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.Basic information
Original name
Data Set Size Analysis for Detecting the Urgency of Discussion Forum Posts
Authors
ŠVÁBENSKÝ, Valdemar; François BOUCHET; Francine TARRAZONA; Michael LOPEZ II and Ryan S. BAKER
Edition
14th International Conference on Learning Analytics and Knowledge, 2024
Other information
Language
English
Type of outcome
Konferenční abstrakta
Country of publisher
United States of America
Confidentiality degree
is not subject to a state or trade secret
References:
Marked to be transferred to RIV
No
Organization
Repository – Repository
Keywords in English
learning analytics; educational data mining; urgency detection; replication
Changed: 23/3/2024 03:48, RNDr. Daniel Jakubík
Abstract
In the original language
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.