Sunbird Obsrv
  • Introduction
    • The Value of Data
    • Data Value Chain
    • Challenges
    • The Solution: Obsrv
  • Core Concepts
    • Obsrv Overview
    • Key Capabilities
    • Datasets
    • Connectors
    • High Level Architecture
    • Tech Stack
    • Monitoring
  • Explore
    • Roadmap
    • Case Studies
      • Agri Climate Advisory
      • Learning Analytics at Population Scale
      • IOT Observations Infra
      • Data Driven Features in Learning Platform
      • Network Observability
      • Fraud Detection
    • Performance Benchmarks
  • Guides
    • Installation
      • AWS Installation Guide
      • Azure Installation Guide
      • GCP Installation Guide
      • OCI Installation Guide
      • Data Center Installation Guide
    • Dataset Management APIs
    • Dataset Management Console
    • Connector APIs
    • Data In & Out APIs
    • Alerts and Notification Channels APIs
    • Developer Guide
    • Example Datasets
    • Connectors Developer Guide
      • SDK Assumptions
      • Required Files
        • metadata.json
        • ui-config.json
        • metrics.yaml
        • alerts.yaml
      • Obsrv Base Setup
      • Dev Requirements
      • Interfaces
        • Stream Interfaces
        • Batch Interfaces
      • Classes
        • ConnectorContext Class
        • ConnectorStats Class
        • ConnectorState Class
        • ErrorData Class
        • MetricData Class
      • Verifying
      • Packaging Guide
      • Reference Implementations
    • Coming Soon!
  • Community
  • Previous Versions
    • SB-5.0 Version
      • Overview
      • USE
        • Release Notes
          • Obsrv 2.0-Beta
          • Obsrv 2.1.0
          • Obsrv 2.2.0
          • Obsrv 2.0.0-GA
          • Obsrv 5.3.0-GA
          • Release V 5.1.0
          • Release V 5.1.2
          • Release V 5.1.3
          • Release V 5.0.0
          • Release V 4.10.0
        • Installation Guide
        • Obsrv 2.0 Installation Guide
          • Getting Started with Obsrv Deployment Using Helm
        • System Requirements
      • LEARN
        • Functional Capabilities
        • Dependencies
        • Product Roadmap
        • Product & Developer Guide
          • Telemetry Service
          • Data Pipeline
          • Data Service
          • Data Product
            • On Demand Druid Exhaust Job
              • Component Diagram
              • ML CSV Reports
              • Folder Struture
          • Report Service
          • Report Configurator
          • Summarisers
      • ENGAGE
        • Discuss
        • Contribute to Obsrv
      • Raise an Issue
  • Release Notes
    • Obsrv 1.1.0 Beta Release
    • Obsrv 1.2.0-RC Release
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  • The Challenge
  • Benefits

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  1. Explore
  2. Case Studies

Learning Analytics at Population Scale

PreviousAgri Climate AdvisoryNextIOT Observations Infra

Last updated 1 year ago

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In the rapidly evolving landscape of education, analytics has emerged as a pivotal tool for learning platforms at scale, ushering in a transformative era of data-driven insights. As the demand for online learning experiences continues to grow, the importance of analytics in educational platforms becomes increasingly evident. Analytics not only provides a comprehensive view of learner engagement and performance but also empowers educators and administrators with the ability to make informed decisions. Through the systematic analysis of vast datasets, learning platforms can personalize content, identify patterns of student behavior, and optimize the overall learning experience.

This case study explores how OBSRV enabled a population scale learning platform like DIKSHA to provide a means for students to access educational content remotely and offered teachers a digital repository of learning resources and teaching material to facilitate remote instruction, ensuring continuity in learning during the recent pandemic.

The Challenge

Any learning analytics system which aims at gathering insights into interactions, engagement, and performance to enhance educational outcomes are faced with multifaceted challenges.

  • Scalability: Ensuring that systems can handle increasing data volumes and user interactions without compromising performance is a persistent challenge.

  • Data Privacy and Security: Protecting the anonymity of the user data and ensuring proper access controls are present in the system for multi-tenant access.

  • Data Accessibility: Educators need to have an easy and efficient way of accessing historical data and understand the lineage to come up with specific learning objectives.

Benefits

  • Reliability at Scale: Educators can make informed decisions in real-time and change the learning plan as Obsrv ensures reliability at scale while processing huge volumes of data.

  • Lossless data processing: Obsrv ensures that the client systems always get an acknowledgment to ensure the data ingestion and processing are lossless.

  • Data Transparency: Obsrv enabled the teachers to have transparent access to the learning data to understand and personalize learning paths for various students.

  • Scalability: DIKSHA’s mission was to reach 200 million children with basic learning experiences and Obsrv delivered effortlessly with 5 million Daily Active Users and processing 2 billion data points a day at peak.

Learning Analytics at Scale