
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
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)
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)
| 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% |
