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AI summary · arXiv: 1708.09344v1 · stat.OT

Stem-ming the Tide: Predicting STEM attrition using student transcript data

Lovenoor Aulck, Rohan Aras, Lysia Li, Coulter L'Heureux, Peter Lu, Jevin West
Original paper Submitted to arXiv on 28 Aug. 2017 License: NONEXCLUSIVE-DISTRIB 1.0

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The essentials

  • STEM fields are important for economic growth and generate high-paying jobs
  • The US is falling behind other countries in STEM education and training
  • There is a predicted shortage of domestic STEM-trained professionals
  • Reducing the rate of students leaving STEM-related fields in higher education could help address this deficit
  • Over half of all students intending to graduate with a STEM degree eventually drop out
  • Little quantitative research has been conducted on the causes of STEM attrition or using machine learning to predict it accurately
  • The paper "Stem-ming the Tide" details efforts to model and predict dropout from STEM fields using student transcript data
  • Attrition from STEM fields can be accurately predicted with data collected during students' first academic year
  • A method is proposed to model student STEM intentions for each academic term to better understand timing of attrition events
  • Machine learning techniques can be used by universities to improve retention rates in both traditional and non-traditional campus settings.

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AI-generated keywords: STEM attrition machine learning student transcript data STEM intentions retention rates

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  • Original abstract
The Science, Technology, Engineering, and Math (STEM) fields are crucial to the growth of national and international economies as they drive innovation and generate high-paying jobs. However, the US is falling behind other highly industrialized nations in terms of STEM education and training. Economic forecasts predict a shortage of domestic STEM-trained professionals for years to come. To address this deficit, reducing the rate at which students leave STEM-related fields in higher education is a potential solution since over half of all students intending to graduate with a STEM degree eventually drop out. Despite this issue, little quantitative research has been conducted at scale on the causes of STEM attrition or using machine learning to predict it accurately. In their paper titled "Stem-ming the Tide: Predicting STEM attrition using student transcript data," authors Lovenoor Aulck, Rohan Aras, Lysia Li, Coulter L'Heureux, Peter Lu, and Jevin West detail their efforts to model and predict dropout from STEM fields using one of the largest datasets used for research on students at a traditional campus setting. The results suggest that attrition from STEM fields can be accurately predicted with data routinely collected at universities using only information from students' first academic year. Additionally, they propose a method to model student STEM intentions for each academic term to better understand the timing of STEM attrition events. These findings show great promise in helping universities use machine learning techniques to improve retention rates in both traditional and non-traditional campus settings.
Created on 06 May. 2023

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