{
  "format": "dataset-manifest/1",
  "slug": "learning-disabilities-support-software",
  "version": 3,
  "seed": "learning-disabilities-support-software:1",
  "engine": 1,
  "engineVersion": "1.0.0",
  "spec": {
    "format": "dataset/1",
    "slug": "learning-disabilities-support-software",
    "name": "Learning Progress Insights",
    "tagline": "Student performance metrics, learning goals, assessments, and materials tailored for learning disabilities.",
    "description": "Specialists support students with learning disabilities, tailoring their educational experiences to meet individual needs. In this dedicated environment, a variety of learning materials and interactive tools are employed to engage students effectively. Each student has a unique diagnosis that influences their learning journey, and progress is monitored through frequent assessments and recorded performance metrics. These assessments capture how well students interact with exercises and track their milestones, creating a comprehensive picture of educational engagement over time.\n\nThe dataset consists of several interconnected tables: 'students' provides insights into individual learners, including their diagnoses and personal details. Each student is linked to specific 'learning_goals' that outline their objectives, helping to customize the educational approach. Performance is tracked through 'performance_metrics', which detail assessable data points that reflect students' progress across various dimensions. The 'assessments' table records information about the evaluations conducted, while 'learning_materials' lists the resources available for each student, supporting their tailored learning experience.\n\nBy analyzing the relationships among these tables, developers can explore questions regarding student performance trends, the effectiveness of different learning materials, and the impact of specific learning goals on student engagement. The dataset is particularly useful for practicing SQL queries, understanding joins between tables, and developing analytical skills focused on educational data. The data contained within this ecosystem is synthetic, designed to provide a realistic yet controlled environment for practice and exploration.",
    "domain": "customer-support",
    "shape": "time-series",
    "useCases": [
      "analytics-demo",
      "bi-dashboards"
    ],
    "keywords": [
      "learning disabilities",
      "student progress",
      "educational assessment",
      "performance metrics",
      "support strategies"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "students": {
        "description": "This table contains information about students with learning disabilities. Each row represents a unique student and includes details such as their name, diagnosis, and contact information.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each student."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The full name of the student."
          },
          "diagnosis": {
            "kind": "enum",
            "values": [
              "Dyslexia",
              "ADHD",
              "Dyscalculia",
              "Autism Spectrum Disorder",
              "Others"
            ],
            "description": "The learning disability diagnosis of the student.",
            "weights": [
              5,
              3,
              2,
              1,
              4
            ]
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "The email address of the student for communication."
          },
          "enrollment_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The date the student was enrolled.",
            "trend": "growing"
          }
        }
      },
      "learning_goals": {
        "description": "This table outlines the specific learning goals set for each student. Each goal is linked to a student and includes a description and target completion date.",
        "rows": {
          "per": "students",
          "via": "student_id",
          "min": 1,
          "max": 5,
          "skew": "long-tail"
        },
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each learning goal."
          },
          "student_id": {
            "kind": "fk",
            "ref": "students.id",
            "description": "The ID of the student associated with this goal."
          },
          "goal_description": {
            "kind": "sentence",
            "description": "Description of the learning goal."
          },
          "target_completion_date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "The date by which the goal should be achieved.",
            "trend": "seasonal"
          },
          "goal_priority": {
            "kind": "int",
            "min": 1,
            "max": 5,
            "distribution": "normal",
            "description": "Priority level of the learning goal, 1 being the highest priority."
          },
          "goal_status": {
            "kind": "enum",
            "values": [
              "Not Started",
              "In Progress",
              "Completed"
            ],
            "weights": [
              50,
              30,
              20
            ],
            "description": "Current status of the learning goal."
          }
        }
      },
      "performance_metrics": {
        "description": "This table captures various performance metrics for each student over time. Metrics include engagement levels, completion rates, and scores on exercises.",
        "rows": {
          "per": "students",
          "via": "student_id",
          "min": 1,
          "max": 10,
          "skew": "long-tail"
        },
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each performance metric entry."
          },
          "student_id": {
            "kind": "fk",
            "ref": "students.id",
            "description": "The ID of the student this metric pertains to."
