# Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning

arXiv: https://arxiv.org/abs/2208.01182 · HTML: https://mlsanity.com/paper/2208.01182/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 2 Aug 2022.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Personalized Federated Learning: A Meta-Learning Approach](https://mlsanity.com/paper/2002.07948/) (2020)
- [Personalized Cross-Silo Federated Learning on Non-IID Data](https://mlsanity.com/paper/2007.03797/) (2020)
- [Improving Federated Learning Personalization via Model Agnostic Meta Learning](https://mlsanity.com/paper/1909.12488/) (2019)
- [Pay Less Attention with Lightweight and Dynamic Convolutions](https://mlsanity.com/paper/1901.10430/) (2019)
- [Dynamic Key-Value Memory Networks for Knowledge Tracing](https://mlsanity.com/paper/1611.08108/) (2016)
- [Beyond Parity: Fairness Objectives for Collaborative Filtering](https://mlsanity.com/paper/1705.08804/) (2017)
- [iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making](https://mlsanity.com/paper/1806.01059/) (2018)
- [Learning Private Neural Language Modeling with Attentive Aggregation](https://mlsanity.com/paper/1812.07108/) (2018)
- [Algorithmic Fairness in Education](https://mlsanity.com/paper/2007.05443/) (2020)
- [Are Pre-trained Convolutions Better than Pre-trained Transformers?](https://mlsanity.com/paper/2105.03322/) (2021)
- [Unbounded Human Learning: Optimal Scheduling for Spaced Repetition](https://mlsanity.com/paper/1602.07032/) (2016)
- [Stimuli-Sensitive Hawkes Processes for Personalized Student Procrastination Modeling](https://mlsanity.com/paper/2102.00089/) (2021)
- [Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach](https://mlsanity.com/paper/2111.00901/) (2021)

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

- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection](https://mlsanity.com/paper/2401.16685/) (2024)
- [Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics](https://mlsanity.com/paper/2212.02985/) (2022)
- [See, Think, Act: Online Shopper Behavior Simulation with VLM Agents](https://mlsanity.com/paper/2510.19245/) (2025)
- [FedMCSA: Personalized Federated Learning via Model Components Self-Attention](https://mlsanity.com/paper/2208.10731/) (2022)
- [Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization](https://mlsanity.com/paper/2305.13503/) (2023)
- [Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation](https://mlsanity.com/paper/2401.07456/) (2024)
- [Modeling and Analyzing Scorer Preferences in Short-Answer Math Questions](https://mlsanity.com/paper/2306.00791/) (2023)
- [Scalable Early Childhood Reading Performance Prediction](https://mlsanity.com/paper/2412.10401/) (2024)
- [Guarding Digital Privacy: Exploring User Profiling and Security Enhancements](https://mlsanity.com/paper/2504.07107/) (2025)
- [Evaluating Federated Learning for At-Risk Student Prediction: A Comparative Analysis of Model Complexity and Data Balancing](https://mlsanity.com/paper/2508.18316/) (2025)
- [Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges](https://mlsanity.com/paper/2509.07946/) (2025)
- [Privacy-Preserving Personalization in Education: A Federated Recommender System for Student Performance Prediction](https://mlsanity.com/paper/2509.10516/) (2025)

## Abstract (arXiv)

Traditional learning-based approaches to student modeling generalize poorly to underrepresented student groups due to biases in data availability. In this paper, we propose a methodology for predicting student performance from their online learning activities that optimizes inference accuracy over different demographic groups such as race and gender. Building upon recent foundations in federated learning, in our approach, personalized models for individual student subgroups are derived from a global model aggregated across all student models via meta-gradient updates that account for subgroup heterogeneity. To learn better representations of student activity, we augment our approach with a self-supervised behavioral pretraining methodology that leverages multiple modalities of student behavior (e.g., visits to lecture videos and participation on forums), and include a neural network attention mechanism in the model aggregation stage. Through experiments on three real-world datasets from online courses, we demonstrate that our approach obtains substantial improvements over existing student modeling baselines in predicting student learning outcomes for all subgroups. Visual analysis of the resulting student embeddings confirm that our personalization methodology indeed identifies different activity patterns within different subgroups, consistent with its stronger inference ability compared with the baselines.