Understanding salient trajectories and emerging profiles in the development of Chinese learners’ motivation: a growth mixture modeling approach

Abstract

Based on the theoretical framework of the L2 Motivational Self System (L2MSS), the present study aims to make a methodological contribution to L2 motivation research. With the application of a novel growth mixture modeling (GMM) technique, the study depicted developmental trajectories of three motivational variables (ideal L2 self, ought-to L2 self, and L2 learning experience) of 176 Chinese tertiary-level students over a period of two semesters. Results showed two to three salient classes with typical developmental patterns for the three motivational variables respectively, with which the study gained fresh insights into the developmental processes of motivation beyond the individual level. Our study further established three main multivariate profiles of motivation characterized by a distinct combination of different motivational variables. The findings extend our understanding of motivational dynamics, providing a nuanced picture of emergent motivational trajectories systemically. Additionally, GMM has shown to be an effective and applicable method for the identification of salient patterns in motivation development, which leads to practical implications.

The Quality of Fair Revaluation of Fixed Assets and Additional Calculations Aimed at Facilitating Prospective Investors’ Decisions

“…A small cross-section measurement may (i) lead to influenced standard errors, (ii) affect estimate parameters, and (iii) lead to a poor exceptional identification test due to influenced standard errors [37]. Device growth, according to [38], is to blame for these problems. It is a solution that reduces instrumental combination measurement.…”