The invention provides a learning input degree evaluation method and
system based on reaction behaviors of human-computer interaction, and relates to the technical field of input degree evaluation, and the method comprises the steps: data collection and synchronous alignment: achieving
time sequence alignment through collecting physiological behavior data;
feature engineering and scene adaptive modeling are carried out, and a
deep learning network is constructed to extract scene sensitive features; performing multi-
modal fusion and cognitive state decoding, fusing
eye movement, electroencephalogram and behavior data, and outputting
cognition, emotion and behavior input degree indexes; performing adaptive intervention decision and execution, and dynamically selecting content highlight or
visual guidance based on
reinforcement learning; personal baseline evolution and model online updating are carried out, and
continuous optimization of the
system is realized. The
system comprises a multi-mode fusion module, a cognitive state decoding module and the like. According to the method, the cognitive response is actively stimulated through standardized interaction tasks, the problem of
data splitting is solved, a multi-level
evaluation system is constructed in combination with the education theory, the teaching effectiveness is improved, an evaluation intervention
closed loop is formed, and the evaluation accuracy and the personalized level are improved.