Measures for Predicting Task Cohesion in a Global Collaborative Learning Environment

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Abstract

This paper describes a study that compared a number of interaction-based measures and their ability to predict cohesion within global software development projects. Messages were collected from three software development projects that involved students from two different countries. The similarities and quantities of such interactions were then analyzed and compared. Results from this analysis show a statistically significant correlation of linguistic characteristics (LSM) and Information Exchange Similarity with Task cohesion, when controlled by culture. In addition, the study found that quantity-based metrics had higher correlations with students' perceptions of their group's cohesiveness than similarity-based measures. More specifically, a word-based measure called Information Exchange Rate had a significant relationship to cohesion. Group rate measures were also tested, but only low significant correlations were found. These results suggest that measures based on quantity of interactions tend to be better predictors of cohesion within distributed learning teams.

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