Student Performance Prediction Using Collaborative Filtering Methods

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F15%3A00082601" target="_blank" >RIV/00216224:14330/15:00082601 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-19773-9_59" target="_blank" >http://dx.doi.org/10.1007/978-3-319-19773-9_59</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-19773-9_59" target="_blank" >10.1007/978-3-319-19773-9_59</a>

Result language
angličtina
Original language name
Student Performance Prediction Using Collaborative Filtering Methods
Original language description
This paper shows how to utilize collaborative filtering methods for student performance prediction. These methods are often used in recommender systems. The basic idea of such systems is to utilize the similarity of users based on their ratings of the items in the system. We have decided to employ these techniques in the educational environment to predict student performance. We calculate the similarity of students utilizing their study results, represented by the grades of their previously passed courses. As a real-world example we show results of the performance prediction of students who attended courses at Masaryk University. We describe the data, processing phase, evaluation, and finally the results proving the success of this approach.
Czech name
—
Czech description
—

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
17th International Conference on Artificial Inteligence in Education - AIED 2015
ISBN
9783319197722
ISSN
0302-9743
e-ISSN
—
Number of pages
4
Pages from-to
550-553
Publisher name
Springer International Publishing
Place of publication
Madrid
Event location
Madrid, Spain
Event date
Jan 1, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

Similar results(10)