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The use of tools of data mining to decision making in engineering education—A systematic mapping study
Author(s) -
BuenañoFernandez Diego,
VillegasCH William,
LujánMora Sergio
Publication year - 2019
Publication title -
computer applications in engineering education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.478
H-Index - 29
eISSN - 1099-0542
pISSN - 1061-3773
DOI - 10.1002/cae.22100
Subject(s) - computer science , learning analytics , educational data mining , data science , analytics , field (mathematics) , process (computing) , data analysis , dropout (neural networks) , software analytics , systematic review , educational research , engineering education , software , data mining , engineering management , mathematics education , machine learning , engineering , software development , software development process , mathematics , medline , political science , pure mathematics , law , programming language , operating system
In recent years, there has been an increasing amount of theoretical and applied research that has focused on educational data mining. The learning analytics is a discipline that uses techniques, methods, and algorithms that allow the user to discover and extract patterns in stored educational data, with the purpose of improving the teaching‐learning process. However, there are many requirements related to the use of new technologies in teaching‐learning processes that are practically unaddressed from the learning analytics. In an analysis of the literature, the existence of a systematic revision of the application of learning analytics in the field of engineering education is not evident. The study described in this article provides researchers with an overview of the progress made to date and identifies areas in which research is missing. To this end, a systematic mapping study has been carried out, oriented toward the classification of publications that focus on the type of research and the type of contribution. The results show a trend toward case study research that is mainly directed at software and computer science engineering. Furthermore, trends in the application of learning analytics are highlighted in the topics, such as student retention or dropout prediction, analysis of academic student data, student learning assessment and student behavior analysis. Although this systematic mapping study has focused on the application of learning analytics in engineering education, some of the results can also be applied to other educational areas.