Detailed Information on Publication Record

@proceedings{59910,
   author = {Švábenský, Valdemar and Bouchet, François and Tarrazona, Francine and Lopez II, Michael and Baker, Ryan S.},
   booktitle = {14th International Conference on Learning Analytics and Knowledge},
   keywords = {learning analytics; educational data mining; urgency detection; replication},
   language = {eng},
   title = {Data Set Size Analysis for Detecting the Urgency of Discussion Forum Posts},
   url = {https://www.solaresearch.org/core/companion-proceedings-of-the-14th-international-learning-analytics-and-knowledge-conference-lak24/},
   year = {2024}
}

TY  - CONF
ID  - 59910
AU  - Švábenský, Valdemar - Bouchet, François - Tarrazona, Francine - Lopez II, Michael - Baker, Ryan S.
PY  - 2024
TI  - Data Set Size Analysis for Detecting the Urgency of Discussion Forum Posts
KW  - learning analytics
KW  - educational data mining
KW  - urgency detection
KW  - replication
UR  - https://www.solaresearch.org/core/companion-proceedings-of-the-14th-international-learning-analytics-and-knowledge-conference-lak24/
N2  - 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.
ER  - 

ŠVÁBENSKÝ, Valdemar, Fran$\backslash$c cois BOUCHET, Francine TARRAZONA, Michael LOPEZ II and Ryan S. BAKER. Data Set Size Analysis for Detecting the Urgency of Discussion Forum Posts. In \textit{14th International Conference on Learning Analytics and Knowledge}. 2024.