Methodological Considerations for Predicting At-risk Students

Charles Koutcheme*, Sami Sarsa, Arto Hellas, Lassi Haaranen, Juho Leinonen

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

1 Sitaatiot (Scopus)
21 Lataukset (Pure)

Abstrakti

Educational researchers have long sought to increase student retention. One stream of research focusing on this seeks to automatically identify students who are at risk of dropping out. Studies tend to agree that earlier identification of at-risk students is better, providing more room for targeted interventions. We looked at the interplay of data and predictive power of machine learning models used to identify at-risk students. We critically examine the often used approach where data collected from weeks 1, 2,..., n is used to predict whether a student becomes inactive in the subsequent weeks w, w ≥ n + 1, pointing out issues with this approach that may inflate models’ predictive power. Specifically, our empirical analysis highlights that including students who have become inactive on week n or before, where n > 1, to the data used to identify students who are inactive on the following weeks is a significant cause of bias. Including students who dropped out during the first week makes the problem significantly easier, since they have no data in the subsequent weeks. Based on our results, we recommend including only active students until week n when building and evaluating models for predicting dropouts in subsequent weeks and evaluating and reporting the particularities of the respective course contexts.
AlkuperäiskieliEnglanti
OtsikkoACE '22: Australasian Computing Education Conference
KustantajaACM
Sivut105-113
Sivumäärä9
ISBN (elektroninen)9781450396431
DOI - pysyväislinkit
TilaJulkaistu - 14 helmik. 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaAustralasian Computing Education Conference - Virtual, Online, Austraalia
Kesto: 14 helmik. 202218 helmik. 2022
Konferenssinumero: 24
https://aceconference.wordpress.com/

Conference

ConferenceAustralasian Computing Education Conference
LyhennettäACE
Maa/AlueAustraalia
KaupunkiVirtual, Online
Ajanjakso14/02/202218/02/2022
www-osoite

Sormenjälki

Sukella tutkimusaiheisiin 'Methodological Considerations for Predicting At-risk Students'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä