Automatic Detection of Metacognitive Language and Student Achievement in an Online STEM College Course

Abstract

Metacognition is a valuable tool due to its role in self-regulated learning. However, online learning settings bring new challenges for engaging in metacognition given the unique opportunities and challenges presented by the online space, especially for diverse populations and students underrepresented in STEM (UR-STEM). Thus, we investigated whether a relationship existed between college STEM students’ metacognition—measured by their spontaneously produced metacognitive phrases in online course discussions forums—and their success in an online STEM college course—measured by their final course grade. Using Bayesian generalized linear models, we examined whether this relationship differed for UR-STEM compared to non-UR-STEM students and whether related course behaviors (i.e., engagement and verbosity) and prior knowledge predicted variance in course grade. Metacognition plausibly predicted course grade and no plausible differences between UR-and non-UR-STEM students were found, suggesting that the online space could afford students from diverse groups the capacity to engage equally in a critical aspect of self-regulated learning: metacognition. Implications of the results for teaching and learning STEM content in the online space are discussed.