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Learners frequently overlook crucial information, directions, and concepts while they are passive and sleepy. They tend to miss important content, instructions, and concepts. Iris Angle Position (IAP) and electroencephalography (EEG) were used in this model to identify the behaviour of learners. Specifically, a Deep Convolutional Neural Network (DCNN) is constructed to extract IAP in order to accurately capture the learner\u2019s facial area. EEG signals are effectively handled and sorted using deep reinforcement learning (DRL). The learners\u2019 facial landmarks are retrieved from a frame using the dlib toolbox. Only eye landmark points from face landmarks alone are focused on in order to determine the learner\u2019s behaviour. When the learners EEG signals and Iris positions are monitored simultaneously, it\u2019s helpful to identify the learner\u2019s fatigue state (LFS) and the learner\u2019s distraction state (LDS). The Brain Vision Algorithm (BVA) uses iris position and minimal facial landmarks, along with brain activity, to properly identify the learner\u2019s level of distraction and exhaustion. When a student is detected as being preoccupied or sleepy, an alert goes off automatically, and the educator gets performance feedback. Iris position data and brain-computer interface-based EEG signal values are utilised to identify distraction and sleepiness. Comparative tests have demonstrated that this innovative method offers fast and high-accuracy student activity detection in virtual learning settings. Applying the suggested approach to different existing classifiers yields an F-Score of 91.92%, a recall of 93.87%, and a precision of 92.37% . 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