Assessing the Quality of Synchronous Network Learning Activities using Machine Learning Techniques

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. 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 NSCL environment. Collaboration quality, however is a term that needs first to be defined using a relevant scheme. The typical collaboration activities studied involved dyads of students following a distance learning computer science course. The dyads were asked to solve an algorithm problem and develop the solutions in the form of flowchart diagrams. They used a NSCL tool, Synergo, that provides a frame of reference, a shared drawing space through which various diagrammatic representations can be built jointly by a group o