Clickstream-based outcome prediction in short video MOOCs

In this paper, we present a data mining approach for analysing students’ clickstream data logged by an e-learning platform and we propose a machine learning procedure to predict course completion of students. For this, we used data from a short MOOC course which was motivated by the teachers of elem...

Teljes leírás

Elmentve itt :
Bibliográfiai részletek
Szerzők: Kőrösi Gábor
Esztelecki Péter
Farkas Richárd
Tóth Krisztina
Dokumentumtípus: Könyv része
Megjelent: IEEE Piscataway (NJ) 2018
Sorozat:Proceedings of International Conference on Computer, Information and Telecommunication Systems (CITS) 2018
Tárgyszavak:
doi:10.1109/CITS.2018.8440182

mtmt:30631826
Online Access:http://publicatio.bibl.u-szeged.hu/23815
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520 3 |a In this paper, we present a data mining approach for analysing students’ clickstream data logged by an e-learning platform and we propose a machine learning procedure to predict course completion of students. For this, we used data from a short MOOC course which was motivated by the teachers of elementary schools. We show that machine learning approaches can accurately predict the course outcome based on clickstream data and also highlight patterns in data which provide useful insights to MOOC developers. 
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