# Towards Equity and Algorithmic Fairness in Student Grade Prediction

arXiv: https://arxiv.org/abs/2105.06604 · HTML: https://mlsanity.com/paper/2105.06604/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 46 references that are in the map.
- First posted on arXiv 14 May 2021.

## Builds on (most cited first, 17 of 17)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Learning Adversarially Fair and Transferable Representations](https://mlsanity.com/paper/1802.06309/) (2018)
- [Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems](https://mlsanity.com/paper/1805.04508/) (2018)
- [Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations](https://mlsanity.com/paper/1707.00075/) (2017)
- [Algorithmic Fairness](https://mlsanity.com/paper/2001.09784/) (2020)
- [Fairness in Deep Learning: A Computational Perspective](https://mlsanity.com/paper/1908.08843/) (2019)
- [Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction](https://mlsanity.com/paper/1807.00199/) (2018)
- [Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements](https://mlsanity.com/paper/1901.04562/) (2019)
- [On Formalizing Fairness in Prediction with Machine Learning](https://mlsanity.com/paper/1710.03184/) (2017)
- [FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learning](https://mlsanity.com/paper/2006.16742/) (2020)
- [Algorithmic Fairness in Education](https://mlsanity.com/paper/2007.05443/) (2020)
- [Algorithmic Fairness from a Non-ideal Perspective](https://mlsanity.com/paper/2001.09773/) (2020)
- [Goal-based Course Recommendation](https://mlsanity.com/paper/1812.10078/) (2018)
- [AI and Holistic Review: Informing Human Reading in College Admissions](https://mlsanity.com/paper/1912.09318/) (2019)
- [Connectionist Recommendation in the Wild: On the utility and scrutability of neural networks for personalized course guidance](https://mlsanity.com/paper/1803.09535/) (2018)

## Built on by (most cited first, 13 of 13)

- [Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data](https://mlsanity.com/paper/2210.13043/) (2022)
- [Is Your Model "MADD"? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Models](https://mlsanity.com/paper/2305.15342/) (2023)
- [Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools](https://mlsanity.com/paper/2309.17337/) (2023)
- [Single-Agent vs. Multi-Agent LLM Strategies for Automated Student Reflection Assessment](https://mlsanity.com/paper/2504.05716/) (2025)
- [Evaluation of group fairness measures in student performance prediction problems](https://mlsanity.com/paper/2208.10625/) (2022)
- [A Human-Centered Review of Algorithms in Decision-Making in Higher Education](https://mlsanity.com/paper/2302.05839/) (2023)
- [ABROCA Distributions For Algorithmic Bias Assessment: Considerations Around Interpretation](https://mlsanity.com/paper/2411.19090/) (2024)
- [Counterfactual Fairness Evaluation of Machine Learning Models on Educational Datasets](https://mlsanity.com/paper/2504.11504/) (2025)
- [Evaluating Algorithmic Bias in Models for Predicting Academic Performance of Filipino Students](https://mlsanity.com/paper/2405.09821/) (2024)
- [Toward Sufficient Statistical Power in Algorithmic Bias Assessment: A Test for ABROCA](https://mlsanity.com/paper/2501.04683/) (2025)
- [The Hardness of Achieving Impact in AI for Social Impact Research: A Ground-Level View of Challenges & Opportunities](https://mlsanity.com/paper/2506.14829/) (2025)
- [Towards Equalised Odds as Fairness Metric in Academic Performance Prediction](https://mlsanity.com/paper/2209.14670/) (2022)
- [Compounding Disadvantage: Auditing Intersectional Bias in LLM-Generated Explanations Across Indian and American STEM Education](https://mlsanity.com/paper/2601.14506/) (2026)

## Abstract (arXiv)

Equity of educational outcome and fairness of AI with respect to race have been topics of increasing importance in education. In this work, we address both with empirical evaluations of grade prediction in higher education, an important task to improve curriculum design, plan interventions for academic support, and offer course guidance to students. With fairness as the aim, we trial several strategies for both label and instance balancing to attempt to minimize differences in algorithm performance with respect to race. We find that an adversarial learning approach, combined with grade label balancing, achieved by far the fairest results. With equity of educational outcome as the aim, we trial strategies for boosting predictive performance on historically underserved groups and find success in sampling those groups in inverse proportion to their historic outcomes. With AI-infused technology supports increasingly prevalent on campuses, our methodologies fill a need for frameworks to consider performance trade-offs with respect to sensitive student attributes and allow institutions to instrument their AI resources in ways that are attentive to equity and fairness.