Family Coverage

Element Details

{
  "atoms": [
    {
      "_inventory_only": true,
      "atom_id": "L01-MML-LEARNERPREFERE",
      "atom_name": "Student Preference Data Collection",
      "atomic_definition": "Evaluates whether the system possesses a record of the student\u0027s stated (explicit) or demonstrated (implicit) preference for specific instructional modalities, creating the data basis for agency-oriented adaptation.",
      "definition": "Evaluates whether the system possesses a record of the student\u0027s stated (explicit) or demonstrated (implicit) preference for specific instructional modalities, creating the data basis for agency-oriented adaptation.",
      "family_code": "MML",
      "family_name": "MML",
      "internal_id": "MML01",
      "source_file": "L01-MML-LEARNERPREFERE.json",
      "weight_estimation": {}
    }
  ],
  "compounds": [
    {
      "_inventory_only": true,
      "calls_elements": [
        "E-CS-CS-BUNDLE",
        "E-CS-EF-BUNDLE",
        "E-CS-GEN-BUNDLE",
        "E-CS-MAI-BUNDLE",
        "E-CS-MC-BUNDLE",
        "E-CS-MML-BUNDLE",
        "E-CS-PRC-BUNDLE",
        "E-CS-REP-BUNDLE",
        "E-CS-TLD-BUNDLE"
      ],
      "compound_id": "CP-CS",
      "compound_name": "CogniStruct Agent Master Compound",
      "definition": "",
      "execution_steps": [],
      "implements_construct": "K-CS",
      "inputs": {},
      "key_atoms": [],
      "outputs": {},
      "purpose": ""
    }
  ],
  "constructs": [
    {
      "_inventory_only": true,
      "compounds_managed": [],
      "construct_id": "K-CS",
      "construct_name": "CogniStruct Agent Master Construct",
      "definition": "",
      "purpose": "",
      "validation_rules": {}
    }
  ],
  "elements": [
    {
      "_inventory_only": true,
      "aggregation_method": {
        "confidence_propagation": "minimum",
        "missing_handling": "flag_incomplete",
        "strategy": "weighted_mean"
      },
      "atom_ids": [
        {
          "atom_id": "C1",
          "measures": "structure_preference"
        },
        {
          "atom_id": "C3",
          "measures": "ambiguity_tolerance"
        },
        {
          "atom_id": "C4",
          "measures": "guidance_dependency"
        },
        {
          "atom_id": "C6",
          "measures": "environmental_sensitivity"
        },
        {
          "atom_id": "C7",
          "measures": "routine_preference"
        },
        {
          "atom_id": "C8",
          "measures": "flexibility"
        },
        {
          "atom_id": "C9",
          "measures": "organization_level"
        },
        {
          "atom_id": "C10",
          "measures": "planning_tendency"
        }
      ],
      "element_id": "E-CS-CS-BUNDLE",
      "element_name": "E-CS-CS-BUNDLE",
      "family_code": "CS",
      "family_name": "CS",
      "output_contract": {
        "returns": {
          "confidence": "0.0-1.0",
          "context_evidence": "0-100 aggregate",
          "flexibility_index": "0-100 score",
          "structure_need": "0-100 score"
        }
      },
      "purpose": "Evidence aggregator for contextual and structural learning environment factors."
    },
    {
      "_inventory_only": true,
      "aggregation_method": {
        "confidence_propagation": "minimum",
        "missing_handling": "use_default_profile",
        "strategy": "weighted_mean"
      },
      "atom_ids": [
        {
          "atom_id": "E1",
          "measures": "error_sensitivity"
        },
        {
          "atom_id": "E2",
          "measures": "feedback_seeking"
        },
        {
          "atom_id": "E3",
          "measures": "error_correction_rate"
        },
        {
          "atom_id": "E6",
          "measures": "feedback_timing_preference"
        },
        {
          "atom_id": "E8",
          "measures": "corrective_feedback_response"
        }
      ],
      "element_id": "E-CS-EF-BUNDLE",
      "element_name": "E-CS-EF-BUNDLE",
      "family_code": "EF",
      "family_name": "EF",
      "output_contract": {
        "returns": {
          "confidence": "0.0-1.0",
          "error_tolerance": "low/moderate/high",
          "feedback_evidence": "0-100 aggregate",
          "feedback_preference": "immediate/delayed"
        }
      },
      "purpose": "Evidence aggregator for error patterns and feedback processing preferences."
