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运用主成分分析法对疫情期间影响职业治疗教育中电子学习过程的因素进行调查。

Investigation of the factors affecting the e-learning process in occupational therapy education during the pandemic with principal component analysis.

作者信息

Öztürk Başar, Akarsu Remziye, Kayıhan Hülya, Çelik Yusuf, Kayhan Saynur Elif

机构信息

Faculty of Health Sciences, Deparment of Occupational Therapy, Biruni University, İstanbul, Turkey.

Department of Biostatistics and Medical Informatics, Medical School, Biruni University, İstanbul, Turkey.

出版信息

Br J Occup Ther. 2022 Sep;85(9):694-703. doi: 10.1177/03080226211070472.

Abstract

INTRODUCTION

The aim of this study is to examine the factors affecting the e-learning process in occupational therapy education in the COVID-19 period.

METHOD

In the study, a form containing personal information and questions about the e-learning process, the International Physical Activity Questionnaire, the Academic Self-Efficacy Scale, the Perceived Stress Scale, and the Rosenberg Self-Esteem Scale were applied to 253 occupational therapy students via the Google form. Principal component analysis was used to evaluate the data.

RESULTS

A large number of questionnaires were applied in the study, and principal component analysis, an advanced statistical method that enables the interpretation of this type of big data more effectively, was used. 13 components were determined, and a variance of 88% was explained. The main components were listed as students' self-perception about the education system, learning methods, home and university environment, information technologies, physical activity level, and academic performance/participation.

CONCLUSION

We hope that the results of our study will provide a perspective on what innovations can be made for quality improvement in occupational therapy education. It would be beneficial to increase student feedback by applying similar studies in other education programs.

摘要

引言

本研究旨在探讨在新冠疫情期间影响职业治疗教育中电子学习过程的因素。

方法

在本研究中,通过谷歌表单向253名职业治疗专业学生发放了一份包含个人信息以及关于电子学习过程问题的表格、国际体力活动问卷、学业自我效能量表、感知压力量表和罗森伯格自尊量表。采用主成分分析对数据进行评估。

结果

本研究发放了大量问卷,并使用了主成分分析这一能更有效解释此类大数据的先进统计方法。确定了13个成分,解释了88%的方差。主要成分包括学生对教育系统、学习方法、家庭和大学环境、信息技术、体力活动水平以及学业成绩/参与度的自我认知。

结论

我们希望本研究结果能为职业治疗教育质量提升方面可进行哪些创新提供一个视角。在其他教育项目中应用类似研究以增加学生反馈将是有益的。

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