Recursos educacionais baseados em visão computacional para analisar a relação entre atenção e desempenho escolar

Abstract

Monitoring attention in the classroom represents a recurring challenge in the educational context, especially because traditional methods, such as direct observation, tend to be subjective and poorly suited to systematically tracking students’ attentional variations. In this context, computer vision emerges as a promising alternative for the automated extraction of visual information associated with student behavior. Based on the literature review, a scarcity of studies was identified that directly relate attention measured through computer vision to school performance. Therefore, the objective of this research was to develop and apply a computational method to correlate attention indicators identified in classroom images with students’ school performance. To this end, a mixed-method approach was adopted, with an applied, exploratory, correlational, and documentary nature, based on data provided by a private educational institution in the city of São Paulo. Classroom images were processed using the YOLOv11 and YOLO Face models, enabling the detection of people and faces and the extraction of data used in the heuristic classification of attention levels. Subsequently, the frequencies of these levels were consolidated and converted into attention scores on a scale from 0 to 10 and organized with school performance scores, forming data pairs for descriptive and correlational analyses. The results showed greater stability in attention scores compared to performance scores, as well as a positive association between the variables. The Pearson coefficient was r = 0.285, with p = 0.127, indicating a weak and non-significant positive correlation. The Spearman coefficient was ρ = 0.375, with p = 0.041, indicating a moderate and statistically significant positive correlation. It is concluded that the proposed approach demonstrated feasibility in transforming image records into quantitative attention indicators and correlating them with school data. As a contribution, the study presents a methodological path applicable to educational analysis, although limited by the sample scope and the adopted heuristic, indicating the need for future research with expanded datasets and new visual indicators.