Combining Machine Learning Explanation Methods with Expectancy-Value Theory to Identify Tailored Interventions for Engineering Student Persistence

University Of Louisville Research Foundation Inc, Louisville KY

Investigators

Abstract

This project aims to serve the national interest by applying machine learning (ML) methods to improve engineering student persistence. The increasing demand for a diverse, qualified engineering workforce requires an improvement in undergraduate program persistence rates, especially for underrepresented and minoritized students. This Engaged Student Learning Level 2 project intends to apply well-established predictive ML methods and cutting-edge ML explanation methods to identify and predict which students might leave and why. The project team plans to identify tailored interventions to meet individual student needs. This project will test the viability and variability of various ML methods and generalize the methodology for other universities. The proposed project has the potential to generate knowledge and streamline a methodology for the identification of individualized interventions for engineering persistence. The project is grounded in expectancy-value theory. The proposed methodology will help engineering education researchers to begin intervening in the first year to help at-risk students persist. This project also has the potential to have broad impacts on computer science communities which require real-world applications of emerging tools. Project findings will be presented at the American Society of Engineering Education annual conference and Frontiers in Education annual conference. In addition, all python code for each stage will be made available through the Open Science Foundation. The codes will be accompanied with detailed instructions for using the proposed methodology with new data. The proposed methods to improve engineering persistence potentially have broad impacts in a local, nation-wide, or global scale. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.