Data Analyst

Overview

The Data Analyst component is an AI-powered chatbot designed specifically for educators. It provides intelligent insights into student engagement, performance metrics, and learning patterns. Educators can ask natural language questions about their students’ study habits, identify areas where students may need additional support, and receive personalized recommendations to improve learning outcomes.

Creating a Data Analyst Component

import StudyfetchSDK from '@studyfetch/sdk';

const client = new StudyfetchSDK({
  apiKey: 'your-api-key',
  baseURL: 'https://studyfetchapi.com',
});

const dataAnalystComponent = await client.v1.components.create({
  name: 'Student Performance Analyst',
  type: 'data-analyst',
  config: {
    materials: ['mat-123', 'mat-456'], // Course materials with student activity data
    folders: ['folder-789'],
    model: 'gpt-4o-mini-2024-07-18',
    systemPrompt: 'You are an AI assistant for educators. Help analyze student usage patterns, performance metrics, and provide actionable recommendations to improve learning outcomes. Focus on identifying struggling students, engagement trends, and suggesting personalized interventions.',
    temperature: 0.3, // Lower temperature for more precise analysis
    maxTokens: 4096
  }
});

console.log('Data Analyst component created:', dataAnalystComponent._id);

from studyfetch_sdk import StudyfetchSDK

client = StudyfetchSDK(
    api_key="your-api-key",
    base_url="https://studyfetchapi.com",
)

data_analyst_component = client.v1.components.create(
    name="Student Performance Analyst",
    type="data-analyst",
    config={
        "materials": ["mat-123", "mat-456"],  # Course materials with student activity data
        "folders": ["folder-789"],
        "model": "gpt-4o-mini-2024-07-18",
        "systemPrompt": "You are an AI assistant for educators. Help analyze student usage patterns, performance metrics, and provide actionable recommendations to improve learning outcomes. Focus on identifying struggling students, engagement trends, and suggesting personalized interventions.",
        "temperature": 0.3,  # Lower temperature for more precise analysis
        "maxTokens": 4096
    }
)

print(f"Data Analyst component created: {data_analyst_component._id}")

import com.studyfetch.javasdk.client.StudyfetchSdkClient;
import com.studyfetch.javasdk.client.okhttp.StudyfetchSdkOkHttpClient;
import com.studyfetch.javasdk.models.v1.components.ComponentResponse;
import com.studyfetch.javasdk.models.v1.components.ComponentCreateParams;
import java.util.List;

public class CreateDataAnalystComponent {
    public static void main(String[] args) {
        StudyfetchSdkClient client = StudyfetchSdkOkHttpClient.builder()
            .fromEnv()
            .baseUrl("https://studyfetchapi.com")
            .build();

        ComponentCreateParams params = ComponentCreateParams.builder()
            .name("Student Performance Analyst")
            .type(ComponentCreateParams.Type.DATA_ANALYST)
            .config(ComponentCreateParams.Config.DataAnalystConfigDto.builder()
                .materials(List.of("mat-123", "mat-456")) // Course materials with student activity data
                .folders(List.of("folder-789"))
                .model("gpt-4o-mini-2024-07-18")
                .systemPrompt("You are an AI assistant for educators. Help analyze student usage patterns, performance metrics, and provide actionable recommendations to improve learning outcomes. Focus on identifying struggling students, engagement trends, and suggesting personalized interventions.")
                .temperature(0.3) // Lower temperature for more precise analysis
                .maxTokens(4096)
                .build())
            .build();

        ComponentResponse component = client.v1().components().create(params);
        System.out.println("Data Analyst component created: " + component._id());
    }
}

using StudyfetchSDK;
using StudyfetchSDK.Models.V1.Components;
using System;
using System.Collections.Generic;
using System.Threading.Tasks;
public class CreateDataAnalystComponent
{
    public static async Task CreateDataAnalyst()
    {
        var client = new StudyfetchSDKClient()
        {
            APIKey = Environment.GetEnvironmentVariable("STUDYFETCH_API_KEY"),
            BaseUrl = new Uri("https://studyfetchapi.com")
        };

        var dataAnalystComponent = await client.V1.Components.Create(new()
        {
            Name = "Student Performance Analyst",
            Type = StudyfetchSDK.Models.V1.Components.ComponentCreateParamsProperties.Type.DataAnalyst,
            Config = new StudyfetchSDK.Models.V1.Components.ComponentCreateParamsProperties.ConfigProperties.DataAnalystConfigDto()
            {
                Model = "gemini-2.5-flash",
                Materials = new List<string> { "mat-123" }
            }
        });

        Console.WriteLine($"Data Analyst component created: {dataAnalystComponent._ID}");
    }
}

Configuration Parameters

name

string

required

Name of the data analyst component

type

string

required

Must be "data-analyst"

config

object

required

Data analyst configuration object

Show Configuration Properties

materials

array

required

Array of material IDs containing student activity and performance data

folders

array

Array of folder IDs containing course materials with student data

model

string

default:"gpt-4o-mini-2024-07-18"