    },
    {
      "_inventory_only": true,
      "aggregation_method": {
        "confidence_propagation": "minimum",
        "missing_handling": "use_defaults",
        "strategy": "weighted_mean"
      },
      "atom_ids": [
        {
          "atom_id": "L01-GEN-ATOME10",
          "measures": "general_engagement"
        },
        {
          "atom_id": "L01-GEN-ATOMICMICROAGE",
          "measures": "micro_age_context"
        },
        {
          "atom_id": "L01-GEN-ATOMP1",
          "measures": "general_processing"
        }
      ],
      "element_id": "E-CS-GEN-BUNDLE",
      "element_name": "E-CS-GEN-BUNDLE",
      "family_code": "GEN",
      "family_name": "GEN",
      "output_contract": {
        "returns": {
          "confidence": "0.0-1.0",
          "general_evidence": "0-100 aggregate",
          "system_status": "ready/degraded/unavailable"
        }
      },
      "purpose": "Evidence aggregator for general system-level student attributes."
    },
    {
      "_inventory_only": true,
      "aggregation_method": {
        "confidence_propagation": "minimum",
        "missing_handling": "impute_neutral",
        "strategy": "weighted_mean"
      },
      "atom_ids": [
        {
          "atom_id": "A1",
          "measures": "intrinsic_motivation"
        },
        {
          "atom_id": "A2",
          "measures": "extrinsic_motivation"
        },
        {
          "atom_id": "A3",
          "measures": "self_efficacy"
        },
        {
          "atom_id": "A4",
          "measures": "anxiety_level"
        },
        {
          "atom_id": "A5",
          "measures": "goal_orientation"
        },
        {
          "atom_id": "A6",
          "measures": "persistence"
        },
        {
          "atom_id": "A7",
          "measures": "curiosity"
        },
        {
          "atom_id": "A8",
          "measures": "frustration_tolerance"
        },
        {
          "atom_id": "A9",
          "measures": "engagement_level"
        },
        {
          "atom_id": "A10",
          "measures": "interest_alignment"
        }
      ],
      "element_id": "E-CS-MAI-BUNDLE",
      "element_name": "E-CS-MAI-BUNDLE",
      "family_code": "MAI",
      "family_name": "MAI",
      "output_contract": {
        "returns": {
          "affect_baseline": "anxiety and efficacy levels",
          "confidence": "0.0-1.0",
          "mai_evidence": "0-100 aggregate",
          "motivation_profile": "structured motivation data"
        }
      },
      "purpose": "Evidence aggregator for motivation and affective states in theory matching."
    },
    {
      "_inventory_only": true,
      "aggregation_method": {
        "confidence_propagation": "minimum",
        "missing_handling": "flag_for_assessment",
        "strategy": "weighted_mean"
      },
      "atom_ids": [
        {
          "atom_id": "M1",
          "measures": "planning_capability"
        },
        {
          "atom_id": "M2",
          "measures": "monitoring_accuracy"
        },
        {
          "atom_id": "M4",
          "measures": "self_evaluation"
        },
        {
          "atom_id": "M5",
          "measures": "strategy_selection"
        },
        {
          "atom_id": "M6",
          "measures": "goal_setting"
        },
        {
          "atom_id": "M7",
          "measures": "reflection_depth"
        },
        {
          "atom_id": "M8",
          "measures": "calibration_accuracy"
        },
        {
          "atom_id": "M9",
          "measures": "adaptive_control"
        }
      ],
      "element_id": "E-CS-MC-BUNDLE",
      "element_name": "E-CS-MC-BUNDLE",
      "family_code": "MC",
      "family_name": "MC",
      "output_contract": {
        "returns": {
          "confidence": "0.0-1.0",
          "metacognitive_evidence": "0-100 aggregate",
          "monitoring_accuracy": "0-100 score",
          "self_regulation_level": "low/moderate/high"
        }
      },
      "purpose": "Evidence aggregator for metacognitive awareness and self-regulation capabilities."
    },
    {
      "_inventory_only": true,
      "aggregation_method": {
        "confidence_propagation": "direct",
        "missing_handling": "use_balanced_defaults",
        "strategy": "passthrough"
      },
      "atom_ids": [
        {
          "atom_id": "MML01",
          "measures": "modality_preference_profile"
        }
      ],
      "element_id": "E-CS-MML-BUNDLE",
      "element_name": "E-CS-MML-BUNDLE",
      "family_code": "MML",
      "family_name": "MML",
      "output_contract": {
        "returns": {
          "accessibility_needs": "list of accommodations",
          "channel_weights": {
            "auditory": "0-100",
            "kinesthetic": "0-100",
            "text": "0-100",
            "visual": "0-100"
          },
          "confidence": "0.0-1.0",
          "modality_evidence": "channel weight distribution"
        }
      },
      "purpose": "Evidence aggregator for multimodal channel preferences and accessibility needs."
