Premium
Associating students and teachers for tutoring in higher education using clustering and data mining
Author(s) -
Urbiájera Argelia B.,
de la Calleja Jorge,
Medina Ma. Auxilio
Publication year - 2017
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.21839
Subject(s) - cluster analysis , computer science , task (project management) , mathematics education , distraction , maximization , process (computing) , artificial intelligence , psychology , engineering , cognitive psychology , social psychology , systems engineering , operating system
Tutoring is part of the teaching–learning process; this is considered a complementary strategy to support the development of integral and competent professionals. When teachers deal with large groups of students such as in digital learning environments, tutoring becomes a time‐consuming and difficult task that can cause distraction and overload. This paper presents an experimental study to associate students and teachers for tutoring according to their skills and affinities using the clustering methods of k‐means, expectation maximization, and farthest first. The study harvests data of 1,199 university students and 35 teachers. The results reached 100% of compatibility between clusters using expectation maximization and farthest first.