The environment is somewhat alike for different adaptive motivations: Machine learning reveals optimal motivational contexts involve collective support of parents, teachers, and peers

Abstract

Achievement motivation is fundamental for human flourishing. While numerous adaptive motivational constructs have been proposed, they are often examined in isolation without considering their shared contextual roots. To identify the contextual factors underlying different forms of adaptive achievement motivation, we conducted comprehensive analyses by integrating a global student assessment dataset (n = 77,068 middle-school students across 19 countries, M_age = 15.79). We conducted a literature review and identified 27 potential predictors theoretically and empirically related to achievement motivation, including immediate contextual factors available in the dataset and distal contextual factors available from varying sources. Results from machine learning analyses showed convergent patterns of the contextual predictors for adaptive motivation (self-efficacy, learning goals, and task mastery orientation). Specifically, the optimal environment for adaptive motivation is characterized by the collective positive influence of parents, teachers, and peers, rather than depending on one exclusively. In comparison, the pattern of other less adaptive motivation constructs (fixed mindsets, performance goals, and fear of failure) is idiosyncratic. These findings provide synthesized evidence consolidating achievement motivation research, highlighting the shared contextual foundations for various adaptive motivations. This integrative approach clarifies that the optimal motivational contexts involve the collective social support of parents, teachers, and peers.