# Text mining in education

arXiv: https://arxiv.org/abs/2403.00769 · HTML: https://mlsanity.com/paper/2403.00769/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 386 references that are in the map.
- First posted on arXiv 11 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [Experiments with Universal CEFR Classification](https://mlsanity.com/paper/1804.06636/) (2018)
- [Automated assessment of non-native learner essays: Investigating the role of linguistic features](https://mlsanity.com/paper/1612.00729/) (2016)
- [Co-Attention Based Neural Network for Source-Dependent Essay Scoring](https://mlsanity.com/paper/1908.01993/) (2019)
- [UnibucKernel: A kernel-based learning method for complex word identification](https://mlsanity.com/paper/1803.07602/) (2018)
- [Learning about social learning in MOOCs: From statistical analysis to generative model](https://mlsanity.com/paper/1312.2159/) (2013)
- [A Portuguese Native Language Identification Dataset](https://mlsanity.com/paper/1804.11346/) (2018)
- [Second Language Acquisition Modeling: An Ensemble Approach](https://mlsanity.com/paper/1806.04525/) (2018)
- [Annotating Student Talk in Text-based Classroom Discussions](https://mlsanity.com/paper/1909.03023/) (2019)
- [Expediting Support for Social Learning with Behavior Modeling](https://mlsanity.com/paper/1605.02836/) (2016)
- [An Improved Phrase-based Approach to Annotating and Summarizing Student Course Responses](https://mlsanity.com/paper/1805.10396/) (2018)

## Built on by (most cited first, 4 of 4)

- [Evaluating NLP Embedding Models for Handling Science-Specific Symbolic Expressions in Student Texts](https://mlsanity.com/paper/2505.17950/) (2025)
- [Good Intentions Beyond ACL: Who Does NLP for Social Good, and Where?](https://mlsanity.com/paper/2510.04434/) (2025)
- [Enhancing Essay Cohesion Assessment: A Novel Item Response Theory Approach](https://mlsanity.com/paper/2507.08487/) (2025)
- [Automatic Reflection Level Classification in Hungarian Student Essays](https://mlsanity.com/paper/2605.02402/) (2026)

## Abstract (arXiv)

The explosive growth of online education environments is generating a massive volume of data, specially in text format from forums, chats, social networks, assessments, essays, among others. It produces exciting challenges on how to mine text data in order to find useful knowledge for educational stakeholders. Despite the increasing number of educational applications of text mining published recently, we have not found any paper surveying them. In this line, this work presents a systematic overview of the current status of the Educational Text Mining field. Our final goal is to answer three main research questions: Which are the text mining techniques most used in educational environments? Which are the most used educational resources? And which are the main applications or educational goals? Finally, we outline the conclusions and the more interesting future trends.