Identify student satisfaction by text
using AI
Below is a free classifier to identify student satisfaction by text. Just input your text, and our AI will predict student satisfaction levels across multiple categories - in just seconds.
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Get started
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("student-satisfaction-by-text-identifier", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/student-satisfaction-by-text-identifier/invoke', {
method: 'POST',
headers: {
'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
'Content-Type': 'application/json',
},
body: JSON.stringify(
{"data": "your_text_here"}
)
})
.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_text_here"}' \
https://www.nyckel.com/v1/functions/student-satisfaction-by-text-identifier/invoke
How this classifier works
To start, input the text that you'd like analyzed. Our AI tool will then predict student satisfaction levels across multiple categories.
This pretrained text model uses a Nyckel-created dataset and has 9 labels, including Very Dissatisfied and Dissatisfied.
We'll also show a confidence score (the higher the number, the more confident the AI model is around student satisfaction levels across multiple categories).
Whether you're just curious or building student satisfaction by text detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify student satisfaction by text at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Feedback Analysis: This use case involves gathering student feedback from various sources, such as course evaluations and online surveys. By identifying sentiments related to satisfaction, academic programs can be improved based on the insights gained from the text data.
- Course Improvement: Instructors can leverage the multilabel text classification function to analyze student comments on specific courses. This allows them to identify patterns in satisfaction levels related to teaching methods, course content, and engagement, leading to actionable insights for course enhancement.
- Program Assessment: Educational institutions can assess the overall satisfaction of students across different programs. Utilizing this function helps determine which programs consistently receive higher satisfaction ratings and which areas need attention for improvement.
- Early Warning System: By monitoring and classifying student feedback in real-time, institutions can create an early warning system for identifying dissatisfied students. This proactive approach enables timely intervention to address potential issues before they escalate.
- Alumni Engagement: Analyzing feedback from alumni regarding their satisfaction with their educational experience can provide valuable insights. This information can be used to enhance marketing strategies for recruitment and strengthen alumni relations, helping the institution build a supportive community.
- Resource Allocation: By understanding satisfaction levels in various departments or services (like library, counseling, or housing), administrators can allocate resources effectively. The multilabel classification can reveal where students feel underserved, allowing for more strategic funding and staffing decisions.
- Benchmarking: Institutions can use the insights gained from multilabel text classification to benchmark student satisfaction against industry standards or competitors. This comparative analysis can inform strategic planning and positioning, enabling institutions to enhance their offerings based on identified gaps.