Factors affecting teacher job satisfaction: a causal inference machine learning approach using data from TALIS 2018

被引：4

作者：

McJames, Nathan ^{[1
,2
]}

Parnell, Andrew ^{[1
,2
]}

O'Shea, Ann ^{[2
]}

机构：

[1] Maynooth Univ, Hamilton Inst, Maynooth, Kildare, Ireland

[2] Maynooth Univ, Dept Math & Stat, Maynooth, Kildare, Ireland

来源：

EDUCATIONAL REVIEW | 2025年 / 77卷 / 02期

关键词：

Teacher job satisfaction; teacher retention; causal inference; machine learning; TALIS; BEGINNING TEACHERS; SELF-EFFICACY; ACHIEVEMENT; RETENTION; RECRUITMENT; INDUCTION; ATTRITION; SCHOOLS; QUALITY;

D O I：

10.1080/00131911.2023.2200594

中图分类号：

G40 [教育学];

学科分类号：

040101 ; 120403 ;

摘要：

Teacher shortages and attrition are problems of international concern. One of the most frequent reasons for teachers leaving the profession is a lack of job satisfaction. Accordingly, in this study we have adopted a causal inference machine learning approach to identify practical interventions for improving overall levels of job satisfaction. We apply our methodology to the English subset of the data from TALIS 2018. Of the treatments we investigate, participation in continual professional development and induction activities are found to have the most positive effect. The negative impact of part-time contracts is also demonstrated.

引用

页码：381 / 405

页数：25

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