Identify whether the teacher is at the board
using AI
Below is a free classifier to identify whether the teacher is at the board. Just upload your image, and our AI will predict if the teacher is at the board - in just seconds.
API Access
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("whether-the-teacher-is-at-the-board-identifier", "your_image_url", credentials)
fetch('https://www.nyckel.com/v1/functions/whether-the-teacher-is-at-the-board-identifier/invoke', {
method: 'POST',
headers: {
'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
'Content-Type': 'application/json',
},
body: JSON.stringify(
{"data": "your_image_url"}
)
})
.then(response => response.json())
.then(data => console.log(data));
curl -X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_BEARER_TOKEN" \
-d '{"data": "your_image_url"}' \
https://www.nyckel.com/v1/functions/whether-the-teacher-is-at-the-board-identifier/invoke
How this classifier works
To start, upload your image. Our AI tool will then predict if the teacher is at the board.
This pretrained image model uses a Nyckel-created dataset and has 2 labels, including Teacher At Board and Teacher Not At Board.
We'll also show a confidence score (the higher the number, the more confident the AI model is around if the teacher is at the board).
Whether you're just curious or building whether the teacher is at the board detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify whether the teacher is at the board at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Classroom Attendance Monitoring: This use case involves automatically tracking teacher presence at the whiteboard during lessons. By analyzing classroom videos, the system can generate attendance reports, ensuring teachers are engaged during instruction time and improving classroom management.
- Interactive Learning Analysis: Schools can use this function to measure student interaction levels with the teacher at the board. By determining when the teacher is actively presenting, administrators can assess engagement and modify teaching methods based on student response patterns.
- Professional Development Feedback: Educational institutions can implement this binary classification function to provide feedback to teachers about their classroom presence. By tracking how often and when teachers are at the board, they can identify areas of improvement in their teaching practices.
- Class Pace Adjustment: Using this technology, schools can analyze how much time a teacher spends at the board versus interacting with students. If the system detects an imbalance, it can prompt educators to adjust their pace, ensuring a more dynamic and inclusive learning environment.
- Lesson Effectiveness Assessment: By correlating student performance data with instances of the teacher being at the board, educational researchers can evaluate lesson effectiveness. This analysis can help in refining lesson plans to better suit student learning needs.
- Safety Compliance Monitoring: Schools can utilize this binary classification to ensure that teachers maintain a safe distance from board equipment during demonstrations. By monitoring the teacher's presence, the system can identify if necessary safety protocols are being followed in the classroom.
- Remote Learning Enhancement: For hybrid or remote learning environments, this function can help virtual classrooms maintain engagement. The system can analyze recorded sessions to evaluate whether teachers effectively interact with students when presenting materials, thus ensuring quality in remote education.