A recent study led by Arturo Corona Ferreira and his team introduces an innovative methodology to assess teaching practices through students' brain activity.
Using low-cost Brain-Computer Interface (BCI) devices—specifically the NeuroSky Mindwave model—alongside the open-source software Neuroexperimenter, the researchers recorded real-time EEG signals during university classes.
This technology enabled the measurement of attention and meditation levels, resulting in more than 56,000 data points that provide insight into how students respond to various teaching activities.
What makes this approach unique is its ability to offer objective feedback to instructors without relying on surveys or self-reports.
By combining physiological data with qualitative classroom observations, the study reveals patterns of concentration, differences between groups, and even gender-based variations—highlighting the potential of brain signals as reliable indicators of student engagement.
For example, several students exhibited low attention levels that would have gone unnoticed through observation alone, underscoring the added value of EEG data.
This work not only marks progress in the field of neuroscience applied to education but also paves the way for new forms of learning analytics in real classroom settings.
The integration of BCI technologies into traditional teaching environments allows for a deeper understanding of students' mental states during learning, enabling more tailored pedagogical interventions. In this way, the classroom becomes a space where brain science can directly support the development of more effective and personalized teaching.
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How to Cite: Corona Ferreira, A., Altamirano Santiago, M., & López Ortega, M. de los Ángeles. (2021). Use of BCI devices in students for teacher assessment. RIED. Revista Iberoamericana De Educación a Distancia, 24(1), 315–328. https://doi.org/10.5944/ried.24.1.27502
