Learning Strategies as Moderators Between Motivation and Mathematics Performance in East Asian Students
Latent Class Analysis
Bibliographic Data
| ID | 16912430 |
|---|---|
| Authors | Yi-Jhen Wu (0000-0002-0038-9234, TU Dortmund University, corresponding author), Yi-Hsin Chen (0000-0002-6102-7544, University of South Florida), Sarah M Kiefer (0000-0001-9532-8890, University of South Florida), Claus H Carstensen (University of Bamberg) |
| Year | 2021 |
| Volume | 11 |
| Issue | 4 |
| Publication date | 2021-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | SAGE Open (JOURNAL) |
| Journal identifiers | ISSN: 2158-2440 • E-ISSN: 2158-2440 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/21582440211061382 |
| OpenAlex | W3214768884 |
| Language | EN |
| Citations received | 2 |
| References cited | 59 |
This study applied a three-step latent class analysis (LCA) approach to explore latent classes of learning strategy use and their moderation effects on the relationships between motivation and mathematics performance. The data of 15-year-old students from five East Asian educational systems related to Chinese culture in the Programme of International Student Assessment (PISA) in 2012 were analyzed. The findings indicated that Shanghai, Singapore, Taiwan, and Macau showed three latent classes of learning strategies, whereas Hong Kong had two latent classes. Most students in the five educational systems reported to use the control strategy, some students reported the use of combined learning strategies, and few students reported the use of memorization except for students in Shanghai. Furthermore, we found the moderation effects of learning strategy use on mathematics performance depended on the types of motivation and educational systems. This study provides insights into the advantages of a three-step LCA approach in educational research
Latent class model · Latent variable · Mathematics education · Memorization · Moderation · Statistics · Computer Science · Mathematics · Online Learning and Analytics · Psychology · School Choice and Performance · Social Psychology · Artificial Intelligence
Principled missing data methods for researchers
Effectiveness of learning strategy instruction on academic performance
Predicting high school students' cognitive engagement and achievement
Learning strategies
Classical Latent Profile Analysis of Academic Self-Concept Dimensions
A latent profile analysis of college students’ achievement goal orientation
The role of motivation in promoting and sustaining self-regulated learning
Misconceptions about the learning approaches, motivation and study practices of Asian students
Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling
Attaining Self-Regulation
Factor Analysis and AIC
Estimating the Dimension of a Model
Application of Model-Selection Criteria to Some Problems in Multivariate Analysis
Ten frequently asked questions about latent class analysis.
Auxiliary Variables in Mixture Modeling
Students’ Trait Emotional Intelligence and Perceived Teacher Emotional Support in Preventing Burnout
Pisa and high-performing education systems
Latent Class Modeling with Covariates
The moderator-mediator variable distinction in social psychological research
The Making of the Motivated Strategies for Learning Questionnaire
Cultural Capital in East Asian Educational Systems
| Unique citing works | 2 |
|---|---|
| Citations per year | 1 |
| Citation span | 2024 - 2025 (2) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |