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At the present learning through online video is very popular. But there is no way to determine whether student is actually watching video or not. In this paper, an algorithm for real time eye state classification using simple web-cam is presented. Here one application is developed in which eyes of the person seated in-front of camera are detected using classifier. Four different eye positions: looking straight, looking left, looking upward and looking right are classified with the help of K-means clustering of the features of detected eyes. Here looking downward is not considered because it seems closed eyes and when closed eyes are detected the video will automatically pause. This approach is also used to detect constant gaze towards screen to prevent Computer Vision Syndrome. Another application of eye detection and eye state classification is to detect driver fatigue during driving. The experimental results prove the effectiveness of the presented methods. Given that two eyes are detected in a face; the system classifies the eye-states with an accuracy of 95%.