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Extracting relevant information in collaborative supported computer learning to enable an effective monitoring of students and fostering learning management is a challenge. Several works have been developed from data mining techniques for the purpose of educational decision making. The main objective of this study was to investigate the feasibility of using the linear regression model to obtain inferences in the early stages of conducting online courses as a way to support decision making by teachers and administrators. We propose the use of linear regression to estimate the performance of students based on their interactions within the learning management system, taking into account behavioral variables. The results showed that it's possible to use the linear regression technique to obtain inferences with good accuracy rates.

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Due to the difficulty on teachers manually monitoring social interactions on the Collaborative Learning Environments (CLE), is expected some automatic support. The main objective of this work was to investigate and develop the architecture for monitoring social interactions called Amadeus-SIMA integrated with CLE Amadeus. This work validated two hypotheses through an empirical experiment: enhance the teachers' awareness of the student's social presence and predict the students performance based on social behaviors generated by Amadeus-SIMA.