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.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10639-023-12235-0/MediaObjects/10639_2023_12235_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10639-023-12235-0/MediaObjects/10639_2023_12235_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10639-023-12235-0/MediaObjects/10639_2023_12235_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10639-023-12235-0/MediaObjects/10639_2023_12235_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10639-023-12235-0/MediaObjects/10639_2023_12235_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10639-023-12235-0/MediaObjects/10639_2023_12235_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10639-023-12235-0/MediaObjects/10639_2023_12235_Fig7_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10639-023-12235-0/MediaObjects/10639_2023_12235_Fig8_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10639-023-12235-0/MediaObjects/10639_2023_12235_Fig9_HTML.png)
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.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
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).
LIWC (Linguistic Inquiry Word Count) is a dictionary that allows the extraction of grammatical, psychological and social characteristics of a textual document.
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.
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.
References
Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13–49. https://doi.org/10.1016/j.tele.2019.01.007
Allen, L. K., Likens, A. D., & McNamara, D. S. (2018). A multi-dimensional analysis of writing flexibility in an automated writing evaluation system. Proceedings of the 8th International Conference on Learning Analytics and Knowledge, 380–388. https://doi.org/10.1145/3170358.3170404
Allen, L. K., & Mcnamara, D. S. (2015). You are Your Words: Modeling Students’ Vocabulary Knowledge with Natural Language Processing Tools. In O. C. Santos, J. G. Boticario, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J. M. Luna, C. Mihaescu, P. Moreno, A. Hershkovitz, S. Ventura, & M. Desmarais (Eds.), Proceedings of the 8th international conference on educational data mining (pp. 258–265). https://eric.ed.gov/?id=ED560539
Angeli, C., Howard, S. K., Ma, J., Yang, J., & Kirschner, P. A. (2017). Data mining in educational technology classroom research: Can it make a contribution? Computers and Education, 113, 226–242. https://doi.org/10.1016/J.COMPEDU.2017.05.021
Bahel, V., & Thomas, A. (2021). Text similarity analysis for evaluation of descriptive answers. ArXiv, 1–7. https://arxiv.org/abs/2105.02935
Baig, M. I., Shuib, L., & Yadegaridehkordi, E. (2020). Big data in education: A state of the art, limitations, and future research directions. International Journal of Educational Technology in Higher Education, 17(44), 1–23. https://doi.org/10.1186/s41239-020-00223-0
Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In Learning Analytics: From Research to Practice (pp. 61–75). Springer New York. https://doi.org/10.1007/978-1-4614-3305-7_4
Barbosa, A., Ferreira, M., Ferreira Mello, R., Dueire Lins, R., & Gasevic, D. (2021). The impact of automatic text translation on classification of online discussions for social and cognitive presences. LAK21: 11th International Learning Analytics and Knowledge Conference, 77–87. https://doi.org/10.1145/3448139.3448147
Barbosa, G., Camelo, R., Cavalcanti, A. P., Miranda, P., Mello, R. F., Kovanović, V., & Gašević, D. (2020).Towards automatic cross-language classification of cognitive presence in online discussions. Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, 605–614. https://doi.org/10.1145/3375462.3375496
Behzadi, F. (2015). Natural Language Processing and Machine Learning: A Review. (IJCSIS) International Journal of Computer Science and Information Security, 13(9), 101–106. https://sites.google.com/site/ijcsis/
Bittencourt, I. I., Chalco, G., Santos, J., Fernandes, S., Silva, J., Batista, N., Hutz, C., & Isotani, S. (2023). Positive artificial intelligence in education (P-AIED): A roadmap. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-023-00357-y
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
Cavalcanti, A. P., Barbosa, A., Carvalho, R., Freitas, F., Tsai, Y. S., Gašević, D., & Mello, R. F. (2021). Automatic feedback in online learning environments: A systematic literature review. In Computers and Education: Artificial Intelligence (Vol. 2). Elsevier B.V. https://doi.org/10.1016/j.caeai.2021.100027
Cavalcanti, A. P., Diego, A., Mello, R. F., Mangaroska, K., Nascimento, A., Freitas, F., & Gašević, D. (2020). How good is my feedback? Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, 428–437. https://doi.org/10.1145/3375462.3375477
Cavalcanti, A. P., Rolim, V., Andre, M., Ferreira Leite de Mello, R., Freitas, F., Ferreira, R., & Gasevic, D. (2019). An Analysis of the use of Good Feedback Practices in Online Learning Courses. 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), 153–157. https://doi.org/10.1109/ICALT.2019.00061
Crossley, S. A., Kim, M., Allen, L., & McNamara, D. (2019). Automated summarization evaluation (ASE) using natural language processing tools. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11625 LNAI, 84–95. https://doi.org/10.1007/978-3-030-23204-7_8
Daniel, B. (2014). Big data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230
Daniel, B. (2017). Big Data in Higher Education: The Big Picture. In Big Data and Learning Analytics in Higher Education (pp. 19–28). Springer International Publishing. https://doi.org/10.1007/978-3-319-06520-5_3
Devlin, J., Chang, M.-W., Lee, K., Google, K. T., & Language, A. I. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Conference of the north american chapter of the association for computational linguistics: human language technologies, 4171–4186.
