Check out the new open-access journal article by Dr. Chunrui Zou and Yuqing Zou:

Zou, Y., & Zou, C. (2023). Exploring factors associated with higher education students' learning outcomes in emergency remote teaching environments during the COVID-19 pandemic: General patterns and individual differences. Education and Information Technologies. https://doi.org/10.1007/s10639-023-12032-9

（探索在COVID-19新冠大流行期间的紧急远程教学环境中，与高等教育学生学习成果相关的因素： 一般模式和个体差异）

Our purposes were to explore the factors associated with higher education students' learning outcomes in emergency remote teaching environments (ERTEs) during the COVID-19 pandemic at both the population and individual levels.

（我们的研究目的是从群体和个体两个层面探索在COVID-19新冠大流行期间的紧急远程教学环境中，与高等教育学生学习成果相关的因素。）

9418 students from 41 countries were selected for analysis from a survey-based dataset collected with the aim of understanding the self-perceived impacts of the first-wave COVID-19 pandemic on higher education students.

（我们从一个基于调查问卷的数据中选取了来自41个国家的9418名学生进行分析。收集此问卷的目的是了解高等教育学生如何感觉COVID-19新冠第一波大流行对于他们的影响。）

Fig. 1 Participant number for each country

We conducted structural equation modeling analysis to explore associated factors and latent profile analysis to identify student profiles based on these factors. Utilizing the identified profiles, we developed a random forest-based classifier to identify the membership of students' profiles.

（我们进行了结构方程建模分析以探究相关因素，并进行了潜在类型分析以根据这些因素识别学生类型。利用所识别的学生类型，我们开发了一种基于随机森林的分类器来识别学生类型的成员资格。）

Fig. 2 Framework of the design of this study

The results revealed that multiple environmental and individual factors were significantly associated with learning outcomes, each with varying path coefficient magnitudes.

（结构方程建模分析的结果表明，多种环境或个人因素与学生学习成果有显著相关性，且每种因素的路径系数大小各不相同。）

Fig. 3 Structural equation modeling analysis results

Based on these factors, eight profiles were identified with different learning outcomes and student characteristics. The classifier achieved a testing accuracy of 0.904.

（基于这些显著相关的因素，我们通过潜在类型分析识别出八个具有不同学习成果和学生背景特征的类型。分类器的测试准确率达到0.904。）

Fig. 4 Scores for six factors, learning outcomes, and academic emotions in each class. (a): heatmap; (b): line plot

Fig. 5 SEM model scaled scores for six factors in each class. (a): radar charts; (b): dot charts

By integrating variable-centered and person-centered approaches, this study bridges the gap in understanding general patterns and individual differences regarding key factors associated with higher education students' learning outcomes. The findings have implications for designing individualized interventions and support strategies to enhance student learning outcomes and mitigate educational disparities in ERTEs during crisis situations.

（通过整合以变量为中心和以人为中心的方法，本研究弥补了对于与高等教育学生学习成果相关的因素的一般模式和个体差异的理解的差距。研究结果对于在紧急远程教学环境中设计个性化的干预和支持策略，以促进学生学习成果和提升教育公平具有重要意义。）