Identifying Students' Persistence Profiles in Problem Solving Task
Document Type
Conference Proceeding
Publication Date
7-2018
Abstract
This study explores task persistence in the context of Learning by Teaching. Using features extracted from students' interaction logs, a centroid based clustering algorithm derived two well-separated groups describing two types of students, Cluster 1 which is characterized by the more persistent students and Cluster 0 which is characterized by the less persistent students. The more persistent students demonstrated effective help-seeking behavior, and greater level of task engagement and resourcefulness compared to the less persistent students.
Recommended Citation
Cristina E. Dumdumaya, Michelle P. Banawan, and Ma. Mercedes T. Rodrigo. 2018. Identifying Students’ Persistence Profiles in Problem Solving Task. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization (UMAP ’18). Association for Computing Machinery, New York, NY, USA, 281–286. DOI:https://doi.org/10.1145/3213586.3225237
