About Prompt
- Prompt Type – Dynamic
- Prompt Platform – ChatGPT, Grok, Deepseek, Gemini, Copilot, Midjourney, Meta AI and more
- Niche – At-Risk Student Identification
- Language – English
- Category – Student Retention Analytics
- Prompt Title – AI Prompt for Predicting Student Dropout Risk Using Learning Patterns
Prompt Details
**Prompt Goal:** Predict the risk of a student dropping out based on their learning patterns and other relevant data. This prompt is designed for dynamic adaptation based on available data and intended for use across various AI platforms.
**Prompt Type:** Dynamic, adaptable to different data structures and AI models.
**Target Audience:** Student Retention Analytics teams, educational institutions, and researchers.
**Prompt Structure:**
This prompt uses a modular structure, allowing you to customize it based on the available data and the specific AI model you are using. Include the relevant sections and replace the placeholders with actual data.
**Section 1: Core Prompt**
“`
Predict the likelihood of a student dropping out within [Timeframe: e.g., the next semester, the next year] based on the provided learning patterns and other relevant data. Output the prediction as a probability (between 0 and 1) and a risk level (e.g., Low, Medium, High). Explain the key factors contributing to the prediction. Consider [Specific factors to consider: e.g., the student’s academic history, engagement patterns, demographics, socioeconomic background].
“`
**Section 2: Student Information (Required)**
“`
Student ID: [Student’s unique identifier]
Current Academic Year/Level: [e.g., Sophomore, 10th Grade]
Program/Major: [e.g., Computer Science, General Studies]
“`
**Section 3: Academic Performance Data (Highly Recommended)**
“`
GPA: [Student’s current GPA]
Grades in Key Courses: {
[Course Name 1]: [Grade],
[Course Name 2]: [Grade],
…
}
Attendance Rate: [Percentage of classes attended]
Number of Failed Courses: [Number of courses failed]
Test Scores: {
[Test Name 1]: [Score],
[Test Name 2]: [Score],
…
}
“`
**Section 4: Engagement Data (Recommended)**
“`
Library Usage: [Frequency of library visits, resources accessed]
Learning Management System (LMS) Activity: {
Login Frequency: [Number of logins per week/month],
Time Spent on LMS: [Average time spent on the LMS per week/month],
Forum Participation: [Number of posts/replies in online forums],
Assignment Submission Patterns: [Timeliness of assignment submissions, patterns of late submissions],
Resource Access: [Types and frequency of accessing learning resources]
}
Advisor Meetings: [Frequency of meetings with academic advisors]
Extracurricular Activities: [Participation in clubs, sports, or other activities]
“`
**Section 5: Demographic and Socioeconomic Data (Optional, use ethically and responsibly)**
“`
Age: [Student’s age]
Gender: [Student’s gender]
Ethnicity: [Student’s ethnicity]
First-Generation College Student: [Yes/No]
Socioeconomic Status Indicators: [e.g., Pell Grant recipient, free/reduced lunch eligibility (if applicable)]
“`
**Section 6: Historical Data (If Available)**
“`
Past Dropout Risk Predictions: [Previous predictions and their associated timeframes]
Intervention History: [Details of any past interventions and their outcomes]
Reasons for Past Academic Struggles (if documented): [e.g., documented learning disabilities, personal challenges]
“`
**Section 7: Output Format (Important)**
“`
Provide the output in JSON format:
“`json
{
“student_id”: “[Student’s unique identifier]”,
“dropout_probability”: [Probability between 0 and 1],
“risk_level”: [“Low”, “Medium”, “High”],
“key_factors”: [“List of key factors contributing to the prediction”],
“explanation”: “[Detailed explanation of the prediction, including the influence of different factors]”
}
“`
**Prompt Usage Instructions:**
1. **Select Relevant Sections:** Choose the sections relevant to the available data. The Student Information section is required.
2. **Replace Placeholders:** Replace the bracketed placeholders with actual student data.
3. **Adapt for Specific AI Models:** Modify the prompt’s phrasing and structure as needed to be compatible with the specific AI platform’s capabilities. For example, some platforms may benefit from more structured data input, while others might handle free-text descriptions effectively.
4. **Ethical Considerations:** Be mindful of privacy and ethical implications when using sensitive data like demographic or socioeconomic information. Ensure compliance with relevant regulations and guidelines.
This dynamic prompt empowers you to leverage AI for effective student retention analytics by providing a flexible and adaptable framework for predicting student dropout risk. Remember to evaluate the model’s performance regularly and refine the prompt based on feedback and evolving needs.