Engage AI: Leveraging Video Analytics for Instructor-class Awareness in Virtual Classroom Settings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A difficulty for teachers in COVID-era online teaching settings is assessing engagement and student attention.This has made adapting teaching to the responses of the class a challenge.We developed a system called Engage AI for assessing engagement during live lectures.Engage AI uses video-based machine learning models to detect drowsiness and emotions like happiness and neutrality, and aggregates them in a dashboard that instructors can view as they speak.This provides real-time feedback to instructors, allowing them to adjust their teaching to keep students engaged.There is no video data transmitted outside of students' web browsers, and individual students are anonymous to the instructor.Testing in undergraduate engineering lectures resulted in 78.2% reporting feeling at least potentially more engaged during the lecture and at least 34.4% of students reporting feeling more engaged during the lecture.These approaches could be applicable to many forms of remote and in-person education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it