a 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.

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