# Strategizing University Rank Improvement using Interpretable Machine Learning and Data Visualization

arXiv: https://arxiv.org/abs/2110.09050 · HTML: https://mlsanity.com/paper/2110.09050/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 49 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [A comparison of university performance scores and ranks by MNCS and FSS](https://mlsanity.com/paper/1810.12661/) (2018)
- [A Longitudinal Analysis of University Rankings](https://mlsanity.com/paper/1908.10632/) (2019)

## Abstract (arXiv)

Annual ranking of higher educational institutions (HEIs) is a global phenomenon and have significant impact on higher education landscape. Most of the HEIs pay close attention to ranking results and look forward to improving their ranks. However, maintaining a good rank and ascending in the rankings is a difficult task because it requires considerable resources, efforts and performance improvement plan. In this work, firstly, we show how exploratory data analysis (EDA) using correlation heatmaps and box plots can aid in understanding the broad trends in the ranking data. Subsequently, we present a novel idea of classifying the rankings data using Decision Tree (DT) based algorithms and retrieve decision paths for rank improvement using data visualization techniques. Using Laplace correction to the probability estimate, we quantify the amount of certainty attached with different decision paths obtained from interpretable DT models. The proposed methodology can aid Universities and HEIs to quantitatively assess the scope of improvement, adumbrate a fine-grained long-term action plan and prepare a suitable road-map.