Estimating student retention and degree-completion time: decision trees and neural networks vis-à-vis regression
Abstract
Abstract Focusing on student retention and time to degree completion, this study illustrates how institutional researchers may benefit from the power of predictive analyses associated with data‐mining tools.
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Initial research articles predicting performance estimated time to degree completion using student demographic, academic, residential and financial aid information, student parent data and school transfer records.
“Initial research articles predicting performance estimated time to degree completion (Herzog, 2006) using student demographic, academic, residential and financial aid information, student parent data and school transfer records.”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Herzog, 2006
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- Authors
- Herzog, S.
- Affiliations
- University of Nevada, Reno (US)
- Corresponding author
- Herzog, Serge
- ORCID
- Herzog, Serge
- Venue
- New Directions for Institutional Research
- Publisher
- Wiley
- Published
- 2006-09
- Volume
- 2006
- Issue
- 131
- Pages
- 17-33
- ISSN
- 0271-0579 (ISSN-L)
- 1536-075X
- OpenAlex
- W2129273781
- Cited by
- 138
- References
- 19
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 4 · 2023: 5 · 2022: 8 · 2021: 11 · 2020: 11 · 2019: 9 · 2018: 10 · 2017: 3 · 2016: 11 · 2015: 9 · 2014: 12 · 2013: 8 · 2012: 7
- FWCI
- 3.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- New Directions for Institutional Research (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Higher Education Research Studies
- Social Sciences › Demography › Evasion and Academic Success Factors
- Keywords
- student retention, time to degree completion, predictive modeling, decision tree, artificial neural networks, regression analysis, data mining, institutional research
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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