Research Article
A latent profile analysis of students’ motivation of engaging in one-to-one computing environment for English learning
@ARTICLE{10.4108/eai.25-9-2018.155574, author={Shan Li and Juan Zheng}, title={A latent profile analysis of students’ motivation of engaging in one-to-one computing environment for English learning}, journal={EAI Endorsed Transactions on e-Learning}, volume={5}, number={17}, publisher={EAI}, journal_a={EL}, year={2018}, month={10}, keywords={one-to-one computing environment, latent profile analysis, self-efficacy, task value, task anxiety}, doi={10.4108/eai.25-9-2018.155574} }
- Shan Li
Juan Zheng
Year: 2018
A latent profile analysis of students’ motivation of engaging in one-to-one computing environment for English learning
EL
EAI
DOI: 10.4108/eai.25-9-2018.155574
Abstract
This study used latent profile analysis to cluster students into three groups with homogenous motivational profiles based on self-reported self-efficacy, task value and task anxiety measures obtained from 263 middle school students. The results demonstrated that there were distinct motivation profiles among students while engaging in a one-to-one computing environment for English learning, which resulted in differences on their performance. In general, this eLearning environment had a significant positive effect on students’ learning achievements regardless of various motivation profiles. But students with high self-efficacy, task value while low task anxiety performed better than those in other profiles. This study also suggested that task anxiety impeded students from benefiting from the one-to-one computing environment, but it could not significantly affect students’ learning outcomes. The profiling of student motivation orientations enhanced our understanding of the complex interactions of various motivational components and extended our existing knowledge in this emerging area of student learning. Besides, the findings inform future interventions in curriculum design and effective scaffoldings.
Copyright © 2018 S. Li and J. Zheng, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.