Cultural Diversity in Academic Motivation: Universality and Model Complexity

This contribution centres on two interconnected conjectures. The first posits that motivational—and the way we model what influences motivation— can be significantly enhanced by integrating the concept of growth orientation. This integration enables to connect growth mindset frameworks and motivational theories through straightforward antecedent-consequence models. The second conjecture is that simpler models tend to vary less and are more stable across different groups compared to more complex models. An example brings these two conjectures together by analysing cultural differences in academic motivation. It compares three models using data from international students studying mathematics and statistics: (1) a comprehensive growth orientation model incorporating both global and specific factors, estimated using Bifactor Exploratory Structural Equation Modelling (B-ESEM); (2) a simplified version of this model with growth orientation as the sole antecedent factor; and (3) a Structural Equation Model (SEM) using specific factors as antecedents. In the illustrative example, the degree of cultural diversity in motivational levels is found to be relatively small, comparable in magnitude to gender differences. However, more complex antecedent-consequence models can easily lead to the conclusion that significant diversity exists between cultural groups. Our findings challenge common assumptions about the cultural specificity of academic motivation models and highlight a promising yet often overlooked factor for building robust explanations of learning motivation: students’ growth orientation.

Institutions

Publication Details

Journal
Cross-Cultural Education Studies
Published
2025-04-30
DOI
https://doi.org/10.63385/cces.v1i1.146
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Cultural Diversity in Academic Motivation: Universality and Model Complexity

Cross-Cultural Education Studies
Geochemistry and Geologic Mapping
article

Cultural Diversity in Academic Motivation: Universality and Model Complexity

article en

Abstract

This contribution centres on two interconnected conjectures. The first posits that motivational—and the way we model what influences motivation— can be significantly enhanced by integrating the concept of growth orientation. This integration enables to connect growth mindset frameworks and motivational theories through straightforward antecedent-consequence models. The second conjecture is that simpler models tend to vary less and are more stable across different groups compared to more complex models. An example brings these two conjectures together by analysing cultural differences in academic motivation. It compares three models using data from international students studying mathematics and statistics: (1) a comprehensive growth orientation model incorporating both global and specific factors, estimated using Bifactor Exploratory Structural Equation Modelling (B-ESEM); (2) a simplified version of this model with growth orientation as the sole antecedent factor; and (3) a Structural Equation Model (SEM) using specific factors as antecedents. In the illustrative example, the degree of cultural diversity in motivational levels is found to be relatively small, comparable in magnitude to gender differences. However, more complex antecedent-consequence models can easily lead to the conclusion that significant diversity exists between cultural groups. Our findings challenge common assumptions about the cultural specificity of academic motivation models and highlight a promising yet often overlooked factor for building robust explanations of learning motivation: students’ growth orientation.

Cross-Cultural Education StudiesVol. 1(1)
Maastricht University (NL)
Gender equality
Openalex Percentile: Top 32%
Geochemistry and Geologic Mapping
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.