# Ranking-Based At-Risk Student Prediction Using Federated Learning and Differential Features

arXiv: https://arxiv.org/abs/2505.09287 · HTML: https://mlsanity.com/paper/2505.09287/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 53 references that are in the map.
- First posted on arXiv 14 May 2025.

## Builds on (most cited first, 20 of 20)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://mlsanity.com/paper/2102.07623/) (2021)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction](https://mlsanity.com/paper/2203.11751/) (2022)
- [SCAFFOLD: Stochastic Controlled Averaging for Federated Learning](https://mlsanity.com/paper/1910.06378/) (2019)
- [Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach](https://mlsanity.com/paper/2003.08725/) (2020)
- [Industry-Scale Orchestrated Federated Learning for Drug Discovery](https://mlsanity.com/paper/2210.08871/) (2022)
- [Towards Generalizable Agents in Text-Based Educational Environments: A Study of Integrating RL with LLMs](https://mlsanity.com/paper/2404.18978/) (2024)
- [Student Performance Prediction Using Dynamic Neural Models](https://mlsanity.com/paper/2106.00524/) (2021)
- [Towards Generalizable Detection of Urgency of Discussion Forum Posts](https://mlsanity.com/paper/2307.07614/) (2023)
- [Pairwise Difference Learning for Classification](https://mlsanity.com/paper/2406.20031/) (2024)
- [Detecting Unsuccessful Students in Cybersecurity Exercises in Two Different Learning Environments](https://mlsanity.com/paper/2408.08531/) (2024)
- [Generalisable Methods for Early Prediction in Interactive Simulations for Education](https://mlsanity.com/paper/2207.01457/) (2022)
- [A Comparative Analysis of Student Performance Predictions in Online Courses using Heterogeneous Knowledge Graphs](https://mlsanity.com/paper/2407.12153/) (2024)
- [E2Vec: Feature Embedding with Temporal Information for Analyzing Student Actions in E-Book Systems](https://mlsanity.com/paper/2407.13053/) (2024)
- [Knowledge Distillation in RNN-Attention Models for Early Prediction of Student Performance](https://mlsanity.com/paper/2412.14526/) (2024)

## Built on by (most cited first, 1 of 1)

- [Evaluating Federated Learning for At-Risk Student Prediction: A Comparative Analysis of Model Complexity and Data Balancing](https://mlsanity.com/paper/2508.18316/) (2025)

## Abstract (arXiv)

Digital textbooks are widely used in various educational contexts, such as university courses and online lectures. Such textbooks yield learning log data that have been used in numerous educational data mining (EDM) studies for student behavior analysis and performance prediction. However, these studies have faced challenges in integrating confidential data, such as academic records and learning logs, across schools due to privacy concerns. Consequently, analyses are often conducted with data limited to a single school, which makes developing high-performing and generalizable models difficult. This study proposes a method that combines federated learning and differential features to address these issues. Federated learning enables model training without centralizing data, thereby preserving student privacy. Differential features, which utilize relative values instead of absolute values, enhance model performance and generalizability. To evaluate the proposed method, a model for predicting at-risk students was trained using data from 1,136 students across 12 courses conducted over 4 years, and validated on hold-out test data from 5 other courses. Experimental results demonstrated that the proposed method addresses privacy concerns while achieving performance comparable to that of models trained via centralized learning in terms of Top-n precision, nDCG, and PR-AUC. Furthermore, using differential features improved prediction performance across all evaluation datasets compared to non-differential approaches. The trained models were also applicable for early prediction, achieving high performance in detecting at-risk students in earlier stages of the semester within the validation datasets.