Emotion-Based Learning Platform Transforming Education

Solution

Business analysis:

Our team started by performing a comprehensive needs analysis and holding workshops with psychologists, educators, and students to determine the essential functions and user experience specifications. To guarantee scalability, we opted for a microservices architecture and implemented an Agile development process using two-week sprints.

Software development approach and testing:

Secure login management, educator onboarding, class administration with QR code integration, mood and student journal tracking, extensive instructor dashboards, and powerful analytics capabilities were among the key features put into place. We also put in place an advanced email reminder and notification system, as well as an integrated payment mechanism for premium features.

DevOps Implementation

• CI/CD Pipeline:

  • To ensure quick and dependable updates, we set up GitLab CI/CD for automated testing and deployment.

• Containerisation:

  • The application was containerised using Docker and orchestrated on AWS EKS using Kubernetes, which made scalability and management simple.

• Infrastructure as Code:

  • Version-controlled infrastructure changes were made possible by using Terraform to manage AWS resources.

• Monitoring and Logging:

  • Prometheus with Grafana was utilised for real-time monitoring and alerting, and the ELK stack (Elasticsearch, Logstash, Kibana) was put into place for centralised logging.

• Security:

  • Strict access controls were put in place, regular security audits were carried out, AWS WAF was set up to guard against web attacks, and AWS KMS was used to encrypt any sensitive data.

• Database management:

  • To enhance performance and ensure data security, we employed read replicas for PostgreSQL on Amazon RDS, along with automated backups.

• Caching:

  • Redis was used to manage sessions and cache frequently requested information, which sped up response times.

• Content Delivery:

  • Static assets were sent via Amazon CloudFront, lowering latency for users in various geographic areas.

• Automated Scaling:

  • To manage traffic spikes, particularly during school hours, we have auto-scaling groups set up.

• Disaster Recovery:

  • To guarantee high availability, a multi-region failover system was put into place.

Support

We set up a round-the-clock support staff with three escalation levels. The group created a knowledge base and managed incidents using ServiceNow. To continuously develop the platform, user feedback sessions and performance reviews were held on a regular basis.

Result

With 99.99% uptime, the platform now serves more than 500 schools. More than 5 million mood entries have been processed, giving educators insightful information. Users have praised the application's dependability and functionality, giving it an average app store rating of more than 4.8 stars.

Important accomplishments consist of:

• 40% less time spent deploying
• A thirty percent increase in application response time
• Achieved 99.99% uptime
• Managing more than 100,000 concurrent users with success during peak hours
• 50% infrastructure cost savings achieved through effective resource use