Are Collaborative Filtering Methods Suitable for Student Performance Prediction?

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F15%3A00083048" target="_blank" >RIV/00216224:14330/15:00083048 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-23485-4_42" target="_blank" >http://dx.doi.org/10.1007/978-3-319-23485-4_42</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-23485-4_42" target="_blank" >10.1007/978-3-319-23485-4_42</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Are Collaborative Filtering Methods Suitable for Student Performance Prediction?

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Are Collaborative Filtering Methods Suitable for Student Performance Prediction?

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Progress in Artificial Intelligence - 17th Portuguese Conference on Artificial Inteligence - EPIA 2015

ISBN

9783319234847

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

6

Strana od-do

425-430

Název nakladatele

Springer International Publishing

Místo vydání

Portugal

Místo konání akce

Coimbra, Portugal

Datum konání akce

1. 1. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—