Is This Advisor a Trap? Calculator Version

Scientific evaluation of advisor comprehensive strength, helping you make informed academic choices. Scientifically compare multiple advisors to help you avoid problematic supervisors.

Detailed-Analysis:

🎯 Product Features

🔍 New 20-Dimension Evaluation System

Personality Assessment: Advisor's character, communication skills, management style, student-advisor relationship.
Academic Capability: Research strength, academic reputation, career prospects, research funding.
Work Environment: Work-life balance, lab conditions, geographical location, research group size.
Career Development: Graduation difficulty, internship policy, salary and benefits, peer relationships.

🎚️ Smart Weight System

Master's Recommendation: School 60% | Advisor 40%
PhD Recommendation: School 30% | Advisor 70%
Postdoc Recommendation: School 20% | Advisor 80%
Manual Adjustment: Supports personalized weight configuration.
Smart Tips: Detailed explanations of weight definitions.

📊 Intelligent Analysis Report

Sub-score Visualization: Personality score, academic score, treatment score, prospect score.
Precise Risk Identification: Automatically identifies all specific evaluation metrics scoring below 3 points.
Personalized Advantage Analysis: Highlights excellent performance (4-5 points).
Targeted Suggestions: Decision guidance based on specific risk points.
Collapsible Detailed Report: Full analysis can be expanded.

💾 Comprehensive Data Management

Import/Export Functionality: JSON format data backup.
Advisor Nickname System: Supports pseudonyms for privacy protection.
Local Storage: Data is secure and not uploaded to servers.
Version Control: Data files include version information.

🎨 Excellent User Experience

Descriptive Scoring: Intuitive text descriptions (e.g., "996/007") instead of numbers.
Responsive Design: Perfect support for desktop and mobile devices.
Real-time Calculation: Instantaneous score and suggestion updates.
Multi-Advisor Comparison: Supports simultaneous evaluation of up to 3 advisors.
Accessibility Design: Supports keyboard navigation and screen readers.

🚀 Quick Start

Environment Requirements

Node.js 16+
npm/yarn/pnpm/bun

Installation and Running

# Clone the repository
git clone https://github.com/ktwu01/advisor-calculator.git
cd advisor-calculator

# Install dependencies
npm install

# Start the development server
npm run dev

Visit http://localhost:3000 to view the application.

Deployment

# Build for production
npm run build

# Start the production server
npm start

📋 Detailed Usage Guide

1. Basic Information Setup

Advisor Nickname: Use a pseudonym (e.g., "Prof. X") for easy identification and data management.
Advisor Gender: Influences management style weight calculation.
Age Range: Young/Mid-career/Senior faculty, influences experience assessment.
Advisor Title: From Assistant/Associate Prof to Academician, automatically adjusts academic weights.
School Level: 7 levels from Community College to Ivy League / Top Tier Research University.
Degree Program: Automatically adjusts weight configuration after selection.

2. 20 Evaluation Metrics Explained

Personality Dimension (4 items)

Advisor's character, communication skills, management style, student-advisor relationship.

Academic Dimension (4 items)

Research strength, academic reputation, career prospects, research funding.

Work Dimension (6 items)

Work-life balance, research group funding, lab conditions, geographical location, research group size, gender ratio.

Development Dimension (6 items)

Graduation difficulty, mentoring frequency, internship policy, salary and benefits, living costs, peer relationships.

3. Intelligent Evaluation System

Real-time Calculation: Results update immediately after each rating.
Decimal Precision: All scores displayed to one decimal place.
Level Assessment: Excellent Advisor, Good Advisor, Average, Somewhat Problematic, Major Red Flags.

4. Detailed Analysis Report

Basic Information

Total score and level assessment.
Current weight configuration display.

Sub-scores

Personality score, academic score, treatment score, prospect score.
2x2 grid layout, color-coded.

Detailed Analysis (Collapsible)

Main Advantages: High-scoring metrics and sub-category advantages.
Potential Risks: Detailed listing of all metrics scoring below 3 points.
Personalized Suggestions: Targeted guidance based on specific problem areas.

5. Data Management

Export Data: Saves as a JSON file, including a timestamp.
Import Data: Restores previous evaluation data.
Multi-Advisor Comparison: Supports simultaneous evaluation of up to 3 advisors.

🛠️ Technical Architecture

Frontend Technology Stack

Framework: Next.js 15 + TypeScript
UI Library: shadcn/ui (Radix UI + Tailwind CSS)
Icons: Lucide React
Styling: Tailwind CSS
Components: Collapsible panels, tooltips, etc.

Core Algorithm

Smart Weight System: Dynamic weights based on degree type and advisor title.
Risk Identification Algorithm: Comprehensive detection of low-scoring metrics and generation of personalized risk reports.
Advantage Analysis Algorithm: Multi-level advantage identification and deduplication.
Suggestion Generation Algorithm: Targeted suggestion system based on specific issues.

