Data-Driven Moderated Mediation Analysis of the Mindset-Plus-Context Theory: The Moderating Role of Classroom Factors

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Abstract

A growth mindset - the belief that intelligence can be developed - has been identified as a critical motivational factor in improving math performance (Yeager et al., 2019; Blackwell et al., 2007). Yet, the path from a growth mindset to enhanced math performance is complex. To refine theoretical models within the mindset literature, my research uses Bayesian Additive Regression Trees (BART), a machine learning approach, to 1) examine the mediating role of behavioral learning engagement underlying the relationship between adolescents’ growth mindset and math performance and 2) explore the variation in this mediation mechanism due to classroom factors. Data collection and entry were finalized. I will apply BART to the data and expect to obtain results by January 2025.

Author

• Tetsuya Yamada, University of Pittsburgh