# Predicting Higher Education Throughput in South Africa Using a Tree-Based Ensemble Technique

arXiv: https://arxiv.org/abs/2106.06805 · HTML: https://mlsanity.com/paper/2106.06805/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 35 references that are in the map.
- First posted on arXiv 12 Jun 2021.

## Builds on (most cited first, 1 of 1)

- [Stem-ming the Tide: Predicting STEM attrition using student transcript data](https://mlsanity.com/paper/1708.09344/) (2017)

## Abstract (arXiv)

We use gradient boosting machines and logistic regression to predict academic throughput at a South African university. The results highlight the significant influence of socio-economic factors and field of study as predictors of throughput. We further find that socio-economic factors become less of a predictor relative to the field of study as the time to completion increases. We provide recommendations on interventions to counteract the identified effects, which include academic, psychosocial and financial support.