          },
          "date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "The date when the performance was assessed.",
            "trend": "growing"
          },
          "engagement_level": {
            "kind": "decimal",
            "min": 0,
            "max": 100,
            "scale": 2,
            "description": "The engagement level of the student on a scale from 0 to 100.",
            "distribution": "high"
          },
          "completion_rate": {
            "kind": "decimal",
            "min": 0,
            "max": 100,
            "scale": 2,
            "description": "The percentage of assigned tasks completed by the student.",
            "distribution": "high"
          },
          "score": {
            "kind": "int",
            "min": 0,
            "max": 100,
            "description": "The score achieved by the student in assessments.",
            "distribution": "high"
          }
        }
      },
      "assessments": {
        "description": "This table records the assessments taken by each student, including the type of assessment and the results.",
        "rows": {
          "per": "students",
          "via": "student_id",
          "min": 1,
          "max": 5,
          "skew": "long-tail"
        },
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each assessment."
          },
          "student_id": {
            "kind": "fk",
            "ref": "students.id",
            "description": "The ID of the student who took the assessment."
          },
          "assessment_date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "The date the assessment was conducted."
          },
          "assessment_type": {
            "kind": "enum",
            "values": [
              "Quiz",
              "Midterm",
              "Final Exam",
              "Project"
            ],
            "description": "The type of assessment.",
            "weights": [
              6,
              2,
              1,
              3
            ]
          },
          "result": {
            "kind": "decimal",
            "min": 0,
            "max": 100,
            "scale": 2,
            "description": "The result score achieved in the assessment.",
            "distribution": "high"
          }
        }
      },
      "learning_materials": {
        "description": "This table lists various learning materials assigned to students, linked to their specific goals and assessments.",
        "rows": {
          "per": "learning_goals",
          "via": "goal_id",
          "min": 1,
          "max": 3,
          "skew": "long-tail"
        },
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each learning material."
          },
          "goal_id": {
            "kind": "fk",
            "ref": "learning_goals.id",
            "description": "The ID of the learning goal associated with this material."
          },
          "material_type": {
            "kind": "enum",
            "values": [
              "Video",
              "Article",
              "Interactive Exercise"
            ],
            "description": "The type of learning material."
          },
          "material_link": {
            "kind": "url",
            "description": "The link to access the learning material."
          },
          "assigned_date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "The date the material was assigned."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "timestamped",
      "nullable",
      "categorical",
      "high-cardinality"
    ],
    "analysisQuestions": [
      "What patterns can be observed in student performance over time?",
      "How do different diagnoses affect learning goal achievement?",
      "Which types of learning materials lead to higher engagement levels?",
      "What is the typical completion rate for students with each type of learning disability?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Students with Performance Metrics",
        "sql": "SELECT s.full_name, p.engagement_level FROM students s JOIN performance_metrics p ON s.id = p.student_id;",
        "dialect": "postgres"
      },
      {
        "title": "Average Score by Assessment Type",
        "sql": "SELECT assessment_type, AVG(result) FROM assessments GROUP BY assessment_type;",
        "dialect": "postgres"
      },
      {
        "title": "Engagement Levels Over Time",
        "sql": "SELECT date, AVG(engagement_level) FROM performance_metrics GROUP BY date ORDER BY date;",
        "dialect": "postgres"
      },
      {
        "title": "Goals Achieved by Diagnosis",
        "sql": "SELECT s.diagnosis, COUNT(lg.id) FROM learning_goals lg JOIN students s ON lg.student_id = s.id GROUP BY s.diagnosis;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Educators Tracking Student Progress",
        "body": "Educators can utilize the Learning Progress Insights dataset to monitor the academic performance of students with learning disabilities. By analyzing performance metrics and assessment results, they can identify trends in student engagement and tailor their teaching strategies accordingly."
      },
      {
        "title": "Support Staff Evaluating Learning Materials",
        "body": "Support staff can leverage the dataset to evaluate the effectiveness of various learning materials assigned to students. By correlating engagement levels with specific resources, they can refine material selection to enhance learning experiences."
      },
      {
        "title": "Data Analysts Reporting on Engagement Trends",
        "body": "Data analysts can use this dataset to generate reports on engagement trends among students with different learning disabilities. By presenting visualizations of performance over time, they can provide actionable insights to educators and administrators."
      },
      {
        "title": "Program Administrators Monitoring Goal Achievement",
        "body": "Program administrators can monitor the achievement of learning goals across different student demographics using this dataset. By analyzing which goals are commonly met or missed, they can adjust program offerings to better support students' needs."
      }
    ]
  },
  "counts": {
    "students": 100,
    "assessments": 217,
    "learning_goals": 238,
    "learning_materials": 380,
    "performance_metrics": 414
  },
  "stats": {
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          "max": "Zara Torres"
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