Text mining in education
Open accessAbstract
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. This article is categorized under: Application Areas > Education and Learning Ensemble Methods > Text Mining
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Text mining is being increasingly used in research in education, as it helps provide new insights by analyzing huge quantities of information.
“Text mining is being increasingly used in research in education (Ferreira-Mello et al., 2019), as it helps provide new insights by analyzing huge quantities of information.”
From Key factors in digital literacy in learning and education: a systematic literature review using text mining (2022) · cites this paper as Ferreira-Mello et al., 2019
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- Authors
- Ferreira‐Mello, R., André, M., Pinheiro, A., Costa, E., & Romero, C.
- Affiliations
- Department of Computing Federal Rural University of Pernambuco Recife Brazil
- Universidade Federal Rural de Pernambuco (BR)
- Universidade Federal de Alagoas (BR)
- Department of Computer Sciences University of Cordoba Cordoba Spain
- Venue
- WIREs Data Mining and Knowledge Discovery
- Publisher
- Wiley
- Published
- 2019-08-04
- Volume
- 9
- Issue
- 6
- ISSN
- 1942-4787 (ISSN-L)
- 1942-4795
- OpenAlex
- W2965905350
- Semantic Scholar
- b56e2b97e97f9c2e90d9a990e24cfa23eb2fe8a8
- Cited by
- 193
- References
- 330
- Citations by year
- 2026: 14 · 2025: 17 · 2024: 45 · 2023: 47 · 2022: 37 · 2021: 23 · 2020: 10
- FWCI
- 18.98
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Universidad de Córdoba Insitutional Repository (Universidad de Córdoba) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- Ministerio de Ciencia y Tecnología (TIN2017‐83445‐P)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- virtual learning environments, educational data mining, student essays, educational software
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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