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 theleast absolute shrinkage and selection operator are applied to matched student-teacher data for math and Kiswahili fromgrades 2 and 3 in 392 schools across Tanzania. These two machine learning methods produce consistent results andoutperform 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 predictorsof teacher effectiveness. Instead, teacher practice (what teachers do, measured through classroom observations andstudent 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 beliefsthat they can help disadvantaged and struggling studentslearn (for math) and they have good relationships within schools (for Kiswahili), teacher practice of providingwritten feedback and reviewing key concepts at the end of class (for math), and spending extra time with strugglingstudents (for Kiswahili) are highly predictive of teacher effectiveness. As is teacher preparation on how to teachfoundational topics (for both Math and Kiswahili). These results demonstrate the need to pay more systematicattention to teacher preparation, practice, and beliefs in teacher research and policy.