Predicting teamwork results from social network analysis
Abstract
Modelling students' behaviours has reached a status that can only be overcome by improving the ability of predicting the results on teamwork. Indeed, teamwork is an important piece on the learning process, but understanding their mechanisms and predicting the results achieved is far from being solved by traditional classifiers. In this paper, we address the problem of predicting teamwork results, and propose a recommender system that suggests new teams, in the context of a given curricular unit. Any student, who is looking for a team, may use the system; in particular, he may ask for the best team to join, either considering all available colleagues or just the set of his previous teammates. Our system makes use of social network analysis and classification methods as the algorithmic core of the decision‐making process. System evaluation is presented through a set of experimental results, which report the performance of social network analysis and classification algorithms over real datasets.
Number of times cited according to CrossRef: 6
- Chih-Ming Chen and Chi-Hsiung Kuo, An optimized group formation scheme to promote collaborative problem-based learning, Computers & Education, 10.1016/j.compedu.2019.01.011, (2019).
- Yeawon Yoo, Yonghan Ju and So Young Sohn, Quantitative analysis of a half‐century of K‐Pop songs: Association rule analysis of lyrics and social network analysis of singers and composers, Journal of Popular Music Studies, 29, 3, (2017). 2017 18th International Carpathian Control Conference (ICCC) Sinaia, Romania 2017 18th International Carpathian Control Conference (ICCC) IEEE , (2017). 978-1-5090-4862-5 Alex Becheru and Elvira Popescu Design of a conceptual knowledge extraction framework for a social learning environment based on Social Network Analysis methods , (2017). 177 182 7970393 , 10.1109/CarpathianCC.2017.7970393 http://ieeexplore.ieee.org/document/7970393/
- Ángel García-Crespo, Manuel Ceballos, Israel González-Carrasco, Nora Lado and José L. López-Cuadrado, Innovation in the Spanish Twittersphere: An Ontology and Stakeholders’ Salience Analysis, Revolution of Innovation Management, 10.1057/978-1-137-57475-6_5, (97-128), (2016). 2017 21st International Conference on System Theory, Control and Computing (ICSTCC) Sinaia 2017 21st International Conference on System Theory, Control and Computing (ICSTCC) IEEE , (2017). 978-1-5386-3842-2 Alex Becheru and Elvira Popescu Using social network analysis to investigate students' collaboration patterns in eMUSE platform , (2017). 266 271 8107045 , 10.1109/ICSTCC.2017.8107045 http://ieeexplore.ieee.org/document/8107045/
- Ekaterina Koromyslova and Jerry Visser, A Statistical-Based Framework for Predicting Supplier’s Behavior to Quality Requirement Changes in Supply Chain, iBusiness, 07, 04, (137), (2015).
