Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness
This paper uses machine learning methods to identify key predictors of teacher effectiveness, proxied by student learning gains linked to a teacher over an academic year. Conditional inference forests and the least absolute shrinkage and selection...
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World Bank, Washington, DC
2021
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| Online Access: | http://documents.worldbank.org/curated/undefined/737101636989604541/Preparation-Practice-and-Beliefs-A-Machine-Learning-Approach-to-Understanding-Teacher-Effectiveness http://hdl.handle.net/10986/36600 |
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okr-10986-366002021-11-19T05:10:44Z Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness Filmer, Deon Nahata, Vatsal Sabarwal, Shwetlena EDUCATION TEACHER PERFORMANCE TEACHER VALUE-ADDED TEACHER MINDSET STUDENT ACHIEVEMENT MACHINE LEARNING TEACHER EFFECTIVENESS This paper uses machine learning methods to identify key predictors of teacher effectiveness, proxied by student learning gains linked to a teacher over an academic year. Conditional inference forests and the least absolute shrinkage and selection operator are applied to matched student-teacher data for math and Kiswahili from grades 2 and 3 in 392 schools across Tanzania. These two machine learning methods produce consistent results and outperform standard ordinary least squares in out-of-sample prediction by 14–24 percent. As in previous research, commonly used teacher covariates like teacher gender, education, experience, and so forth are not good predictors of teacher effectiveness. Instead, teacher practice (what teachers do, measured through classroom observations and student surveys) and teacher beliefs (measured through teacher surveys) emerge as much more important. Overall, teacher covariates are stronger predictors of teacher effectiveness in math than in Kiswahili. Teacher beliefs that they can help disadvantaged and struggling students learn (for math) and they have good relationships within schools (for Kiswahili), teacher practice of providing written feedback and reviewing key concepts at the end of class (for math), and spending extra time with struggling students (for Kiswahili) are highly predictive of teacher effectiveness. As is teacher preparation on how to teach foundational topics (for both Math and Kiswahili). These results demonstrate the need to pay more systematic attention to teacher preparation, practice, and beliefs in teacher research and policy. 2021-11-18T17:54:10Z 2021-11-18T17:54:10Z 2021-11 Working Paper http://documents.worldbank.org/curated/undefined/737101636989604541/Preparation-Practice-and-Beliefs-A-Machine-Learning-Approach-to-Understanding-Teacher-Effectiveness http://hdl.handle.net/10986/36600 English Policy Research Working Paper;No. 9847 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper Africa Africa Eastern and Southern (AFE) Tanzania |
| repository_type |
Digital Repository |
| institution_category |
Foreign Institution |
| institution |
Digital Repositories |
| building |
World Bank Open Knowledge Repository |
| collection |
World Bank |
| language |
English |
| topic |
EDUCATION TEACHER PERFORMANCE TEACHER VALUE-ADDED TEACHER MINDSET STUDENT ACHIEVEMENT MACHINE LEARNING TEACHER EFFECTIVENESS |
| spellingShingle |
EDUCATION TEACHER PERFORMANCE TEACHER VALUE-ADDED TEACHER MINDSET STUDENT ACHIEVEMENT MACHINE LEARNING TEACHER EFFECTIVENESS Filmer, Deon Nahata, Vatsal Sabarwal, Shwetlena Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness |
| geographic_facet |
Africa Africa Eastern and Southern (AFE) Tanzania |
| relation |
Policy Research Working Paper;No. 9847 |
| description |
This paper uses machine learning
methods to identify key predictors of teacher effectiveness,
proxied by student learning gains linked to a teacher over
an academic year. Conditional inference forests and the
least absolute shrinkage and selection operator are applied
to matched student-teacher data for math and Kiswahili from
grades 2 and 3 in 392 schools across Tanzania. These two
machine learning methods produce consistent results and
outperform standard ordinary least squares in out-of-sample
prediction by 14–24 percent. As in previous research,
commonly used teacher covariates like teacher gender,
education, experience, and so forth are not good predictors
of teacher effectiveness. Instead, teacher practice (what
teachers do, measured through classroom observations and
student surveys) and teacher beliefs (measured through
teacher surveys) emerge as much more important. Overall,
teacher covariates are stronger predictors of teacher
effectiveness in math than in Kiswahili. Teacher beliefs
that they can help disadvantaged and struggling students
learn (for math) and they have good relationships within
schools (for Kiswahili), teacher practice of providing
written feedback and reviewing key concepts at the end of
class (for math), and spending extra time with struggling
students (for Kiswahili) are highly predictive of teacher
effectiveness. As is teacher preparation on how to teach
foundational topics (for both Math and Kiswahili). These
results demonstrate the need to pay more systematic
attention to teacher preparation, practice, and beliefs in
teacher research and policy. |
| format |
Working Paper |
| author |
Filmer, Deon Nahata, Vatsal Sabarwal, Shwetlena |
| author_facet |
Filmer, Deon Nahata, Vatsal Sabarwal, Shwetlena |
| author_sort |
Filmer, Deon |
| title |
Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness |
| title_short |
Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness |
| title_full |
Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness |
| title_fullStr |
Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness |
| title_full_unstemmed |
Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness |
| title_sort |
preparation, practice, and beliefs : a machine learning approach to understanding teacher effectiveness |
| publisher |
World Bank, Washington, DC |
| publishDate |
2021 |
| url |
http://documents.worldbank.org/curated/undefined/737101636989604541/Preparation-Practice-and-Beliefs-A-Machine-Learning-Approach-to-Understanding-Teacher-Effectiveness http://hdl.handle.net/10986/36600 |
| _version_ |
1764485587123830784 |