The use of tools of data mining to decision making in engineering education—A systematic mapping study

Abstract

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.

A data‐mining‐based approach to informed decision‐making in engineering education

“…Concerning the EDM applications to analyze educational data in engineering education, a very useful source on these works is , where four main application areas of EDM are identified. The first application area is an improvement in terms of the academic development of the students such as the analysis of learning styles or learning strategies, the analysis and improvement of student skills, and the motivation trends of students, among others.…”

Extraction, Processing and Visualization of Peer Assessment Data in Moodle

“…For example, in the systematic review of the literature on educational data mining by [18], the authors identify 166 research articles dealing with clustering methods (i.e., pertaining to the realm of data mining algorithms and analysis techniques) in educational settings in the 1983 to 2016 period. The same is not true for data extraction and processing, as the mapping study by [19] confirms. In general, little attention is given to the internal operation of data processing (data preprocessing generally involves three different types of operations as a requisite for data mining: data reduction, data projection [20] and data aggregation [21]; this research specifically focuses on data aggregation of peer assessement activities as a means to facilitate data visualization and guarantee extensibility and interoperability), which is an essential part of the learning analytics cycle and contributes to increasing the effectiveness of most learning analytics and educational data mining techniques [22].…”

Data Mining for Student Performance Prediction in Education

“…Until now, data mining algorithms have been applied on various different educational fields such as engineering education [11], physical education [12], and English language education [13]. Some studies have focused on high school students [14], while some of them have interested in higher education [15].…”