Are Collaborative Filtering Methods Suitable for Student Performance Prediction?

BYDŽOVSKÁ, Hana. Are Collaborative Filtering Methods Suitable for Student Performance Prediction? In Francisco Pereira, Penousal Machado, Ernesto Costa, Amílcar Cardoso. Progress in Artificial Intelligence - 17th Portuguese Conference on Artificial Inteligence - EPIA 2015. Portugal: Springer International Publishing, 2015, s. 425-430. ISBN 978-3-319-23484-7. Dostupné z: https://dx.doi.org/10.1007/978-3-319-23485-4_42.

Základní údaje

Originální název

Are Collaborative Filtering Methods Suitable for Student Performance Prediction?

Autoři

BYDŽOVSKÁ, Hana (203 Česká republika, garant, domácí)

Vydání

Portugal, Progress in Artificial Intelligence - 17th Portuguese Conference on Artificial Inteligence - EPIA 2015, od s. 425-430, 6 s. 2015

Nakladatel

Springer International Publishing

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Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Portugalsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/15:00083048

Organizační jednotka

Fakulta informatiky

ISBN

978-3-319-23484-7

ISSN

DOI

http://dx.doi.org/10.1007/978-3-319-23485-4_42

UT WoS

000363570000042

Klíčová slova anglicky

Student Performance; Prediction; Collaborative Filtering Methods; Recommender System

Příznaky

Mezinárodní význam, Recenzováno

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Anotace

V originále

Researchers have been focusing on prediction of students’ behavior for many years. Different systems take advantages of such revealed information and try to attract, motivate, and help students to improve their knowledge. Our goal is to predict student performance in particular courses at the beginning of the semester based on the student’s history. Our approach is based on the idea of representing students’ knowledge as a set of grades of their passed courses and finding the most similar students. Collaborative filtering methods were utilized for this task and the results were verified on the historical data originated from the Information System of Masaryk University. The results show that this approach is similarly effective as the commonly used machine learning methods like Support Vector Machines.