Research Trends in Educational Data Mining in MOOCS: A Systematic Mapping of Literature
DOI:
https://doi.org/10.5753/rbie.2020.28.0.491Keywords:
MOOCs, Educational Data Mining, Systematic ScopingAbstract
Massive Open Online Courses (MOOCs) use online platforms and attract different student profiles, offering qualification opportunities - whether formal or informal - in a very dynamic format. A characteristic of the platforms that offer such courses is the ability to store a large amount of data, which made it possible to explore it through Educational Data Mining (EDM) techniques. In this context, a systematic scoping was conducted in five databases with the purpose of discover research trends regarding to the use of EDM in MOOCs. The search covered the period from 2015 to 2020, selecting 158 papers. The results revealed that studies related to Behavior Analysis, Prediction (Performance, Abandonment, Conclusion), Text Mining and Recommendation Systems are the most frequent. Promissing researches were also identified, such as Social Network Analysis (SNA), Digital Learning Ecosystem (DLE) and Mind Wandering Analysis (MW). Methods and tools used in research were listed, as well as challenges in the use of EDM in research on MOOC. We concluded that the issue of class imbalance, caused by low adherence to courses, is one of the biggest challenges.
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Copyright (c) 2020 Vanessa Faria De Souza, Gabriela Trindade Perry
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