Literaturnachweis - Detailanzeige
| Autor/inn/en | Esteban, Aurora; Zafra, Amelia; Romero, Cristóbal |
|---|---|
| Titel | A Hybrid Multi-Criteria Approach Using a Genetic Algorithm for Recommending Courses to University Students [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (11th, Raleigh, NC, Jul 16-20, 2018). |
| Quelle | (2018), (7 Seiten)
PDF als Volltext kostenfreie Datei |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | College Students; Course Selection (Students); Computer Uses in Education; Elective Courses; Mathematics; Criteria; Foreign Countries; Data Analysis; Spain |
| Abstract | This paper describes a multiple criteria approach based on a hybrid method of Collaborative Filtering (CF) and ContentBased Filtering (CBF) for discovering the most relevant criteria which could affect the elective course recommendation for university students. In order to determine which factors are the most important, it is proposed a genetic algorithm which automatically discovers the importance of the different criteria assigning weights to each one of them. We have carried out an in-depth study using a real data set with more than 1700 ratings of Computer Science graduates at University of Cordoba. We have used different proposals and different weights for each criterion in order to discover what is the combination of multiple criteria which provides better results. [For the full proceedings, see ED593090.] (As Provided). |
| Anmerkungen | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2020/1/01 |
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