Evaluation of content adaptation: Case study with NeuroSky MindWave in children with learning difficulties

Purpose: Students have learning difficulties, mainly in processes that involve attention and interpretation of written or spoken language. Technological tools allow to create computational platforms with adaptation aspects depending on the student’s characteristics. It is also important to highlight the progress of the measurement of cognitive processes such as attention through NeuroSky’s MindWave EEG sensors. This paper aims to present the results of analyzing attention levels of children with learning difficulties, based on the acquired brain waves. As a final result, an adaptive computational system that displays educational activities regarding educational profiles of children is obtained. Design/methodology/approach: The Kamachiy–Idukay platform was chosen to make the validation. The platform generates the educational activities according to the students’ profile. The validation phases were identification of the test environment, the first environment required a scenario that involved students with learning difficulties, to verify the functionality of the system, when analyzing cases of the students with learning difficulties; identification of two validation criteria, type of educational activity and attention difficulties of the students; and analysis of the brain signal when children interact with the educational content. Findings: The adaptation of contents that include music and animations generate higher levels of attention in students with difficulty. The analysis of signals from the NeuroSky sensor to determine the attentional levels in children allowed a generation of content adapted to the characteristics of the difficulty in each child. Research limitations/implications: For the validation, it was necessary at the beginning of the activity to determine the stability of the signal emitted by the NeuroSky sensor. Two cases were studied in children with difficulty and their measure of attention versus adaptive contents. Practical implications: A k-means algorithm was used to establish the attention levels of the children. Social implications: Children with learning difficulties have different learning styles, which implies an adaptation of content that generates an attentional process according to their characteristics. Originality/value: Evaluation content adaptation taking into account the signal brain sensor NeuroSky for learning process. The signal brain of the student when interacting with the activities is include in the student profile.