# Educational data mining and learning analytics: An updated survey

arXiv: https://arxiv.org/abs/2402.07956 · HTML: https://mlsanity.com/paper/2402.07956/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 76 references that are in the map.
- First posted on arXiv 10 Feb 2024.

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

- [Transfer Learning using Representation Learning in Massive Open Online Courses](https://mlsanity.com/paper/1812.05043/) (2018)

## Built on by (most cited first, 17 of 17)

- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)
- [Time Series Analysis for Education: Methods, Applications, and Future Directions](https://mlsanity.com/paper/2408.13960/) (2024)
- [Multi-stakeholder Perspective on Responsible Artificial Intelligence and Acceptability in Education](https://mlsanity.com/paper/2402.15027/) (2024)
- [An overview of artificial intelligence in computer-assisted language learning](https://mlsanity.com/paper/2505.02032/) (2025)
- [Towards an Operational Responsible AI Framework for Learning Analytics in Higher Education](https://mlsanity.com/paper/2410.05827/) (2024)
- [Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests](https://mlsanity.com/paper/2506.05990/) (2025)
- [A Fair Post-Processing Method based on the MADD Metric for Predictive Student Models](https://mlsanity.com/paper/2407.05398/) (2024)
- [Improving the portability of predicting students performance models by using ontologies](https://mlsanity.com/paper/2410.07358/) (2024)
- [The Course Difficulty Analysis Cookbook](https://mlsanity.com/paper/2508.13218/) (2025)
- [AI-Driven Personalized Learning: Predicting Academic Per-formance Through Leadership Personality Traits](https://mlsanity.com/paper/2510.19964/) (2025)
- [Multi-Method Analysis of Mathematics Placement Assessments: Classical, Machine Learning, and Clustering Approaches](https://mlsanity.com/paper/2511.04667/) (2025)
- [Open Datasets in Learning Analytics: Trends, Challenges, and Best PRACTICE](https://mlsanity.com/paper/2602.17314/) (2026)
- [EDM-ARS: A Domain-Specific Multi-Agent System for Automated Educational Data Mining Research](https://mlsanity.com/paper/2603.18273/) (2026)
- [MBP-KT: Learning Global Collaborative Information from Meta-Behavioral Pattern for Enhanced Knowledge Tracing](https://mlsanity.com/paper/2605.08697/) (2026)
- [Stable and Faithful Explanations for Knowledge Tracing](https://mlsanity.com/paper/2609.28502/) (2026)
- [Limited Structural Reliability in Public Educational Prediction Benchmarks: A Four-Dimension Audit of Seven Datasets](https://mlsanity.com/paper/2609.29625/) (2026)
- [A Time-Synchronous Prequential Evaluation Framework for Next-Week Student Disengagement Prediction from Moodle LMS Logs](https://mlsanity.com/paper/2610.17490/) (2026)

## Abstract (arXiv)

This survey is an updated and improved version of the previous one published in 2013 in this journal with the title data mining in education. It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have been applied over educational data. In the last decade, this research area has evolved enormously and a wide range of related terms are now used in the bibliography such as Academic Analytics, Institutional Analytics, Teaching Analytics, Data-Driven Education, Data-Driven Decision-Making in Education, Big Data in Education, and Educational Data Science. This paper provides the current state of the art by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, the main objectives, and the future trends in this research area.