In this project, we have proposed an idea of embedding cognitive processes into information technologies to optimize students’ behaviors in virtual environments. We have developed hybrid spatio-temporal features for student behavior recognition (SBR) system that recognizes student-student behaviors from sequences of digital images. The proposed SBR system segments student silhouettes using neighboring data points observation and extracts co-occurring robust spatio-temporal features having full body and key body points techniques. Then, artificial neural network is used to measure student interactions taken from UT-Interaction and classroom behaviors datasets. Finally a survey is performed to evaluate the effectiveness of video based interactive learning using proposed SBR system.