Predicting time to graduation at a large enrollment American university.

John M Aiken,Riccardo De Bin,Morten Hjorth-Jensen,Marcos D Caballero

doi:10.1371/journal.pone.0242334

Abstract

The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend. Different universities have different populations, student services, instruction styles, and degree programs, however, they all collect institutional data. This study presents data for 160,933 students attending a large American research university. The data includes performance, enrollment, demographics, and preparation features. Discrete time hazard models for the time-to-graduation are presented in the context of Tinto’s Theory of Drop Out. Additionally, a novel machine learning method: gradient boosted trees, is applied and compared to the typical maximum likelihood method. We demonstrate that enrollment factors (such as changing a major) lead to greater increases in model predictive performance of when a student graduates than performance factors (such as grades) or preparation (such as high school GPA).

Highlights

University students must meet a number of objectives to obtain degrees and in many cases this can prolong their time at the university [1] or they drop out altogether [2]
We will first describe the effectiveness of the gradient boosted model in comparison to the traditional maximum likelihood model
We will describe the effectiveness of the gradient boosted model in comparison to the traditional maximum likelihood model

Summary

Introduction

University students must meet a number of objectives to obtain degrees and in many cases this can prolong their time at the university [1] or they drop out altogether [2]. Tinto theorized that a student’s college drop out decision is mediated by two conglomerate features: 1) educational goal commitment, and 2) institutional commitment. He further theorized that these commitments are dynamic. A student’s commitment to education is mediated by the initial state of a student entering the university and the dynamics that occur while the student attends university. A student’s commitment to an institution is tempered by many factors such as the educational goals available at an institution (e.g., a Predicting time to graduation at a large enrollment American university technical university degree offerings versus a liberal arts university), family commitment to a university, and social acceptance at the university. In this paper we focus on a student’s commitment to education as the framing for features that predict when a student will graduate if they do so

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Discussion

Conclusion