    },
    {
      "_inventory_only": true,
      "aggregation_method": {
        "confidence_propagation": "minimum",
        "missing_handling": "impute_conservative",
        "strategy": "weighted_mean"
      },
      "atom_ids": [
        {
          "atom_id": "P2",
          "measures": "processing_speed"
        },
        {
          "atom_id": "P3",
          "measures": "working_memory_capacity"
        },
        {
          "atom_id": "P4",
          "measures": "cognitive_load_current"
        },
        {
          "atom_id": "P5",
          "measures": "cognitive_load_max"
        },
        {
          "atom_id": "P8",
          "measures": "attention_span"
        },
        {
          "atom_id": "P9",
          "measures": "information_integration"
        }
      ],
      "element_id": "E-CS-PRC-BUNDLE",
      "element_name": "E-CS-PRC-BUNDLE",
      "family_code": "PRC",
      "family_name": "PRC",
      "output_contract": {
        "returns": {
          "available_capacity": "0-100 estimate",
          "confidence": "0.0-1.0",
          "load_capacity_ratio": "0.0-1.0",
          "processing_evidence": "0-100 aggregate"
        }
      },
      "purpose": "Evidence aggregator for cognitive processing speed and working memory capacity."
    },
    {
      "_inventory_only": true,
      "aggregation_method": {
        "confidence_propagation": "minimum",
        "missing_handling": "impute_baseline",
        "strategy": "weighted_mean"
      },
      "atom_ids": [
        {
          "atom_id": "R1",
          "measures": "schema_integration"
        },
        {
          "atom_id": "R3",
          "measures": "prior_knowledge_depth"
        },
        {
          "atom_id": "R4",
          "measures": "concept_connectivity"
        },
        {
          "atom_id": "R6",
          "measures": "abstraction_level"
        },
        {
          "atom_id": "R7",
          "measures": "example_preference"
        },
        {
          "atom_id": "R8",
          "measures": "transfer_capability"
        },
        {
          "atom_id": "R9",
          "measures": "misconception_density"
        },
        {
          "atom_id": "R10",
          "measures": "knowledge_organization"
        }
      ],
      "element_id": "E-CS-REP-BUNDLE",
      "element_name": "E-CS-REP-BUNDLE",
      "family_code": "REP",
      "family_name": "REP",
      "output_contract": {
        "returns": {
          "confidence": "0.0-1.0",
          "knowledge_depth": "novice/intermediate/expert",
          "representation_evidence": "0-100 aggregate",
          "schema_readiness": "0-100 score"
        }
      },
      "purpose": "Evidence aggregator for knowledge representation and schema structure."
    },
    {
      "_inventory_only": true,
      "aggregation_method": {
        "confidence_propagation": "minimum",
        "missing_handling": "use_population_baseline",
        "strategy": "weighted_mean"
      },
      "atom_ids": [
        {
          "atom_id": "T1",
          "measures": "learning_pace_preference"
        },
        {
          "atom_id": "T2",
          "measures": "session_duration_optimal"
        },
        {
          "atom_id": "T3",
          "measures": "spacing_interval"
        },
        {
          "atom_id": "T4",
          "measures": "retention_decay_rate"
        },
        {
          "atom_id": "T5",
          "measures": "review_frequency"
        },
        {
          "atom_id": "T6",
          "measures": "time_on_task"
        },
        {
          "atom_id": "T7",
          "measures": "learning_curve_slope"
        },
        {
          "atom_id": "T8",
          "measures": "plateau_tendency"
        },
        {
          "atom_id": "T9",
          "measures": "recovery_time"
        },
        {
          "atom_id": "T10",
          "measures": "peak_performance_time"
        }
      ],
      "element_id": "E-CS-TLD-BUNDLE",
      "element_name": "E-CS-TLD-BUNDLE",
      "family_code": "TLD",
      "family_name": "TLD",
      "output_contract": {
        "returns": {
          "confidence": "0.0-1.0",
          "pacing_profile": "slow/moderate/fast",
          "spacing_recommendation": "massed/distributed/expanding",
          "temporal_evidence": "0-100 aggregate"
        }
      },
      "purpose": "Evidence aggregator for time-based learning patterns and pacing preferences."
    }
  ],
  "metadata": {
    "agent": "cognistruct",
    "generated_at": "",
    "generator": "CogniStruct Operator Inference v3",
    "layer": "L01",
    "synced_at": "2026-04-11T17:56:32.000965+00:00"
  },
  "summary": {
    "compounds": 1,
    "constructs": 1,
    "duplicates_resolved": 0,
    "total_atoms": 1,
    "total_elements": 9,
    "validation_passed": true
  },
  "validation": {
    "family_coverage": {
      "MML": [
        "CP-CS",
        "CP-LODA",
        "CP-LOPA"
      ]
    },
    "issues": [],
    "passed": true
  }
}