breakpilot-lehrer/backend-lehrer/unit_analytics_models.py

"""
Unit Analytics API - Pydantic Models.

Data models for learning gains, stop performance, misconceptions,
student progress, class comparison, and export.
"""

from typing import List, Optional, Dict, Any
from datetime import datetime
from enum import Enum

from pydantic import BaseModel, Field


class TimeRange(str, Enum):
    """Time range for analytics queries"""
    WEEK = "week"
    MONTH = "month"
    QUARTER = "quarter"
    ALL = "all"


class LearningGainData(BaseModel):
    """Pre/Post learning gain data point"""
    student_id: str
    student_name: str
    unit_id: str
    precheck_score: float
    postcheck_score: float
    learning_gain: float
    percentile: Optional[float] = None


class LearningGainSummary(BaseModel):
    """Aggregated learning gain statistics"""
    unit_id: str
    unit_title: str
    total_students: int
    avg_precheck: float
    avg_postcheck: float
    avg_gain: float
    median_gain: float
    std_deviation: float
    positive_gain_count: int
    negative_gain_count: int
    no_change_count: int
    gain_distribution: Dict[str, int]
    individual_gains: List[LearningGainData]


class StopPerformance(BaseModel):
    """Performance data for a single stop"""
    stop_id: str
    stop_label: str
    attempts_total: int
    success_rate: float
    avg_time_seconds: float
    avg_attempts_before_success: float
    common_errors: List[str]
    difficulty_rating: float  # 1-5 based on performance


class UnitPerformanceDetail(BaseModel):
    """Detailed unit performance breakdown"""
    unit_id: str
    unit_title: str
    template: str
    total_sessions: int
    completed_sessions: int
    completion_rate: float
    avg_duration_minutes: float
    stops: List[StopPerformance]
    bottleneck_stops: List[str]  # Stops where students struggle most


class MisconceptionEntry(BaseModel):
    """Individual misconception tracking"""
    concept_id: str
    concept_label: str
    misconception_text: str
    frequency: int
    affected_student_ids: List[str]
    unit_id: str
    stop_id: str
    detected_via: str  # "precheck", "postcheck", "interaction"
    first_detected: datetime
    last_detected: datetime


class MisconceptionReport(BaseModel):
    """Comprehensive misconception report"""
    class_id: Optional[str]
    time_range: str
    total_misconceptions: int
    unique_concepts: int
    most_common: List[MisconceptionEntry]
    by_unit: Dict[str, List[MisconceptionEntry]]
    trending_up: List[MisconceptionEntry]  # Getting more frequent
    resolved: List[MisconceptionEntry]  # No longer appearing


class StudentProgressTimeline(BaseModel):
    """Timeline of student progress"""
    student_id: str
    student_name: str
    units_completed: int
    total_time_minutes: int
    avg_score: float
    trend: str  # "improving", "stable", "declining"
    timeline: List[Dict[str, Any]]  # List of session events


class ClassComparisonData(BaseModel):
    """Data for comparing class performance"""
    class_id: str
    class_name: str
    student_count: int
    units_assigned: int
    avg_completion_rate: float
    avg_learning_gain: float
    avg_time_per_unit: float


class ExportFormat(str, Enum):
    """Export format options"""
    JSON = "json"
    CSV = "csv"