🚀 Score Impact Analyzer

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Empower students to focus on the questions that matter most!

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🎯 Overview

Score Impact Analyzer is a smart tool for analyzing SAT/DSAT diagnostic test results. It identifies which questions in both English and Math would have contributed most to a student's score improvement, using official scoring models and adaptive test logic. Perfect for targeted study and actionable feedback!

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✨ Features

• 📊 Data-Driven Insights: Simulates score changes based on real student attempt data and official scoring models.
• 🏆 Impact Ranking: Ranks questions by their potential to improve the student's overall score.
• 📚 Subject-Specific Analysis: Separate analysis for Math and Reading & Writing.
• 🧠 Adaptive/Cascade What-If Analysis: Models the DSAT's adaptive module structure, showing how flipping Module 1 answers can change Module 2 assignment (easy/hard) and cause large score jumps.
• ⚙️ Configurable Threshold: Easily adjust the threshold for switching from "easy" to "hard" Module 2 (default: 50% correct in Module 1).
• 🗄️ MongoDB Integration: Uses MongoDB to store and manage student results and scoring data.

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⚡ Quick Start

1. Clone the Repository

   git clone <repository-url>
   cd score-impact-analyzer

2. Install Dependencies

   pip install -r requirements.txt

3. Start MongoDB
   • Make sure your MongoDB server is running on its default port (27017).
4. Run the Analyzer

   python main.py

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📁 Data Files

• scoring_DSAT_v2.json — SAT scoring model
• 67f2aae2c084263d16dbe462user_attempt_v2.json — Student 1 (anonymized)
• 66fece285a916f0bb5aea9c5user_attempt_v3.json — Student 2 (anonymized)

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🛠️ Usage

• The script will:
  • Connect to MongoDB and load the data
  • For each student, output:
    • The current score for each subject and the current Module 2 assignment (easy/hard)
    • A ranked list of the top five Module 1 questions that would have provided the most significant score increase if answered correctly, including whether flipping the answer would change the Module 2 assignment (cascade effect)

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🎬 Demo Output

--- Adaptive Analysis for Reading and Writing ---
Current Score: 650 (Module 2: hard)
Top 5 impactful Module 1 questions (cascade-aware):
1. QID: 659041da1d3470ce13e94642, Topic: Text structure and purpose, Score +20
2. QID: 659040581d3470ce13e94610, Topic: Text structure and purpose, Score +20
...

--- Adaptive Analysis for Math ---
Current Score: 520 (Module 2: easy)
Top 5 impactful Module 1 questions (cascade-aware):
1. QID: 659043271d3470ce13e94679, Topic: Advanced Math, Score +60 (Module 2 changes to hard)
2. QID: 659e991004e80b72d57ac8c2, Topic: Nonlinear functions, Score +20
...

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⚙️ Advanced Configuration

• Adjust the adaptive threshold in the code (main.py, default is 0.5, or 50% correct in Module 1).
• Ready to analyze both a high-performing and a regular student.

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💡 Extensions

• Analyze Module 2 questions
• Cluster by topic for targeted recommendations
• Run multiple what-if scenarios

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