What do we want to know about MOOCs? Results from a machine learning approach to a systematic literature mapping review

  1. Ignacio Despujol
  2. Linda Castañeda
  3. Victoria I. Marín
  4. Carlos Turró
Revista:
International Journal of Educational Technology in Higher Education

ISSN: 2365-9440

Año de publicación: 2022

Número: 19

Tipo: Artículo

DOI: 10.1186/S41239-022-00359-1 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: International Journal of Educational Technology in Higher Education

Resumen

By the end of 2020, over 16,300 Massive Open Online Courses (MOOCs) from 950 universities worldwide had enrolled over 180 million students. Interest in MOOCs has been matched by signifcant research on the topic, including a considerable number of reviews. This study uses Machine Learning techniques and human expert supervision to generate a comprehensive systematic literature mapping review that overcomes some limitations of the traditional ones and provides a broader overview of the content and main topics studied in the specialized literature devoted to MOOCs. The sample consisted of 6320 publications automatically classifed within six research topics, denominated by human experts: institutional approach, pedagogical approach, evaluation, analytics, participation, and educational resources. The content analysis of the topics identifed was conducted using visual network analysis, which supported the identifcation of diferent thematic sub-clusters and endorsed the classifcation. Results from the review show that the lowest production of MOOC papers is within the topics of the pedagogical approach and educational resources. In contrast, participation and evaluation are the most frequent ones. In addition, the most cited papers are on the topics of analytics and resources, being the pedagogical approach and the institutional approach the less cited. This highlights the need for more MOOC research from a pedagogical perspective and calls upon the presence of educators.

