{
  "format": "dataset-manifest/1",
  "slug": "ai-tutor-and-homework-helper",
  "version": 2,
  "seed": "ai-tutor-and-homework-helper:1",
  "spec": {
    "format": "dataset/1",
    "slug": "ai-tutor-and-homework-helper",
    "name": "AI Learning Interactions",
    "tagline": "Logged interactions, achievements, quizzes, and flashcard usage from students in online AI-assisted education.",
    "description": "Students engage with AI-driven tutors, enhancing their learning through interactions with quizzes and flashcards. Each student’s journey is documented, capturing moments of interaction that reveal their learning habits and achievements. This business focuses on optimizing the educational experience by analyzing how students respond to various subjects and teaching methods delivered through an AI platform. The interactions recorded include quiz attempts, flashcard usage, and homework submissions, showcasing the comprehensive participation of students in their learning processes.\n\nThe dataset consists of several interconnected tables: the 'students' table lists individual learners, detailing their demographics and enrollment details. The 'interactions' table tracks every engagement a student has with the platform, including time spent on quizzes and flashcards. The 'achievements' table records milestones reached by students, indicating their progress and areas of success. 'Quizzes' detail the types of assessments administered, while 'flashcards' provide insight into tools used for study. Each row in the 'interactions' table can be linked back to a specific student from the 'students' table, while achievements can be traced to interactions, illustrating how engagement translates into success.\n\nThis data answers critical questions regarding student engagement levels, the effectiveness of different learning tools, and the correlation between interaction frequency and academic performance. It allows developers to practice skills in analyzing educational outcomes, visualizing performance trends, and developing algorithms that can adapt to user behavior. Remember, the data is synthetic, designed to simulate real-world scenarios in online education.",
    "domain": "education",
    "shape": "transactional",
    "useCases": [
      "sql-practice",
      "orm-seeding"
    ],
    "keywords": [
      "AI tutoring",
      "student interactions",
      "quizzes",
      "achievements",
      "flashcards",
      "education",
      "learning analytics"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
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        "description": "Each row represents a student in the AI tutoring system, detailing their profile and engagement metrics.",
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        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each student."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The full name of the student."
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "Email address of the student, derived from their name."
          },
          "enrollment_date": {
            "kind": "date",
            "from": "2020-01-01",
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            "description": "The date when the student enrolled in the tutoring system.",
            "trend": "growing"
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          "total_achievements": {
            "kind": "int",
            "min": 0,
            "max": 50,
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            "distribution": "long-tail"
          }
        }
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        "description": "Logs each interaction a student has with quizzes and flashcards, capturing engagement over time.",
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          "via": "student_id",
          "min": 1,
          "max": 10,
          "skew": "long-tail"
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            "kind": "pk",
            "description": "Unique identifier for each interaction."
          },
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            "kind": "fk",
            "ref": "students.id",
            "nullable": false,
            "description": "The ID of the student who interacted."
          },
          "interaction_type": {
            "kind": "enum",
            "values": [
              "quiz",
              "flashcard"
            ],
            "description": "Type of interaction the student engaged in."
          },
          "interaction_date": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date and time of the interaction.",
            "dayProfile": "waking"
          },
          "score": {
            "kind": "decimal",
            "min": 0,
            "max": 100,
            "description": "Score achieved by the student in a quiz interaction.",
            "nullable": true,
            "when": {
              "column": "interaction_type",
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                "quiz"
              ]
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            "distribution": "high"
          }
        }
      },
      "achievements": {
        "description": "Records the achievements unlocked by students, reflecting their progress and milestones.",
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          "min": 1,
          "max": 5,
          "skew": "long-tail"
        },
        "columns": {
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            "description": "Unique identifier for each achievement."
          },
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            "nullable": false,
            "description": "The ID of the student who unlocked the achievement."
          },
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            "kind": "words",
            "min": 1,
            "max": 3,
            "description": "Name of the achievement unlocked by the student.",
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              "completion",
              "excellence",
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              "champion",
              "leader",
              "visionary",
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              "mentor",
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            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date when the achievement was unlocked.",
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        }
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        "columns": {
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            "kind": "pk",
            "description": "Unique identifier for each quiz."
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          "topic": {
            "kind": "words",
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            "description": "The subject area or topic of the quiz.",
            "pool": [
              "math",
              "science",
              "history",
              "literature",
              "art",
              "technology",
              "geography",
              "music",
              "psychology",
              "biology",
              "physics",
              "chemistry",
              "economics",
              "philosophy",
              "sociology",
              "health",
              "engineering",
              "computer",
              "languages",
              "statistics",
              "environment"
            ]
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              "medium",
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                "medium",
                "hard"
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        "rows": 200,
        "columns": {
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            "pool": [
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              "grammar",
              "formulas",
              "dates",
              "concepts",
              "definitions",
              "theories",
              "principles",
              "terms",
              "events",
              "characters",
              "structures",
              "functions",
              "processes",
              "methods",
              "skills",
              "strategies",
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              "examples",
              "applications"
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          },
          "content": {
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            "nullable": true
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    "traits": [
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      "timestamped",
      "high-cardinality"
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    "analysisQuestions": [
      "What subjects are most frequently engaged with by students?",
      "How does the frequency of interactions correlate with the number of achievements?",
      "Which quizzes have the highest average scores among students?",
      "What patterns can be observed in the usage of flashcards over time?",
      "Are there specific times when students are more likely to engage with quizzes?"
    ],
    "suggestedQueries": [
      {
        "title": "Retrieve all quizzes and their details",
        "sql": "SELECT * FROM quizzes;",
        "dialect": "postgres"
      },
      {
        "title": "Count interactions per student",
        "sql": "SELECT student_id, COUNT(*) FROM interactions GROUP BY student_id;",
        "dialect": "postgres"
      },
      {
        "title": "Find average quiz scores by topic",
        "sql": "SELECT q.topic, AVG(i.score) FROM quizzes q JOIN interactions i ON q.id = i.quiz_id GROUP BY q.topic;",
        "dialect": "postgres"
      },
      {
        "title": "List achievements unlocked by each student",
        "sql": "SELECT s.full_name, a.achievement_name FROM students s JOIN achievements a ON s.id = a.student_id;",
        "dialect": "postgres"
      },
      {
        "title": "Analyze interaction trends over time",
        "sql": "SELECT DATE_TRUNC('month', interaction_date) AS month, COUNT(*) FROM interactions GROUP BY month ORDER BY month;",
        "dialect": "postgres"
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    "usageScenarios": [
      {
        "title": "Educational Research",
        "body": "Researchers can use this dataset to analyze student engagement patterns with AI tutors, identifying which features lead to higher achievement rates and overall satisfaction."
      },
      {
        "title": "Performance Analytics",
        "body": "Data analysts in educational institutions can utilize the dataset to track student performance over time, evaluating the effectiveness of different quizzes and study materials."
      },
      {
        "title": "Personalized Learning Development",
        "body": "Developers creating personalized learning platforms can use this data to understand user behavior and tailor educational content to enhance student engagement and performance."
      },
      {
        "title": "Gamification in Education",
        "body": "Educators exploring gamification strategies can analyze how achievements influence student motivation and interaction frequency, helping them design better learning experiences."
      }
    ]
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