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Exploring student characteristics as predictors of team effectiveness: A PCA and Machine Learning Approach

Zhang, Yuxuan (2024) Exploring student characteristics as predictors of team effectiveness: A PCA and Machine Learning Approach.

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Abstract:This research focuses on how individual characteristics influence team effectiveness, which are critical to successful educational outcomes at academic institutions. This paper implements PCA technique and develops team predictive models based on student data from University of Twente, providing new insights into the impact of individual characteristics on teams. First, the paper describes data preprocessing methods to address missing data and standardize data formats across years. Second, the paper details the application of decision trees, random forests, and PCA techniques to analyze the effects of different characteristics on team outcomes. Finally, the paper launched a critical analysis of the result. The results reveal that individual previous academic performance has a strong positive correlation with team effectiveness, but Belbin team roles have a smaller impact, likely due to the complex interplay of role within teams. These findings underscore the importance of considering both individual and collective factors in educational team settings.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Business & IT BSc (56066)
Link to this item:https://purl.utwente.nl/essays/101922
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