statistics Education Research Journal Archives

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Published

2018-11-30

Issue

Section

Regular Articles