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Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
Article . 2019 . Peer-reviewed
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https://dx.doi.org/10.48550/ar...
Article . 2024
License: arXiv Non-Exclusive Distribution
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Preprint . 2024
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DBLP
Article . 2019
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Text mining in education

Authors: Rafael Ferreira Leite de Mello; Maverick Andre Dionisio Ferreira; Anderson Pinheiro Cavalcanti; Evandro Costa; Cristóbal Romero 0001;

Text mining in education

Abstract

AbstractThe 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

Country
Spain
Keywords

FOS: Computer and information sciences, Computer Science - Machine Learning, Text analytics, Writing analytics, Computer Science - Information Retrieval, Machine Learning (cs.LG), Natural language processing in education, Computer Science - Computers and Society, Educational text mining, Computers and Society (cs.CY), Information Retrieval (cs.IR)

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    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    129
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
129
Top 1%
Top 10%
Top 1%
Green