rasch-pkg 1.0.1

Rasch modeli asosida o'quvchilarni baholash tizimi - Student assessment system based on Rasch model

Rasch Model Implementation

Rasch modeli asosida o'quvchilarni baholash tizimi. Bu loyiha o'quvchilarning imtihon natijalarini Rasch modeli yordamida tahlil qilish va baholash uchun mo'ljallangan.

Xususiyatlar

• Rasch modeli asosida baholash: O'quvchilarning skill level (θ) ni hisoblash
• Z-score va scaled score: Standartlashtirilgan ballni hisoblash (0-100 oralig'ida)
• Daraja belgilash: A+, A, B+, B, C+, C, NC darajalarini avtomatik belgilash
• Excel fayl bilan ishlash: Excel fayllardan ma'lumotlarni o'qish va natijalarni eksport qilish
• Statistik tahlil: Natijalar bo'yicha batafsil statistika
• Moslashuvchan konfiguratsiya: Daraja chegaralarini o'zgartirish imkoniyati

O'rnatish

PyPI dan o'rnatish (tavsiya etiladi)

pip install rasch-pkg

Repository dan o'rnatish

1. Repository ni klonlash:

git clone <repository-url>
cd rasch

2. Virtual muhit yaratish:

python3 -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

3. Bog'liqliklarni o'rnatish:

pip install -r requirements.txt

Foydalanish

1. Asosiy misol (fayllar bilan)

from rasch_pkg import RaschModel

# Rasch modeli obyektini yaratish
rasch = RaschModel()

# O'quvchi javoblari (1 - to'g'ri, 0 - noto'g'ri)
answers = [1, 1, 0, 1, 0, 1, 1, 0, 1, 1]  # 10 ta savol

# O'quvchini baholash
result = rasch.process_student_answers(
    student_id="001",
    name="Ahmadov Ahmad",
    answers=answers
)

print(f"Ball: {result.scaled_score:.2f}")
print(f"Daraja: {result.grade.value}")

2. PostgreSQL ma'lumotlar bazasi bilan ishlash

from rasch_pkg import RaschEvaluator

# Database konfiguratsiyasi
db_config = DatabaseConfig(
    host="localhost",
    port=5432,
    database="postgres",
    username="postgres",
    password="password"
)

# Context manager bilan ishlash
with DatabaseRaschProcessor(db_config) as processor:
    # Barcha foydalanuvchilarni qayta ishlash
    results = processor.process_all_users()

    # Excel fayliga eksport qilish
    excel_path = processor.export_to_excel(
        results,
        "matematika_test_natijalari.xlsx"
    )

    print(f"Natijalar {excel_path} fayliga saqlandi")

Excel fayl bilan ishlash

import pandas as pd
from src.rasch_model import RaschModel

# Excel faylni o'qish
df = pd.read_excel('exam_results.xlsx')

# Rasch modeli
rasch = RaschModel()

# Barcha o'quvchilarni baholash
results = rasch.process_multiple_students(df)

# Natijalarni eksport qilish
rasch.export_results(results, 'output_results.xlsx')

Ko'p o'quvchilarni baholash

# O'quvchilar ma'lumotlari
students_data = [
    {
        'id': '001',
        'name': 'O\'quvchi 1',
        'answers': [1, 1, 0, 1, 0, 1, 1, 0, 1, 1]
    },
    {
        'id': '002', 
        'name': 'O\'quvchi 2',
        'answers': [1, 0, 1, 1, 0, 0, 1, 1, 0, 1]
    }
]

results = rasch.process_multiple_students(students_data)

# Statistika
stats = rasch.get_statistics(results)
print(f"O'rtacha ball: {stats['score_statistics']['mean']}")

Daraja tizimi

Daraja	Ball oralig'i	Tavsif
A+	70.0 - 100.0	A'lo
A	65.0 - 69.99	A'lo
B+	60.0 - 64.99	Yaxshi
B	55.0 - 59.99	Yaxshi
C+	50.0 - 54.99	Qoniqarli
C	46.0 - 49.99	Qoniqarli
NC	0.0 - 45.99	Qoniqarsiz

Rasch modeli formulalari

1. Theta (θ) hisoblash:

   θ = ln(p / (1-p))
   

