[c135] G. Kahrimanis, E. Mikroyiannidi, N. Avouris, Assessing the Quality of Synchronous Network Learning Activities using Machine Learning Techniques, Proceedings of 6th Int. Conf. on Networked Learning, pp. 187-194, Halkidiki, May 2008. ( pdf )
During network-mediated synchronous collaborative activities there is need for supporting reflection of the learners involved, by providing them with meaningful feedback on the state of group activity and the quality of their collaborative effort . In order to produce timely feedback to the partners, we need to automate processing activity data and producing meaningful measures of the quality of collaboration to be fed back to the students. This paper presents a study investigating applicability and effectiveness of machine learning techniques in the process. The objective is to use different classification algorithms for assessing quality of collaboration using a set of quantitative indices produced by the collaborative learning environment. Collaboration quality, however, is a term that needs first to be defined using a relevant scheme. After developing a scheme for assessment of collaboration quality in various axes, this study shows encouraging results in the performance of machine learning algorithm prediction scores.