Rapid growth and recent developments in education sector and information technologies have promoted E-learning and collaborative sessions among the learning communities and business incubator centers. Traditional practices are being replaced with webinars (live online classes) E-Quizes (online testing) and video lectures for effective learning and performance evaluation. These E-learning methods use sensors and multimedia tools to contribute in resource sharing, social networking, interactivity and corporate trainings. While, artificial intelligence tools are also being integrated into various industries and organizations for students' engagement and adaptability towards the digital world. Predicting students' behaviors and providing intelligent feedbacks is an important parameter in the E-learning domain. To optimize students' behaviors in virtual environments, we have proposed an idea of embedding cognitive processes into information technologies. This paper presents 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.