Eye-tracking and education
Eye-tracking is a method that enables the gathering of information and data to make an empirical analysis of human cognition, behavior, and perception [S2]. These characteristics have enabled that over the last couple of decades, the eye-tracking devices used for education applications or research learning have started to grow. Some of the fields of education in which this technology has been applied are in text reading for language learning, mathematics understanding, and science knowledge learning [ZY1]. Eye-tracking research focused on education has also been concerned in improving the design of computer-based learning, in the visual areas such as medicine or chess, and more recently to promote visual proficiency by eye movement modeling [HK1].
For instance, the use of eye-tracking technology has been applied to student reading behavior of electronic books. In the study of Kao [GX1], eye-tracking technology was used to understand the reading process of students while visualizing online materials. On another hand, image processing devices have also been used to analyze the studying behavior of mathematics content at elementary school one example of this is the work of Sun in which a software interface that employs eye-tracking technology was developed to improve the teaching framework [SL1]. Eye-tracking devices have started to pick up certain attention in the involvement of people that suffer from some type of disability in educational processes an example of the above is presented in the work of Moreva [MK1] in which an eye-tracking platform was used to study the attention process of students while visualizing a web page-oriented for people with disabilities, in this same work it is also mentioned the growth of several students with restricted health conditions.
According to Hajra medical educational field can also be enhanced with eye-tracking technology [HM1]. In his work, a literature review was made in which seventeen studies on clinical assessment and six focused on eye-tracking used as an assessment tool were studied, demonstrating the usefulness of the methodology in medical practices. Beach [PJ1] shows a study in which it is determined that eye-tracking methodologies can give an insight into teachers’ engagement about learning material, reading behaviors, or sensemaking strategies. In this same study, it is remarked that these methodologies can show educational stakeholders' opportunity areas related to learning outcomes and environments. Finally, in the study of Lin [LW1], 38 computer science students were monitor through eye-tracking devices while debugging C language program codes, to understand the cognitive process of the student while reviewing software.
From this brief literature review, it is possible to notice the great areas of opportunity that eye-tracking technology can have in educational applications and research related to teaching strategies. Nonetheless, most of the proposals are enable through image processing devices to collect data from the eye gaze. The above led to the design of an alternative Eye-tracking technology based on electrooculography that has shown some potential used in educational research as shown in [OS1]. Nevertheless, the potential of eye-tracking devices based on Electrooculography has not been extendedly expired in the current state of the art. This procedure is described in the next section of this work.
Eye-tracking device development
Electrooculography is an electrophysiological technique in which the biopotential generated between the cornea and the retina is measure using silver/silver chloride electrodes [AP1]. Nevertheless, this biopotential is susceptible to a great quantity of noise and variations making it rarely deterministic even for the same person in a different environment. To counterweight this problem, it is important to count on adequate processing of the signal both in hardware and software to assure a good performance of the device.
To generate a low-cost device, the preprocessing of the signal needs to be performed with a less quantity of components. To achieve the above, it is important to consider that the EOG has a very low amplitude ranging from 50 to 3500 μV and its bandwidth is limited from 0Hz otherwise known as the D.C. component and 30Hz according to the International Society for Clinical Electrophysiology for Vision (ISCEV) [BF1]. However, these signals are susceptible to very low-frequency noise commonly referred to as based line drift. Therefore, an amplification stage needs to be performed with the use of an instrumentation amplifier (AD620) and a decoupling the D.C. stage achieve by using a high pass filter with a cut-off frequency of 0.5Hz to 1Hz. Later, a low pass filter with a cut off frequency of 30 Hz is placed in cascade to the high pass filter. This low pass filter also serves as an antialiasing filter for the sampling of the signal with a microcontroller (MCU) capable of communicating with any type of computer or electronic device. To enable the communication of the MCU with any type of electronic device, a Bluetooth communication module (HC06) was used for this purpose. Finally, bipolar to unipolar conversion circuit is placed between the output signal of the low pass filter and the analog channel of the microcontroller to limit the amplitude of the signal in a range of 0V to 5V and avoid damaging the analog channel of the MCU. This pre-processing circuit can be appreciated in Fig \ref{149602}.