Please use this identifier to cite or link to this item: http://hdl.handle.net/10497/22845
Title: 
Authors: 
Subjects: 
Teamwork
Pre-processing
Supervised machine learning
Text mining
Learning analytics
Feature engineering
Formative assessment
Chatlog
Issue Date: 
2018
Citation: 
Dhivya Suresh, Lek, H. H., & Koh, E. (2018). Identifying teamwork indicators in an online collaborative problem-solving task: A text-mining approach. In J. C. Yang, M. Chang, L.-H. Wong, & M. M. T. Rodrigo (Eds.), Proceedings of the 26th International Conference on Computers in Education (ICCE) 2018 (pp. 39-48). Manila, Philippines: Asia-Pacific Society for Computers in Education (APSCE). https://apsce.net/icce/icce2018/wp-content/uploads/2018/12/C1-05.pdf
Abstract: 
Teamwork is an important competency for 21st century learner. However, equipping students with an awareness of their teamwork behaviors is difficult. This paper therefore aims to develop a model that will analyze student dialogue to identify teamwork indicators that will serve as formative feedback for students. Four dimensions of teamwork namely coordination, mutual performance monitoring, constructive conflict and team emotional support are measured. In addition, the paper explores multi-label classification approaches combined with feature engineering techniques to classify student chat data. The results show that by incorporating linguistic features, it is possible to achieve better performance in identifying the teamwork indicators in student dialogue.
Description: 
This paper refers to data and analysis from the research projects OER62/12EK and OER09/15 EK, funded by the Education Research Funding Programme, National Institute of Education, Nanyang Technological University, Singapore.
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File Permission: 
Open
File Availability: 
With file
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