Board 57: WIP - A Web-based Face Recognition Application for Better In-Person Learning
Bibliographic record
Abstract
A face recognition application that enables instructors to conveniently know each student's name in the classroom is proposed.Communicating with students during lectures boosts more confidence and builds stronger relationships among students and their instructor, thus, enhances learning.The proposed solution is a web-based application that captures faces from videos and/or pictures through a User Interface that passes the data to a face recognition AI mechanism.The face recognition application also implements user management systems for privacy protection.With the Real-time Face Recognition Application, the course instructors can quickly recognize their students and address them by their names.A survey was conducted among 40 students to assess the comfortability of personal privacy, as well as the improvement of the learning environment in terms of engagement and readiness to engage in asking and answering questions during lectures.Over 85% agreed that instructors calling their names promotes a more friendly and engaging environment leading to improve the leaning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".