Přehled o publikaci
2024
Data Set Size Analysis for Detecting the Urgency of Discussion Forum Posts
ŠVÁBENSKÝ, Valdemar; François BOUCHET; Francine TARRAZONA; Michael LOPEZ II; Ryan S. BAKER et al.Základní údaje
Originální název
Data Set Size Analysis for Detecting the Urgency of Discussion Forum Posts
Autoři
ŠVÁBENSKÝ, Valdemar; François BOUCHET; Francine TARRAZONA; Michael LOPEZ II a Ryan S. BAKER
Vydání
14th International Conference on Learning Analytics and Knowledge, 2024
Další údaje
Jazyk
angličtina
Typ výsledku
Konferenční abstrakta
Stát vydavatele
Spojené státy
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Označené pro přenos do RIV
Ne
Organizace
Masarykova univerzita – Repozitář
Klíčová slova anglicky
learning analytics; educational data mining; urgency detection; replication
Změněno: 23. 3. 2024 03:48, RNDr. Daniel Jakubík
Anotace
V originále
In both Massive Open Online Courses (MOOCs) and private courses, instructors face a large amount of queries in discussion forum posts that may merit a response. There has been ongoing research on how to employ machine learning to predict a post’s urgency in order to focus instructors’ attention. However, it is unclear how large a course is needed to develop these models. We took a publicly available data set of 3,503 labeled forum posts and code from one such prior study. We re-trained the six models described in the study, but with progressively smaller sample sizes, to determine if the models’ performance would be preserved. Likewise, we demonstrate that using random subsets even as small as 10% of the original data set achieves comparable performance to full data sets in five out of six models.