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Referencias bibliográficas

  • Ahmed, M. B. U., Podder, A. A., Chowdhury, M. S., & Al Mumin, M. A. (2021). A systematic literature review on English and Bangla Topic Modeling. Journal of Computer Science, 17(1), 1–18.
  • Archambault, É., Campbell, D., Gingras, Y., & Larivière, V. (2009). Comparing bibliometric statistics obtained from the Web of Science and Scopus. Journal of the American Society for Information Science and Technology, 60(7), 1320–1326.
  • Babori, A. (2020). Trends in MOOCs Research: Analysis of Educational Technology Journals. International Journal of Emerging Technologies in Learning (iJET). 15. 47.
  • Bartolomé, A., Castañeda, L., & Adell, J. (2018). Personalisation in educational technology: The absence of underlying pedagogies. International Journal of Educational Technology in Higher Education, 15(1), 1–17.
  • Bond, M., Bedenlier, S., Marín, V. I., & Händel, M. (2021). Emergency remote teaching in higher education: Mapping the first global online semester. International Journal of Educational Technology in Higher Education, 18(1), 50.
  • Bozkurt, A., Akgün-Özbek, E., & Zawacki-Richter, O. (2017). Trends and patterns in massive open online courses: Review and content analysis of research on MOOCs. The International Review of Research in Open and Distributed Learning, 18(5), 119–147.
  • Bozkurt, A., Keskin, N. O., & de Waard, I. (2016). Research trends in massive open online course (MOOC) theses and dissertations: Surfing the tsunami wave. Open Praxis, 8(3), 203–221.
  • Danilov, G. V., Shifrin, M. A., Kotik, K. V., Ishankulov, T. A., Orlov, Yu. N., Kulikov, A. S., & Potapov, A. A. (2020). Artificial intelligence in neurosurgery: a systematic review using topic modelling. Part I: major research areas. Sovremennye Tehnologii v Medicine, 12(5), 106.
  • Davies, P. (2000). The relevance of systematic reviews to educational policy and practice. Oxford Review of Education, 26(3–4), 365–378.
  • Decuypere, M. (2019). Visual network analysis: a qualitative method for researching sociomaterial practice. Qualitative Research, 1–18.
  • Deng, R., & Benckendorff, P. (2017). A contemporary review of research methods adopted to understand students’ and instructors’ use of massive open online courses (MOOCs). International Journal of Information and Education Technology, 7(8), 601–607.
  • Ebben, M., & Murphy, J. S. (2014). Unpacking MOOC scholarly discourse: A review of nascent MOOC scholarship. Learning, Media and Technology, 39(3), 328–345.
  • Van Eck N, & Waltman L. (2020) VOSviewer Manual. Vosviewer.
  • Flick, U. (2017). The SAGE Handbook of Qualitative Data Collection. USA: SAGE Publications. Gartner. (2018). Understanding Gartner’s Hype Cycles. Gartner. Retrieved September 25, 2021.
  • Gough, D., Oliver, S., & Thomas, J. (2017). An introduction to systematic reviews, (2nd ed.). SAGE.
  • Gusenbauer, M., & Haddaway, N. R. (2020). Which academic search systems are suitable for systematic reviews or metaanalyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Research Synthesis Methods, 11(2), 181–217I
  • Hew, K. F., & Cheung, W. S. (2014). Students’ and instructors’ use of massive open online courses (MOOCs): Motivations and challenges. Educational Research Review, 12, 45.
  • Hollands, F.M., & Tirthali, D. (2014). MOOCs: Expectations and reality. Full Report. New York: Teachers College, Columbia University.
  • Kennedy, J. (2014). Characteristics of Massive Open Online Courses (MOOCs): A research review, 2009–2012. Journal of Interactive Online Learning, 13(1), 1–16. http:// www. ncolr. org/ jiol/ issues/ pdf/ 13.1. 1. pdf
  • Kimmons, R., & Rosenberg, J.M. (2022). Trends and Topics in Educational Technology, 2022 Edition. TechTrends 66, 134–140.
  • Liyanagunawardena, T. R., Adams, A. A., & Williams, S. A. (2013). MOOCs: A systematic study of the published literature 2008–2012. The International Review of Research in Open and Distributed Learning, 4(3), 202–227.
  • Mayo, M. (2018). The Machine Learning Project Checklist. https:// www. kdnug gets. com/ 2018/ 12/ machi ne- learn ingproje ctcheck list. html
  • Newman M., & Gough D. (2019) Systematic Reviews in Educational Research: Methodology, Perspectives and Application. In Zawacki-Richter, O., Kerres, M., Bedenlier, S., Bond, M., & Buntins, K. (eds), Systematic Reviews in Educational Research. Springer VS.
  • Payne, G., & Payne, J. (2004). Key Concepts in Social Research. USA: SAGE Publications. Raffaghelli, J. E., Cucchiara, S., & Persico, D. (2015). Methodological approaches in MOOC research: Retracing the myth of Proteus. British Journal of Educational Technology, 46(3), 488–509.
  • Rafaghelli, J. E., Cucchiara, S., & Persico, D. (2015). Methodological approaches in MOOC research: Retracing the myth of Proteus. British Journal of Educational Technology, 46(3), 488–509
  • Rasheed, R. A., Kamsin, A., Abdullah, N. A., Zakari, A., & Haruna, K. (2019). A Systematic Mapping Study of the Empirical MOOC Literature. In IEEE Access, 7, 124809–124827
  • Röder, M., Both, A., & Hinneburg, A. (2015). Exploring the space of topic coherence measures. In Proceedings of the eighth ACM international conference on Web search and data mining (pp. 399–408).
  • Rodriguez, C. O. (2012). MOOCs and the AI-Stanford Like Courses: Two Successful and Distinct Course Formats for Massive Open Online Courses. European Journal of Open, Distance and E-Learning.
  • Sa’don, N. F., Alias, R. A., & Ohshima, N. (2014). Nascent research trends in MOOCs in higher educational institutions: A systematic literature review. In 2014 International Conference on Web and Open Access to Learning (ICWOAL), (pp. 1–4). IEEE.
  • Salgado, R. (2020). Topic modeling with NMF. Retrieved 20 August 2021. https:// towar dsdat ascie nce. com/ topic- model ing- artic les- withnmf8c6b2 a227a 45
  • Sangrà, A., González Sanmamed, M. & Anderson, T. (2015). Metaanálisis de la investigación sobre mooc en el período 2013–2014. Educación XX1, 18(2), 21–49.
  • Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2, 160.
  • Scholz, R. W., & Tietje, O. (2001). Embedded Case Study Methods: Integrating Quantitative and Qualitative Knowledge. USA: SAGE Publications.
  • Shah, D. (2020). By The Numbers: MOOCs in 2020. Class Central. https:// www. class centr al. com/ report/ moocstats2020/
  • Siemens, G. (2012). What is the theory that underpins our moocs? Elearnspace.
  • Van Eck, N., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84, 523–538.
  • Van Eck, N. J., & Waltman, L. (2011). Text mining and visualization using VOSviewer. ISSI Newsletter, 7(3), 50–54
  • Veletsianos, G., & Shepherdson, P. (2016). A systematic analysis and synthesis of the empirical MOOC literature published in 2013–2015. International Review of Research in Open and Distributed Learning, 17(2), 198–221
  • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge University.
  • Widiger, T. A., Frances, A. J., Pincus, H. A., & Davis, W. W. (1990). DSM-IV Literature reviews: Rationale, process, and limitations. Journal of Psychopathology and Behavioral Assessment, 12(3), 189–202.
  • Yousef, A. M. F., Chatti, M. A., Schroeder, U., Wosnitza, M. & Jakobs, H. (2014), ’A Review of the State-of-the-Art’, Proceedings of the 6th International Conference on Computer Supported Education.
  • Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39.
  • Zhu, M., Sari, A., & Lee, M. M. (2018). A systematic review of research methods and topics of the empirical MOOC literature (2014–2016). The Internet and Higher Education, 37, 31–39.
Fuente de los datos: Dialnet