Building a Machine Learning Model for Teacher Retention: Questionnaire In order to develop a machine learning model aimed at reducing teacher turnover and supporting educators, the following questions can guide the data collection and model-building process:

How can we help teachers stay in the profession?

Gain insights into factors contributing to teacher satisfaction and identify potential interventions that can enhance their professional experience. What type of application can we create to lower teacher turnover?

Explore ideas for a user-friendly application that addresses specific pain points leading to teacher turnover. Consider functionalities that promote engagement, support, and professional growth. How much data can we collect from the user?

Define the extent and nature of data that can be ethically and legally collected from individual teachers. Consider factors such as feedback, preferences, and performance indicators. How much data can we collect from Districts?

Understand the scope and limitations of data collection at the district level. Determine the types of data that can be accessed to gain a holistic view of the educational ecosystem. Where can we begin data collection?

Identify initial sources or touchpoints for data collection, such as teacher surveys, performance reviews, or professional development programs. When is a good time to collect data from educators?

Determine optimal moments during the academic year or specific milestones when educators are likely to provide valuable insights. Consider both regular intervals and critical periods. Why should we help contribute to public/private/charter education?

Articulate the overarching purpose and societal impact of the machine learning model. Understand the potential benefits for different types of educational institutions and emphasize the broader mission of supporting educators. These questions form a foundation for developing a comprehensive strategy to collect relevant data, define model features, and create a machine learning solution that genuinely addresses the challenges of teacher retention. It's crucial to approach data collection ethically, ensuring privacy and consent, and align the model with the goals of fostering a positive and supportive educational environment.