Identify how many hands are raised in class
using AI
Below is a free classifier to identify how many hands are raised in class. Just upload your image, and our AI will predict how many hands are raised in class - in just seconds.
API Access
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("how-many-hands-are-raised-in-class", "your_image_url", credentials)
fetch('https://www.nyckel.com/v1/functions/how-many-hands-are-raised-in-class/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/how-many-hands-are-raised-in-class/invoke
How this classifier works
To start, upload your image. Our AI tool will then predict how many hands are raised in class.
This pretrained image model uses a Nyckel-created dataset and has 21 labels, including 0 Hands Raised, 1 Hand Raised, 2 Hands Raised, 3 Hands Raised, 4 Hands Raised, 5 Hands Raised, 6 Hands Raised, 7 Hands Raised, 8 Hands Raised and 9 Hands Raised.
We'll also show a confidence score (the higher the number, the more confident the AI model is around how many hands are raised in class).
Whether you're just curious or building how many hands are raised in class detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify how many hands are raised in class at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Classroom Engagement Analysis: This function can be used to evaluate student engagement during lectures by counting the number of raised hands. Educators can leverage this data to identify which topics captivate students' attention and which may require more interactive methods.
- Adaptive Learning Systems: Integrating the hand-raising identifier into adaptive learning platforms allows for real-time feedback to adjust lesson plans. When more students raise their hands, the system can quickly provide additional resources or change the pace to match student interest levels.
- Teaching Effectiveness Evaluation: Schools can employ this function to assess the effectiveness of different teaching styles and methods. By analyzing the hand-raising data, administrators can identify patterns and provide tailored training for instructors to enhance classroom interaction.
- Student Participation Tracking: This function can help track individual student participation over time. By recording hand-raising frequency, teachers can identify students who may need additional encouragement or support in contributing to discussions.
- Event Planning and Feedback: Organizers of workshops and seminars can utilize this function to gauge audience engagement during sessions. By counting raised hands, they can assess participant interest and adapt future programming or training offerings accordingly.
- Classroom Management: Teachers can use real-time hand count data to manage classroom dynamics effectively. If too few hands are raised, the instructor may decide to employ strategies to stimulate discussion or assess comprehension before moving forward.
- Research in Educational Technology: Researchers can analyze hand-raising data in studies related to educational technology effectiveness. By quantifying engagement levels, insights can be generated about how technology impacts student interaction and learning outcomes in real instructional settings.