Towards Generalizable Detection of Urgency of Discussion Forum Posts [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023).

Autor/inn/en	Švábenský, Valdemar; Baker, Ryan S.; Zambrano, Andrés; Zou, Yishan; Slater, Stefan
Titel	Towards Generalizable Detection of Urgency of Discussion Forum Posts [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023).
Quelle	(2023), (8 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Generalization; Computer Mediated Communication; MOOCs; State Universities; Rating Scales; Student Attitudes; Group Discussion; Teacher Student Relationship; Student Motivation; Models; Prediction; Learning Analytics; Data Analysis; Computational Linguistics; Sentences; Learning Processes; Natural Language Processing; Pennsylvania; California (Stanford) + Suchen Sie Ihr Suchwort? Computerkonferenz; Staatliche Universität; Rating-Skala; Schülerverhalten; Gruppendiskussion; Teacher student relationships; Lehrer-Schüler-Beziehung; Schulische Motivation; Analogiemodell; Vorhersage; Auswertung; Linguistics; Computerlinguistik; Sentence analysis; Satzanalyse; Learning process; Lernprozess; Natürliche Sprache
Abstract	Students who take an online course, such as a MOOC, use the course's discussion forum to ask questions or reach out to instructors when encountering an issue. However, reading and responding to students' questions is difficult to scale because of the time needed to consider each message. As a result, critical issues may be left unresolved, and students may lose the motivation to continue in the course. To help address this problem, we build predictive models that automatically determine the urgency of each forum post, so that these posts can be brought to instructors' attention. This paper goes beyond previous work by predicting not just a binary decision cut-off but a post's level of urgency on a 7-point scale. First, we train and cross-validate several models on an original data set of 3,503 posts from MOOCs at University of Pennsylvania. Second, to determine the generalizability of our models, we test their performance on a separate, previously published data set of 29,604 posts from MOOCs at Stanford University. While the previous work on post urgency used only one data set, we evaluated the prediction across different data sets and courses. The best-performing model was a support vector regressor trained on the Universal Sentence Encoder embeddings of the posts, achieving an RMSE of 1.1 on the training set and 1.4 on the test set. Understanding the urgency of forum posts enables instructors to focus their time more effectively and, as a result, better support student learning. [For the complete proceedings, see ED630829.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01