   Bu yerda p = to'g'ri javoblar nisbati

2. Z-score hisoblash:

   Z = (θ - μ) / σ
   

3. Scaled score hisoblash:

   Ball = 50 + 10 * Z
   

Loyiha tuzilishi

rasch/
├── src/
│   ├── __init__.py
│   ├── rasch_model.py          # Asosiy Rasch modeli klassi
│   └── database_rasch.py       # PostgreSQL bilan ishlash klassi
├── tests/
│   ├── __init__.py
│   ├── test_rasch_model.py     # Rasch modeli unit testlar
│   └── test_database_rasch.py  # Database unit testlar
├── examples/
│   ├── rasch_example.py        # Asosiy foydalanish misollari
│   ├── advanced_example.py     # Qo'shimcha misollar
│   └── database_example.py     # Database misollari
├── docs/
│   ├── rules.pdf               # Rasch modeli qoidalari
│   └── exam_answers_example_*.xlsx  # Misol Excel fayllari
├── output/                     # Natijalar papkasi
├── requirements.txt            # Python bog'liqliklar
├── README.md                   # Ushbu fayl
└── USAGE_GUIDE.md             # Foydalanish qo'llanmasi

Testlarni ishga tushirish

# Barcha testlar
python -m pytest tests/ -v

# Coverage bilan
python -m pytest tests/ --cov=src --cov-report=html

# Bitta test fayl
python -m unittest tests.test_rasch_model -v

Misollarni ishga tushirish

# Asosiy misollar
python examples/rasch_example.py

# Qo'shimcha misollar
python examples/advanced_example.py

# Database misollari
python examples/database_example.py

# Alohida misollar
python -c "from examples.rasch_example import example_1_single_student; example_1_single_student()"

API Hujjatlari

RaschModel klassi

Konstruktor

RaschModel(grade_thresholds=None, max_iterations=100, convergence_threshold=1e-6)

Asosiy metodlar

• process_student_answers(student_id, name, answers) - Bitta o'quvchini baholash
• process_multiple_students(data) - Ko'p o'quvchilarni baholash
• get_statistics(results) - Statistika hisoblash
• export_results(results, filename) - Natijalarni eksport qilish

Yordamchi metodlar

• calculate_theta(correct_answers, total_questions) - Theta hisoblash
• calculate_z_score(theta, mu, sigma) - Z-score hisoblash
• calculate_scaled_score(z_score) - Scaled score hisoblash
• determine_grade(scaled_score) - Daraja belgilash

StudentResult dataclass

O'quvchi natijasi uchun ma'lumotlar strukturasi:

@dataclass
class StudentResult:
    student_id: str
    name: str
    answers: List[Union[int, float]]
    correct_count: int
    total_questions: int
    raw_score: float
    theta: float
    mu: float
    sigma: float
    z_score: float
    scaled_score: float
    grade: GradeLevel

Konfiguratsiya

Maxsus daraja chegaralari

custom_thresholds = {
    'NC': (0.0, 39.99),
    'C': (40.0, 49.99),
    'C+': (50.0, 59.99),
    'B': (60.0, 69.99),
    'B+': (70.0, 79.99),
    'A': (80.0, 89.99),
    'A+': (90.0, 100.0)
}

rasch = RaschModel(grade_thresholds=custom_thresholds)

Xatoliklar va yechimlar

Keng uchraydigan xatoliklar

1. Excel fayl topilmadi

   • Fayl yo'lini tekshiring
   • Fayl mavjudligini tasdiqlang

2. Noto'g'ri javob formati

   • Javoblar 0 yoki 1 bo'lishi kerak
   • Boshqa qiymatlar avtomatik 0 ga aylantiriladi

3. Bo'sh ma'lumotlar

   • Kamida bitta javob bo'lishi kerak
   • Bo'sh ro'yxatlar xatolikka olib keladi

Hissa qo'shish

1. Fork qiling
2. Feature branch yarating (git checkout -b feature/yangi-xususiyat)
3. O'zgarishlarni commit qiling (git commit -am 'Yangi xususiyat qo'shildi')
4. Branch ni push qiling (git push origin feature/yangi-xususiyat)
5. Pull Request yarating

Litsenziya

MIT License

Muallif

Rasch modeli implementatsiyasi - O'zbekiston Milliy Universiteti