Data Processing

Local Storage: Uses localStorage for visit statistics.
File Operations: JSON format import/export.
Real-time Calculation: Responsive calculation based on React state.

📦 Project Structure

advisor-calculator/
├── README.md, README.CN.md          # Project Documentation
├── assets/                          # Assets
│   ├── Banner-advisor-calculator.png
│   └── todo.md                     # Development Log
├── src/
│   ├── app/
│   │   ├── page.tsx                # Main Application Component
│   │   ├── layout.tsx              # Application Layout
│   │   └── globals.css             # Global Styles
│   ├── components/ui/              # UI Component Library
│   │   ├── badge.tsx, button.tsx, card.tsx
│   │   ├── collapsible.tsx         # Collapsible Component
│   │   ├── input.tsx, label.tsx, select.tsx
│   │   ├── slider.tsx, tooltip.tsx
│   └── lib/
│       └── utils.ts                # Utility Functions
├── tailwind.config.ts              # Tailwind Configuration
├── components.json                 # shadcn/ui Configuration
└── deploy/                         # Deployment Configuration
    └── netlify.toml

🔬 Algorithm Features

Precise Risk Identification

Comprehensive Coverage: Detects items scoring <3 points across all 20 evaluation metrics.
Intelligent Summary: If ≤3 items, lists them; if >3 items, shows "first 3 + total count".
Special Warnings: Specific checks for critical metrics (e.g., 996/007, graduation difficulty).
Layered Analysis: Specific metric risks + sub-score risks.

Personalized Suggestion System

High Score Range (≥80): Highly recommended.
Mid-High Score (70-79): Generally recommended.
Mid Score Range (60-69): Specific attention to risk points advised.
Low Score Range (<60): Detailed listing of major issues.

Multi-Dimensional Weight Algorithm

Base Weights: Preset weights based on degree type.
Title Bonus: Academician, Distinguished Chair, etc., provide academic weight bonuses.
School Influence: 7 levels of school prestige provide brand weight bonuses.
Gender and Age: Subtle adjustments based on management experience.

🤝 Contribution Guide

Development Workflow

Fork this project.
Create your feature branch (git checkout -b feature/AmazingFeature).
Commit your changes (git commit -m 'Add some AmazingFeature').
Push to the branch (git push origin feature/AmazingFeature).
Open a Pull Request.

Code Standards

Use TypeScript for type checking.
Follow ESLint + Biome code standards.
Components use functional programming.
Use Tailwind CSS for styling.

Testing Requirements

Ensure all functionalities work correctly.
Test various scoring combinations.
Verify import/export functions.
Check responsive layout.

📄 License

This project is licensed under the CC BY-NC-ND 4.0 License.

✅ Allows download, use, and sharing.
❌ Prohibits commercial use.
❌ Prohibits modifications and adaptations.

⚠️ Disclaimer

Reference Tool: This tool is for reference only. Please make rational choices based on actual circumstances.
Privacy Protection: Data is stored locally only and not uploaded to servers.
Subjective Evaluation: Evaluation results are based on subjective judgment and do not represent absolute accuracy.
Decision Responsibility: Final decision responsibility rests solely with the user.

🔗 Related Links

🎉 Changelog

v2.1.0 Latest Version

✅ English, Chinese, Spanish, French, Japanese 5-Language Support
✅ New 20-Dimension Evaluation System
✅ Smart Risk Identification Algorithm
✅ Collapsible Detailed Analysis Report
✅ Descriptive Scoring Interface
✅ Complete Import/Export Functionality
✅ Multi-Advisor Comparison System
✅ Personalized Weight Configuration

Historical Versions

v2.0.0: Added smart weight system and data management.
v1.5.0: New economic dimension evaluation.
v1.0.0: Basic evaluation system launched.

If this project is helpful to you, please give it a ⭐ Star!

May every student find their ideal advisor and avoid pitfalls on their academic journey! 🎓

Name		Name	Last commit message	Last commit date
Latest commit History 42 Commits
assets		assets
deploy		deploy
public		public
scripts		scripts
src		src
.gitignore		.gitignore
LICENSE		LICENSE
PROJECT_STRUCTURE.md		PROJECT_STRUCTURE.md
README.CN.md		README.CN.md
README.es.md		README.es.md
README.fr.md		README.fr.md
README.ja.md		README.ja.md
README.md		README.md
TODO.md		TODO.md
biome.json		biome.json
components.json		components.json
eslint.config.mjs		eslint.config.mjs
next.config.js		next.config.js
package-lock.json		package-lock.json
package.json		package.json
postcss.config.mjs		postcss.config.mjs
tailwind.config.ts		tailwind.config.ts
tsconfig.json		tsconfig.json

License

ktwu01/advisor-calculator

Folders and files

Latest commit

History

Repository files navigation