Predicting Online Learners' Knowledge of Statistics: The Role of Self-Efficacy, Metacognitive Monitoring, and Technology Acceptance

• In Event: Marriott Roundtable Session 5
  In Roundtable Session: Empowering Learners' Self-Regulation Skills in Online Learning

Abstract

In this study, self-efficacy, metacognitive monitoring, and technology acceptance were used to predict conceptual knowledge in the context of learning statistics online. One hundred and twenty-five students in an online introductory statistics course competed measures on statistics and online learning self-efficacy, calibration accuracy and bias, and perceived ease of use and usefulness of the online course. Statistical knowledge was positively correlated with statistics self-efficacy and technology acceptance, and negatively correlated with calibration accuracy and bias, but not with online learning self-efficacy. Regression analysis indicated that knowledge was predicted by content learning oriented variables including metacognitive monitoring and statistics self-efficacy. Technology perception oriented variables, including online learning self-efficacy and technology acceptance did not add predictive power to the regression model.

Authors

• Huiqing Huiqing Hu, The Pennsylvania State University

• Rayne A. Sperling, Pennsylvania State University

• Whitney Alicia Zimmerman, The Pennsylvania State University