Predicting first-year computer science students drop-out with machine learning methods: A case study
author
Maksimova, Natalja
Pentel, Avar
Dunajeva, Olga
statement of authorship
Natalja Maksimova, Avar Pentel, Olga Dunajeva
source
Educating Engineers for Future Industrial Revolutions : Proceedings of the 23rd International Conference on Interactive Collaborative Learning (ICL2020). Volume 2
location of publication
Cham
publisher
Springer
year of publication
2021
pages
p. 719-726
series
Advances in Intelligent Systems and Computing ; 1329
conference name, date
ICL2020 – 23rd International Conference on Interactive Collaborative Learning, 23–25 September 2020, Virtual Conference
conference location
Tallinn, Estonia
url
https://doi.org/10.1007/978-3-030-68201-9_70
subject term
õppimine
kõrgkoolid
õppekorraldus
õpiraskused
Scopus
https://www.scopus.com/sourceid/5100152904
https://www.scopus.com/record/display.uri?eid=2-s2.0-85103474918&origin=inward&txGid=0dad13380ef7fba308b7afa11fef95be
WOS
https://www.webofscience.com/wos/woscc/full-record/WOS:000772405700070
quartile
Q4
category (general)
Computer science
Arvutiteadus
Engineering
Tehnika
category (sub)
Computer science. General computer science
Arvutiteadus. Üldine arvutiteadus
Engineering. Control and systems engineering
Tehnika. Juhtimis- ja süsteemitehnika
name of the institution
Tallinna Tehnikaülikool. Virumaa Kolledž
keyword
students’ dropout
machine learning
prediction
ISSN
2194-5357
ISBN
978-3-030-68200-2
notes
Includes bibliogr
scientific publication
teaduspublikatsioon
classifier
3.1
TalTech department
Virumaa kolledž
language
inglise