Associating students and teachers for tutoring in higher education using clustering and data mining
| Autor: | Jorge de la Calleja, Ma. Auxilio Medina, Argelia Berenice Urbina Nájera |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
General Computer Science
Higher education Multimedia business.industry Computer science 05 social sciences General Engineering 050301 education 02 engineering and technology computer.software_genre Educational data mining Education Distraction ComputingMilieux_COMPUTERSANDEDUCATION 0202 electrical engineering electronic engineering information engineering Mathematics education 020201 artificial intelligence & image processing Digital learning business Cluster analysis 0503 education computer |
| Zdroj: | Computer Applications in Engineering Education. 25:823-832 |
| ISSN: | 1061-3773 |
| DOI: | 10.1002/cae.21839 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text