Ecker, R. (2015). Multiple-views analysis of computer-mediated discourses. 17th International Conference on Information Integration and Web-Based Applications and Services, IiWAS 2015 - Proceedings. https://doi.org/10.1145/2837185.2837221
Feldman, R., & Sanger, J. (2007). The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge University Press. https://dl.icdst.org/pdfs/files/25a6d982ee80e1db7a4ebf7eeca4e0ec.pdf
Ferreira‐Mello, R., André, M., Pinheiro, A., Costa, E., & Romero, C. (2019). Text mining in education. WIREs Data Mining and Knowledge Discovery, 9(6). https://doi.org/10.1002/widm.1332
Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., Slater, S., Baker, R., & Warschauer, M. (2020). Mining big data in education: Affordances and challenges. Review of Research in Education, 44(1), 130–160. https://doi.org/10.3102/0091732X20903304
Ganesh, A., Palmer, M., & Kann, K. (2021). What would a teacher Do? Predicting future talk moves. ArXiv. https://arxiv.org/abs/2106.05249
Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence, and computer conferencing in distance education. American Journal of Distance Education, 15(1), 7–23. https://doi.org/10.1080/08923640109527071
Haydt, R. C. (2022). Avaliação do processo ensino-aprendizagem (6th ed.). Ática
Head, A., Glassman, E., Soares, G., Suzuki, R., Figueredo, L., D’Antoni, L., & Hartmann, B. (2017). Writing Reusable Code Feedback at Scale with Mixed-Initiative Program Synthesis. Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale, 89–98. https://doi.org/10.1145/3051457.3051467
Huang, E., Valdiviejas, H., & Bosch, N. (2019). I’m Sure! Automatic Detection of Metacognition in Online Course Discussion Forums. 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII), 1–7. https://doi.org/10.1109/ACII.2019.8925506
Ignatow, G., & Mihalcea, R. (2017). An Introduction to text mining: research design, data collection, and analysis. SAGE Publications.
Júnior, E. M. da S., Silva, H. A. M. da, & Takaki, P. (2021). A dimensão técnica da Competência em Informação na perspectiva da Mineração de Dados. In E. V. Vitorino (Ed.), Competência em Informação no Brasil: dimensão técnica e perspectivas interdisciplinares. Paco Editorial
Khan, M., Khan, S. S., & Alharbi, Y. (2020). Text mining challenges and applications, a comprehensive review. International Journal of Computer Science and Network Security, 20(12), 138–148. https://doi.org/10.22937/IJCSNS.2020.20.12.15
Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering, technical report EBSE 2007-001. Keele University and Durham University Joint Report.
Korde, V. (2012). Text classification and classifiers: A survey. International Journal of Artificial Intelligence & Applications, 3(2), 85–99. https://doi.org/10.5121/ijaia.2012.3208
Kovanović, V., Joksimović, S., Mirriahi, N., Blaine, E., Gašević, D., Siemens, G., & Dawson, S. (2018). Understand students’ self-reflections through learning analytics. Proceedings of the 8th International Conference on Learning Analytics and Knowledge, 389–398. https://doi.org/10.1145/3170358.3170374
Kukul, V. (2021). On becoming an online university in an emergency period: Voices From the students at a state university. Open Praxis, 13(2), 172–183. https://doi.org/10.5944/openpraxis.13.2.127
Lan, A. S., Vats, D., Waters, A. E., & Baraniuk, R. G. (2015). Mathematical Language Processing. Proceedings of the Second (2015) ACM Conference on Learning @ Scale, 167–176. https://doi.org/10.1145/2724660.2724664
Lee, J.-E., & Recker, M. (2021). The effects of instructors’ use of online discussions strategies on student participation and performance in university online introductory mathematics courses. Computers & Education, 162, 104084. https://doi.org/10.1016/j.compedu.2020.104084
Litman, D. (2016). Natural language processing for enhancing teaching and learning. Proceedings of the thirtieth AAAI conference on artificial intelligence (AAAI-16), 4170–4176. https://ojs.aaai.org/index.php/AAAI/article/view/9879
Liu, J.-W., & Sangaiah, A. K. (2021). Research on adaptive updating method of education resource index based on mobile computing. Mobile Networks and Applications, 26, 2153–2162. https://doi.org/10.1007/s11036-021-01771-z/Published
Machado, C. J. R., Maciel, A. M. A., Rodrigues, R. L., & Menezes, R. (2019). An approach for thematic relevance analysis applied to textual contributions in discussion forums. International Journal of Distance Education Technologies, 17(3), 37–51. https://doi.org/10.4018/IJDET.2019070103