AI model to use for analysis

systemPrompt

string

Custom instructions for the AI assistant (default focuses on student analytics for educators)

temperature

number

default:"0.3"

Controls randomness in responses (0-1, lower is more precise)

maxTokens

integer

default:"4096"

Maximum length of analysis responses

Response

{
  "_id": "comp_abc123",
  "name": "Student Performance Analyst",
  "type": "data_analyst",
  "status": "active",
  "config": {
    "materials": ["mat-123", "mat-456"],
    "folders": ["folder-789"],
    "model": "gpt-4o-mini-2024-07-18",
    "systemPrompt": "You are an AI assistant for educators. Help analyze student usage patterns, performance metrics, and provide actionable recommendations to improve learning outcomes. Focus on identifying struggling students, engagement trends, and suggesting personalized interventions.",
    "temperature": 0.3,
    "maxTokens": 4096
  },
  "createdAt": "2024-01-15T10:00:00Z",
  "updatedAt": "2024-01-15T10:00:00Z",
  "organizationId": "org_456def",
  "statistics": {
    "totalSessions": 0,
    "totalAnalyses": 0,
    "averageSessionDuration": null
  }
}

Embedding This Component

Once you’ve created a Data Analyst component, you can embed it on your website using the embedding API.

Generate Embed URL

const embedResponse = await client.v1.components.generateEmbed(dataAnalystComponent._id, {
  // User tracking
  userId: 'user-456',
  studentName: 'Jane Smith',  // Student name for display
  groupIds: ['class-101', 'class-102'],
  sessionId: 'session-789',
  
  // Dimensions
  width: '100%',
  height: '700px',
  
  // Token expiry
  expiryHours: 24
});

embed_response = client.v1.components.generateEmbed(
    component_id=data_analyst_component._id,
    userId="user-456",
    student_name="Jane Smith",  # Student name for display
    groupIds=["class-101", "class-102"],
    sessionId="session-789",
    width="100%",
    height="700px",
    expiryHours=24
)

import com.studyfetch.javasdk.client.StudyfetchSdkClient;
import com.studyfetch.javasdk.client.okhttp.StudyfetchSdkOkHttpClient;
import com.studyfetch.javasdk.models.v1.components.ComponentGenerateEmbedParams;
import com.studyfetch.javasdk.models.v1.components.ComponentGenerateEmbedResponse;

StudyfetchSdkClient client = StudyfetchSdkOkHttpClient.builder()
    .fromEnv()
    .baseUrl("https://studyfetchapi.com")
    .build();

ComponentGenerateEmbedParams params = ComponentGenerateEmbedParams.builder()
    .id(dataAnalystComponent._id())
    // User tracking
    .userId("user-456")
    .studentName("Jane Smith")  // Student name for display
    .groupIds(List.of("class-101", "class-102"))
    .sessionId("session-789")
    
    // Dimensions
    .width("100%")
    .height("700px")
    
    // Token expiry
    .expiryHours(24)
    .build();

ComponentGenerateEmbedResponse embedResponse = client.v1().components()
    .generateEmbed(params);

using StudyfetchSDK;
using StudyfetchSDK.Models.V1.Components;
using System;
using System.Collections.Generic;
using System.Threading.Tasks;

public class GenerateDataAnalystEmbed
{
    public static async Task GenerateEmbed()
    {
        var client = new StudyfetchSDKClient()
        {
            APIKey = Environment.GetEnvironmentVariable("STUDYFETCH_API_KEY"),
            BaseUrl = new Uri("https://studyfetchapi.com")
        };

        var embedResponse = await client.V1.Components.GenerateEmbed(new()
        {
            ID = "component_123abc", // Replace with your component ID
            UserID = "user-456",
            StudentName = "Jane Smith",  // Student name for display
            GroupIDs = new List<string> { "class-101", "class-102" },
            Width = "100%",
            Height = "600px"
        });

        Console.WriteLine($"Embed URL: {embedResponse.EmbedURL}");
        Console.WriteLine($"Token: {embedResponse.Token}");
    }
}

Embed in Your HTML

<iframe 
  src="https://embed.studyfetch.com/component/comp_abc123?token=..."
  width="100%"
  height="700px"
  frameborder="0"
  allow="clipboard-write"
  style="border: 1px solid #e5e5e5; border-radius: 8px;">
</iframe>

Component API

REST API

Overview

Creating a Data Analyst Component

Configuration Parameters

Response

Embedding This Component

Generate Embed URL

Embed in Your HTML

​Overview

​Creating a Data Analyst Component

​Configuration Parameters

​Response

​Embedding This Component

​Generate Embed URL

​Embed in Your HTML

Overview

Creating a Data Analyst Component

Configuration Parameters

Response

Embedding This Component

Generate Embed URL

Embed in Your HTML