Roadblock Detection Classifier (v1)

🔍 Overview

The Roadblock Classifier detects whether a check-in contains signs of a roadblock or struggle.

This helps identify when students need support or intervention.

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🎯 Purpose

Students may express confusion, frustration, or blockers in their check-ins. This model identifies those signals automatically.

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🧠 Model Details

• Task: Binary Text Classification
• Labels:
  • ROADBLOCK → Indicates confusion, issue, or blocker
  • NOT_ROADBLOCK → No issues present
• Architecture: Transformer-based (fine-tuned)

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📦 Use Cases

• Early detection of struggling students
• Automated support systems
• Mentor alerts
• Educational analytics

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🧪 Example

Input

I was struggling to understand why my API deployment kept failing

Output

{
  "label": "ROADBLOCK",
  "confidence": 0.99
}

Input

Today I completed my feature and everything is working well

Output

{
  "label": "NOT_ROADBLOCK",
  "confidence": 0.97
}

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Pipeline Integration

This model runs after check-in validation and quality analysis:

1. Check-in or Not
2. Check-in Detail
3. Roadblock Detection (this model)
4. Intervention or generation

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Usage (Python)

from transformers import pipeline

classifier = pipeline("text-classification", model="mjpsm/roadblock-classifier-v1")

result = classifier("I was confused about my API deployment")
print(result)

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Performance

• High accuracy and F1 score (~0.97–0.99 observed during evaluation)

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Future Improvements

1. Detect severity of roadblocks
2. Multi-label emotions (confusion, frustration, stuck)
3. Suggest solutions automatically

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👤 Author

• Mazamesso Meba
• Information Systems Student | AI Developer