Machado, C., Maciel, A., Rodrigues, R., & Menezes, R. (2018). Análise de Relevância Temática de Postagens em Fóruns de Discussão em Relação ao uso de Vídeos como Recurso Didático. Anais Do XXIX Simpósio Brasileiro de Informática Na Educação (SBIE 2018), 1, 1523. https://doi.org/10.5753/cbie.sbie.2018.1523
Mathimagal, N., & Jayalakshmi, S. (2021). Intellectual behaviour of student based on education data determined by opinion mining. Proceedings of the 2021 8th International Conference on Computing for Sustainable Global Development, INDIACom 2021, 559–564. https://doi.org/10.1109/INDIACom51348.2021.00099
Moore, M. G. (2008). Teoria da distância transacional. Revista Brasileira de Aprendizagem Aberta e a Distância, 1, 1–14. https://doi.org/10.17143/rbaad.v1i0.111
Moreira, L. B., Tamariz, A. D. R., & Fettermann, J. V. (2018). O uso da mineração de textos no suporte a correções de questões discursivas em uma instituição de educação superior. Texto Livre: Linguagem e Tecnologia, 11(3), 213–227. https://doi.org/10.17851/1983-3652.11.3.213-227
Oliveira, J. da S., Espindola, D. B., Barwaldt, R., Ribeiro, L. M., & Pias, M. (2019). IBM Watson Application as FAQ Assistant about Moodle. 2019 IEEE Frontiers in Education Conference (FIE), 1–8. https://doi.org/10.1109/FIE43999.2019.9028667
Osakwe, I., Chen, G., Whitelock-Wainwright, A., Gašević, D., Pinheiro Cavalcanti, A., & Ferreira Mello, R. (2022). Towards automated content analysis of educational feedback: A multi-language study. Computers and Education: Artificial Intelligence, 3, 100059. https://doi.org/10.1016/j.caeai.2022.100059
Özbey, M., & Kayri, M. (2023). Investigation of factors affecting transactional distance in E-learning environment with artificial neural networks. Education and Information Technologies, 28(4), 4399–4427. https://doi.org/10.1007/s10639-022-11346-4
Ozturk, Z. K., Erzurum Cicek, Z. I., & Ergul, Z. (2017). Sentiment analysis: An application to Anadolu University. Acta Physica Polonica A, 132(3), 753–755. https://doi.org/10.12693/APhysPolA.132.753
Piaget, J. (1976). Piaget’s Theory. In Piaget and His School (pp. 11–23). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-46323-5_2
Rabelo, A., Rodrigues, M. W., Nobre, C., Isotani, S., & Zárate, L. (2023). Educational data mining and learning analytics: a review of educational management in e-learning. Information Discovery and Delivery, ahead-of-print(ahead-of-print). https://doi.org/10.1108/IDD-10-2022-0099
Rahman, M. M., & Watanobe, Y. (2023). ChatGPT for Education and Research: Opportunities, Threats, and Strategies. Applied Sciences (Switzerland), 13(9). https://doi.org/10.3390/app13095783
Ramesh, D., & Sanampudi, S. K. (2022). An automated essay scoring systems: A systematic literature review. Artificial Intelligence Review, 55(3), 2495–2527. https://doi.org/10.1007/s10462-021-10068-2
Ray, S., & Saeed, M. (2018). Applications of educational data mining and learning analytics tools in handling big data in higher education. In Applications of Big Data Analytics (pp. 135–160). Springer International Publishing. https://doi.org/10.1007/978-3-319-76472-6_7
Rocha, M. A. da, Nóbrega, G. Â. S. da, de Medeiros Valentim, R. A., & Alves, L. P. C. F. (2020). A text as unique as fingerprint: AVASUS Text Analysis and Authorship Recognition. Proceedings of the 10th Euro-American Conference on Telematics and Information Systems, 1–8. https://doi.org/10.1145/3401895.3401935
Rodrigues, M. W., Isotani, S., & Zárate, L. E. (2018). Educational data mining: A review of evaluation process in the e-learning. Telematics and Informatics, 35(6), 1701–1717. https://doi.org/10.1016/j.tele.2018.04.015
Rolim, V., Ferreira Leite de Mello, R., Kovanovic, V., & Gasevic, D. (2019). Analysing social presence in online discussions through network and text analytics. 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), 163–167. https://doi.org/10.1109/ICALT.2019.00058
Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. WIREs Data Mining and Knowledge Discovery, 10(3). https://doi.org/10.1002/widm.1355
Salas-Pilco, S. Z., & Yang, Y. (2022). Artificial intelligence applications in Latin American higher education: A systematic review. International Journal of Educational Technology in Higher Education, 19(1), 21. https://doi.org/10.1186/s41239-022-00326-w
