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Text mining applied to distance higher education: A systematic literature review

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Abstract

Much of the data produced and consumed by students, teachers, and educational managers is in textual format. Text Mining (TM) and Natural Language Processing (NLP) have been applied in the educational context in different ways. Ideally, such applications combine computational, linguistic, pedagogical, and psychological aspects. This article aims to gather and analyze scientific publications that have applied TM and NLP techniques in textual corpora from distance-higher education through a Systematic Literature Review. Eight scientific databases were searched (ACM DL, Scopus, Web of Science, IEEE Xplore, ArXiv, SpringerLink, ScienceDirect, and ERIC), and publications from 2017 to 2021 were selected. 718 unique publications were screened to identify primary research capable of characterizing this scientific area. 52 resulting publications were fully analyzed, and some consolidated results include: 38% of works had the professors as end users, followed by students (27%) and managers (25%); the English language was present in 50% of publications, followed by the Portuguese language (13,5%) and others languages; the text mining tasks most used were text classification (27%), sentiment analysis (17%), information extraction (15%), chatbot (15%) and topic modeling (13%); LDA (Latent Dirichlet Analysis) was the technique most used (19%); the Python language was the programming language most prevalent (42%), and 54% of works do not mention any educational construct or theory. Thus, this article presents an unprecedented overview of the field of Educational Text Mining (ETM) in distance higher education and analyses the main results obtained, aiming for future research in the area.

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Data availability

The datasets generated during the current study are available in the Google repository, https://docs.google.com/spreadsheets/d/160jzML7fSoI4JQs_GXaHNW9Srd7PJg2xsylsYDhm4jE/edit?usp=sharing

Notes

  1. The BERT (Bidirectional Encoder Representations from Transformer) model is a language representation model trained on 16 GB of unlabeled texts, including BookCorpus and Wikipedia, with a total of 3.3 billion words and a vocabulary of 30,522 words (DEVLIN et al., 2019).

  2. https://docs.google.com/spreadsheets/d/160jzML7fSoI4JQs_GXaHNW9Srd7PJg2xsylsYDhm4jE/edit?usp=sharing

  3. LIWC (Linguistic Inquiry Word Count) is a dictionary that allows the extraction of grammatical, psychological and social characteristics of a textual document.

  4. Coh-Metrix is a computational linguistics tool for extracting features associated with: text cohesion (overlapping arguments), linguistic complexity (based on syntactic tree structures), text readability (readability according to the Flesch index) and lexical diversity (token-like relationship.

  5. CoI (Community of Inquiry) Model by (Garrison et al., 2001) is the most used and validated social-constructivist model of distance learning, based on three dimensions known as “presences”: cognitive presence, social presence and teacher presence.

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This project has received funding from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES/MEC) and Fundação de Amparo à Pesquisa de Minas Gerais (FAPEMIG).

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This article presents an unprecedented overview of the field of Educational Text Mining (ETM) in distance higher education. The answers to the eight listed research questions constitute important contributions that allow a better understanding of the state of the art of text mining applied to distance higher education. The results indicate the strengths that can be reproduced and/or enhanced by the scientific community and even embryonic issues that need greater theoretical and/or practical contributions, both for future research. These contributions are an invitation to scientific research for future work in this area.

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Takaki, P., Dutra, M.L. Text mining applied to distance higher education: A systematic literature review. Educ Inf Technol 29, 10851–10878 (2024). https://doi.org/10.1007/s10639-023-12235-0

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