An exploratory study of teacher retention using data mining



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The object of this investigation is to report a study of mathematics teacher retention in the Texas Education System by generating a model that allows the identification of crucial factors that are associated with teacher retention in their profession. This study answers the research question: given a new mathematics teacher with little or no service in the Texas Education System, how long might one expect her to remain in the system? The basic categories, used in this study to describe teacher retention are: long term (10 and more years of service), medium term (5 to 9 years of service), and short term (1 to 4 years of service). The research question is addressed by generating a model through data mining techniques and using teacher data and variables from the Texas Public Education Information Management System (PEIMS) that allows a descriptive identification of those factors that are crucial in teacher retention. Research on mathematics teacher turnover in Texas has not yet focused on teacher characteristics. The literature review presented in this investigation shows that teacher characteristics are important in studying factors that may influence teachers' decisions to stay or to leave the system. This study presents the field of education, and the state of Texas, with an opportunity to isolate those crucial factors that keep mathematics teachers from leaving the teaching profession, which has the potential to inform policy makers and other educators when making decisions that could have an impact on teacher retention. Also, the methodology applied, data mining, allows this study to take full advantage of a collection of valuable resources provided by the Texas Education Agency (TEA) through the Public Education Information Management System (PEIMS), which has not yet been used to study the phenomenon of teacher retention.