Course Recommendation from Social Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F14%3A00074736" target="_blank" >RIV/00216224:14330/14:00074736 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Course Recommendation from Social Data

Popis výsledku v původním jazyce

This paper focuses on recommendations of suitable courses for students. For a successful graduation, a student needs to obtain a minimum number of credits that depends on the field of study. Mandatory and selective courses are usually defined. Additionally, students can enrol in any optional course. Searching for interesting and achievable courses is time-consuming because it depends on individual specializations and interests. The aim of this research is to inspect different techniques how to recommendstudents such courses. This paper brings results of experiments with three approaches of predicting student success. The first one is based on mining study-related data and social network analysis. The second one explores only average grades of students. The last one aims at subgroup discovery for which prediction may be more reliable. Based on these findings we can recommend courses that students will pass with a high accuracy.

Název v anglickém jazyce

Course Recommendation from Social Data

Popis výsledku anglicky

This paper focuses on recommendations of suitable courses for students. For a successful graduation, a student needs to obtain a minimum number of credits that depends on the field of study. Mandatory and selective courses are usually defined. Additionally, students can enrol in any optional course. Searching for interesting and achievable courses is time-consuming because it depends on individual specializations and interests. The aim of this research is to inspect different techniques how to recommendstudents such courses. This paper brings results of experiments with three approaches of predicting student success. The first one is based on mining study-related data and social network analysis. The second one explores only average grades of students. The last one aims at subgroup discovery for which prediction may be more reliable. Based on these findings we can recommend courses that students will pass with a high accuracy.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

6th International Conference on Computer Supported Education - CSEDU 2014

ISBN

9789897580208

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

268-275

Název nakladatele

2014 SCITEPRESS ? Science and Technology Publications

Místo vydání

Portugal

Místo konání akce

Barcelona, Spain

Datum konání akce

1. 4. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—