Open AccessEducational data mining for students' performance based on fuzzy C‐means clustering
Author(s) -
Li Yu,
Gou Jin,
Fan Zongwen
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0938
Subject(s) - cluster analysis , computer science , educational data mining , process (computing) , fuzzy logic , fuzzy clustering , quality (philosophy) , data mining , mathematics education , machine learning , data science , artificial intelligence , psychology , philosophy , epistemology , operating system
Education greatly aids in the process of students' growth; therefore, education institutions try to provide high‐quality education to their students. A possible remedy to provide high‐quality education is by discovering knowledge from educational data. However, accurately evaluating students' performance is very challenging due to different sources and structures of educational data. In addition, different teaching strategies are required because students' learning ability are different. One way to discover the hidden knowledge from educational data is the use of clustering algorithms, which are capable of mining interesting patterns from educational data. Thus, this study presents a fuzzy C‐means clustering algorithm using 2D and 3D clustering to evaluate students' performance based on their examination results (the examination grades from College of Computer Science and Technology, Huaqiao University for students enrolled in year 2014). Based on the experimental results from 2D and 3D clustering for evaluating students' performance, the educators can better understand the students' performance so as to build a pedagogical basis for decisions. Students can also receive some recommendations from the mining results about their performance.