Predicting academic performance using cognitive biometrics and neuroeducation in smart university environments

Luis David Bastidas González, Jhonny Biler Benavides Galvez, Fanny del Rocio Idrogo Vásquez, Roxana Paola Cetre Vásquez, Idaluz Magly Neira Ortega and Dante Yván Chavil Montenegro
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Luis David Bastidas González: Instituto Sapiens de Investigación Científica Multidisciplinar. Milagro, Ecuador.
Jhonny Biler Benavides Galvez: Universidad Nacional Autónoma de Chota, Chota, Perú.
Fanny del Rocio Idrogo Vásquez: Universidad Nacional Autónoma de Chota, Chota, Perú.
Roxana Paola Cetre Vásquez: Universidad Estatal de Milagro (UNEMI). Milagro, Ecuador.
Idaluz Magly Neira Ortega: Universidad Nacional del Altiplano, Puno, Perú.
Dante Yván Chavil Montenegro: Universidad San Ignacio de Loyola, Lima, Perú.

NeuroData, 2026, vol. 3, 170

Abstract: Conceptualization: Academic performance prediction through cognitive biometrics and neuroeducation constituted a research field aimed at understanding the cognitive, emotional, and technological factors that influenced learning within intelligent university environments. This approach integrated contributions from neuroscience, artificial intelligence, and learning analytics to identify patterns associated with student performance. Objective: The study aimed to analyze the influence of cognitive biometrics and neuroeducational strategies on the prediction of academic performance among university students in Ecuador and Peru, with the purpose of identifying variables that strengthened predictive models and educational decision-making. Methods: A quantitative empirical approach was adopted using a non-experimental, cross-sectional, and correlational-explanatory design. The sample consisted of 1,024 university students selected through stratified probabilistic sampling. Data were collected through a Likert-scale survey and analyzed using descriptive and inferential statistical techniques. Results: The findings revealed a positive perception regarding the influence of cognitive attention, neuroeducational strategies, and intelligent systems on academic performance. More than 70% of the participants reported agreement with the usefulness of these tools for strengthening learning processes. Conclusions: It was concluded that cognitive biometrics and neuroeducation represented relevant factors for understanding and predicting academic performance. Furthermore, the integration of artificial intelligence and multimodal data supported the implementation of personalized educational interventions.

Keywords: cognitive biometrics; neuroeducation; academic performance; artificial intelligence. (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:cxn:neurod:v:3:y:2026:id:170

DOI: 10.63688/neurodata2026170

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