Salloum, S. A., Al-Emran, M., Monem, A. A., & Shaalan, K. (2018). Using Text Mining Techniques for Extracting Information from Research Articles. In Studies in Computational Intelligence (pp. 373–397). Springer International Publishing. https://doi.org/10.1007/978-3-319-67056-0_18
Schubotz, M., Krämer, L., Meuschke, N., Hamborg, F., & Gipp, B. (2017). Evaluating and improving the extraction of mathematical identifier definitions. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10456 LNCS, 82–94. https://doi.org/10.1007/978-3-319-65813-1_7
Shen, J. T., Yamashita, M., Prihar, E., Heffernan, N., Wu, X., Graff, B., & Lee, D. (2021).MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education. https://arxiv.org/abs/2106.07340
Somers, R., Cunningham-Nelson, S., & Boles, W. (2021). Applying natural language processing to automatically assess student conceptual understanding from textual responses. Australasian Journal of Educational Technology, 37(5), 98–115. https://doi.org/10.14742/ajet.7121
Takaki, P., & Dutra, M. (2022). Data science in education: interdisciplinary contributions. In T. M. R. Dias (Ed.), Advanced Notes in Information Science (Vol. 2, pp. 149–160). ColNes Publishing. https://doi.org/10.47909/anis.978-9916-9760-3-6.94
Takaki, P., Dutra, M. L., de Araújo, G., & Júnior, E. M. S. (2022a). A proposed framework for evaluating the academic-failure prediction in distance learning. Mobile Networks and Applications, 27(5), 1958–1966. https://doi.org/10.1007/s11036-022-01965-z
Takaki, P., Dutra, M. L., & Matias, M. (2022b). Mineração de dados educacionais no âmbito da Ciência da Informação: Conexões epistemológicas. In C. Karpinski, E. Mintegui, J. M. C. Silva, & K. R. Vieira (Eds.), Epistemologias em trânsito na Ciência da Informação - perspectivas e possibilidades (pp. 197–222). Editora Fi.
Tang, C. L., Liao, J., Wang, H. C., Sung, C. Y., & Lin, W. C. (2021). ConceptGuide: Supporting online video learning with concept map-based recommendation of learning path. The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021, 2757–2768. https://doi.org/10.1145/3442381.3449808
UNESCO. (2019). Artificial intelligence in education: challenges and opportunities for sustainable development. https://unesdoc.unesco.org/ark:/48223/pf0000366994
UNESCO. (2021). International Forum on AI and the Futures of Education - Developing Competencies for the AI Era. https://unesdoc.unesco.org/ark:/48223/pf0000377251
UNESCO. (2023). Relatório de monitoramento global da educação, resumo, 2023: A tecnologia na educação: Uma ferramenta a serviço de quem? GEM Report UNESCO. https://doi.org/10.54676/CUYC7902
Vachkova, S., Kupriyanov, R., Suleymanov, R., & Petryaeva, E. (2021). The application of text mining algorithms to discover one topic objects in digital learning repositories. Conference of Open Innovation Association, FRUCT, 2021-January. https://doi.org/10.23919/FRUCT50888.2021.9347611
Vázquez-Cano, E., Mengual-Andrés, S., & López-Meneses, E. (2021). Chatbot to improve learning punctuation in Spanish and to enhance open and flexible learning environments. International Journal of Educational Technology in Higher Education, 18(33), 1–20. https://doi.org/10.1186/s41239-021-00269-8
Vygotsky, L. S. (2003). A formação social da mente: o desenvolvimento dos processos psicológicos superiores. Martins Fontes.
Wambsganss, T., Guggisberg, S., & Soellner, M. (2021). ArgueBot: A conversational agent for adaptive argumentation feedback. Wirtschaftsinformatik 2021 Proceedings 2 Track 11, 11. https://aisel.aisnet.org/wi2021/PHuman/Track11/2
Winkler, R., Hobert, S., Salovaara, A., Söllner, M., & Leimeister, J. M. (2020). Sara, the Lecturer: Improving Learning in Online Education with a Scaffolding-Based Conversational Agent. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–14. https://doi.org/10.1145/3313831.3376781
Yin, Y., Liu, Q., Huang, Z., Chen, E., Tong, W., Wang, S., & Su, Y. (2019). QuesNet: A unified representation for heterogeneous test questions. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1328–1336. https://doi.org/10.1145/3292500.3330900
Funding
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).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare that they have no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
• The authors declare that the content of the manuscript has not been published or submitted for publication elsewhere.
• The authors declare that they agree to the terms of the SpringerOpen Copyright and License Agreement.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-023-12235-0