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

Auteurs : Lovenoor Aulck, Rohan Aras, Lysia Li, Coulter L'Heureux, Peter Lu, Jevin West

Soumis à arXiv le 28 Aoû. 2017

Résumé scientifique

Science, technology, engineering, and math (STEM) fields play growing roles in national and international economies by driving innovation and generating high salary jobs. Yet, the US is lagging behind other highly industrialized nations in terms of STEM education and training. Furthermore, many economic forecasts predict a rising shortage of domestic STEM-trained professions in the US for years to come. One potential solution to this deficit is to decrease the rates at which students leave STEM-related fields in higher education, as currently over half of all students intending to graduate with a STEM degree eventually attrite. However, little quantitative research at scale has looked at causes of STEM attrition, let alone the use of machine learning to examine how well this phenomenon can be predicted. In this paper, we detail our efforts to model and predict dropout from STEM fields using one of the largest known datasets used for research on students at a traditional campus setting. Our results suggest that attrition from STEM fields can be accurately predicted with data that is routinely collected at universities using only information on students' first academic year. We also propose a method to model student STEM intentions for each academic term to better understand the timing of STEM attrition events. We believe these results show great promise in using machine learning to improve STEM retention in traditional and non-traditional campus settings.

Les articles autour de lui, par date et par proximité

Plus haut, c’est plus proche de cet article ; à gauche, c’est plus ancien. Les traits relient les articles proches entre eux : les groupes de traits sont des sous-thèmes. Sélectionnez un point pour voir pourquoi il est là. Retour au résumé de cet article

Les articles les plus proches Les travaux antérieurs sur lesquels il s’appuie Les articles qui le citent

Les articles autour de celui-ci

Classés selon la proximité de leur titre et de leur résumé avec ceux de cet article. Sélectionnez-en pour les lire ensemble.

Les articles les plus proches

Apparenté
Determining feature importance for actionable climate change mitigation policies
Romit Maulik, Junghwa Choi, Wesley Wehde et al. · 2020 · Autres statistiques · Résumé IA disponible

Résumé scientifique
Given the importance of public support for policy change and implementation, public policymakers and researchers have attempted to understand the factors associated with this support for climate change mitigation policy. In this article, we compare the feasibility of using different supervised learning methods for regression using a novel socio-economic data set which measures public support for potential climate change mitigation policies. Following this model selection, we utilize gradient boosting regression, a well-known technique in the machine learning community, but relatively uncommon in public policy and public opinion research, and seek to understand what factors among the several …
Apparenté
Generative AI for Data Science 101: Coding Without Learning To Code
Jacob Bien, Gourab Mukherjee · 2024 · Autres statistiques · Résumé IA disponible

Résumé scientifique
Should one teach coding in a required introductory statistics and data science class for non-major students? Many professors advise against it, considering it a distraction from the important and challenging statistical topics that need to be covered. By contrast, other professors argue that the ability to interact flexibly with data will inspire students with a lasting love of the subject and a continued commitment to the material beyond the introductory course. With the release of large language models that write code, we saw an opportunity for a middle ground, which we tried in Fall 2023 in a required introductory data science course in our school's full-time MBA program. We taught studen…
Apparenté
How to survive the Squid Games using probability theory
Elena Moltchanova, Miguel Moyers-González, Geertrui Van de Voorde et al. · 2024 · Autres statistiques · Pas encore résumé

Résumé scientifique
In this paper, we consider how probability theory can be used to determine the survival strategy in two of the ``Squid Game" and ``Squid Game: The Challenge" challenges: the Hopscotch and the Warships. We show how Hopscotch can be easily tackled with the knowledge of the binomial distribution, taught in introductory statistics courses, while Warships is a much more complex problem, which can be tackled at different levels.
Apparenté
Synthesis of High-Resolution Load Profiles with Minimal Data
Thomas Schnake, David Bauer · 2019 · Autres statistiques · Résumé IA disponible

Résumé scientifique
For the estimation of a new energy supply system it is an important to have high-resolution energy load profile. Such a profile is in general either not present or very costly to obtain. We will therefore present a method which synthesizes load profiles from minimal given data, but with maximal resolution. The general initial data setting includes month integrals and load profiles a few days. The resulting time series features all important properties to represent a real energy profile.

Aller plus loin

Comment ces articles sont-ils trouvés ?

Nous comparons le titre et le résumé de cet article avec ceux de tous les articles résumés sur ce site, et avec les articles que Semantic Scholar recommande, ceux qu’il cite et ceux qui le citent (pour un article très cité, ses citations les plus récentes et, depuis OpenAlex, les plus citées). Parmi les travaux antérieurs et les suites, les articles qui s’utilisent en profondeur, une méthode qu’il utilise et les suites très citées remontent un peu. Les niveaux (très proche, proche, apparenté) viennent de la similarité des textes, pas d’un jugement de qualité ; * signale un article sans résumé, comparé par son titre. La phrase sous un article est écrite par une IA à partir des deux résumés : vérifiez-la dans les articles. Un article jugé hors sujet par trois lecteurs quitte la liste.

Pour l’instant seulement parmi les articles résumés sur ce site : relancez la recherche pour ajouter les travaux antérieurs, les suites et les recommandations de Semantic Scholar.