Use formal computational modelling to specify dynamic causal processes in motivation theories¶

Strategy · All strategies
Evidence · 4 claims (4 for) · 1 study (1 theoretical), q1 · 0 of 1 report an effect size · 4 claims rest on one study

Description¶

The article recommends that motivation researchers develop or refine theories using formal (computational) modelling, which "requires that theories specify the precise interrelationships between variables through mathematical expressions", forcing precise thinking about causal mechanisms and enabling validation against empirical data. It distinguishes computational modelling (translating theory into computational terms) from statistical modelling (primarily empirical validation), notes dynamic structural equation modelling makes this easier for applied researchers, and points to dynamic systems approaches for nonlinear interdependence over time.

Design Implications¶

Context¶

Requirements¶

• Data with a sufficient number of time points collected with reliable and valid measures
• Formal training in theory and computational modelling

Constraints¶

• Progress in dynamic systems modeling is rather theoretical and suitable longitudinal data are scarce; construct validity of multimodal or digital trace data remains a big issue

Target Learners¶

• educational psychology researchers and graduate trainees

Target Learning Goals¶

• specifying and testing dynamic causal mechanisms of motivated behavior

Claims¶

• The constellation of dominant motivation theories in educational psychology has changed very little over recent decades [+W]
• Empirical tests of motivation theories tend to be simple, obvious tests of statistical relationships between constructs and their antecedents and outcomes, making supporting evidence easy to obtain [+W]
• The meaning of theoretical arrows (causal vs predictive, timescale, functional form, magnitude) is unclear, and cross-lagged effect signs can change with assessment intervals [+W]
• Motivation theories underspecify causal mechanisms, so empirical findings on mediation cannot refute or challenge them [+W]

Related Strategies¶

• Increase formal training in theory development and treat theories as tools rather than group commitments
• Research agenda: investigate affective dynamics with within-person, longitudinal, and multi-channel methods

Examples¶

-

Key Sources¶

• Murayama, K., & von Keyserlingk, L. (2025). A critical analysis of the current motivation theories in educational psychology: Why the same theories continue to dominate. https://doi.org/10.1080/00461520.2025.2473894