A Study of Human-Computer Interaction in Evaluating Students’ Emotional Behavior and Educational Management under Big Data
Bibliographic record
Abstract
Students in adolescence are not mature in mind, thought, ability and other aspects, which are easily affected by various emotional behaviors.Positive emotional behavior contributes to students' mental health and academic progress.Negative emotional behavior would lead to psychological problems and academic frustration.If it is not paid attention to, students may act out of control under the control of negative emotions, thus resulting in serious mental illness, which is not conducive to the education and management of students.Due to the rapid development of social information network and science and technology, the analysis of students' emotional behavior and educational management by pure human intervention has fallen behind, and it is impossible to timely feedback, track and predict students' status.This paper introduced the general direction and achievements of human-computer interaction research, and discussed the combination of big data and human-computer interaction.The method of applying human-computer interaction technology to students' emotional behavior analysis and education management was studied.The pure human intervention method was compared with facial emotion recognition, voice emotion recognition, human-computer body feeling interaction and virtual scene education methods under human-computer interaction technology.Five experimental groups were designed to conduct research in three aspects of emotional behavior analysis, education and learning, and supervision and management.It was found that the average accuracy of facial emotion recognition for emotional behavior analysis was 88.0%; the average course learning efficiency of virtual scene education used for students' educational learning was 82.8%, and the total progress was up to 99.81%; the average success rate of human-computer somatosensory interaction for supervision and management was the highest, which was 68.1%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".