Biometric signal processing has many of its applications in health care, rehabilitation research, and security systems. Nonetheless, over the last couple of decades, the biometric signal has encountered a new niche in educational research. In this work, the development of an eye-tracking device intended to be used for educational monitoring tasks is presented and learning studies. The device is based on the Electrooculography technique in contrast to the most common approaches that are based on image processing and computer vision devices and techniques. The proposed eye tracking device makes use of machine learning algorithms to detect the movements of the user's eye and in this way serve as a way of monitoring the cognitive activities of subjects or students. Moreover, a brief analysis of the cognitive skills related to the learning process of students that can be measure with the proposed system is presented. Also, a general comparison of other systems available in the current literature is made to compare the capabilities and areas of opportunity of the presented system. It is concluded that the device can recognize basic eye movements that can monitor basic cognitive behaviors like reading, writing, copying, or watching multimedia content through electronic devices. With further development and the generation of study cases, the eye-tracking system can be enhanced for further research related to the educational field.