
Petrut Borcan developed core backend and frontend features for the IP-A2-2025/Quizzy repository over three months, focusing on scalable quiz data management and user engagement. He designed and implemented RESTful APIs and service layers using Java and Spring Boot, enabling CRUD operations for flashcards, sessions, and user streaks. His work included refactoring API endpoints for consistency, introducing user authentication and persistent login, and enhancing frontend state management with React and CSS. By adding theory mode, contextual information, and robust streak tracking, Petrut improved data integrity, learning flow, and progress reporting, demonstrating depth in both backend architecture and user experience.

June 2025 (2025-06) monthly summary for IP-A2-2025/Quizzy: Delivered two major enhancements that improve learning context and user engagement, and implemented robust streak tracking with API support and data integrity safeguards. These changes enhance business value by increasing task completion, reinforcing habit formation, and enabling more personalized progress insights. Technical changes included backend API endpoints, database constraints, frontend state handling, and CSS/UI enhancements.
June 2025 (2025-06) monthly summary for IP-A2-2025/Quizzy: Delivered two major enhancements that improve learning context and user engagement, and implemented robust streak tracking with API support and data integrity safeguards. These changes enhance business value by increasing task completion, reinforcing habit formation, and enabling more personalized progress insights. Technical changes included backend API endpoints, database constraints, frontend state handling, and CSS/UI enhancements.
May 2025 performance summary for IP-A2-2025/Quizzy focused on API consistency, user engagement, and learning flow reliability. Delivered backend API Endpoints Refactor with standardized endpoint names, introduced User Streak Tracking with dedicated data models and new update/fetch endpoints (including inactivity reset), added User ID Persistence in the login flow for cross-session identification via local storage, fixed a Flashcards bug ensuring single-choice answers display correctly by selecting text from the appropriate field, and implemented Course Start Learning Navigation to route to the first material's flashcards with proper handling when none exist. These changes reduce integration friction, improve user engagement measures, and provide a more seamless learning experience across sessions.
May 2025 performance summary for IP-A2-2025/Quizzy focused on API consistency, user engagement, and learning flow reliability. Delivered backend API Endpoints Refactor with standardized endpoint names, introduced User Streak Tracking with dedicated data models and new update/fetch endpoints (including inactivity reset), added User ID Persistence in the login flow for cross-session identification via local storage, fixed a Flashcards bug ensuring single-choice answers display correctly by selecting text from the appropriate field, and implemented Course Start Learning Navigation to route to the first material's flashcards with proper handling when none exist. These changes reduce integration friction, improve user engagement measures, and provide a more seamless learning experience across sessions.
April 2025 monthly summary for IP-A2-2025/Quizzy: Delivered foundational backend infrastructure for quiz data management, establishing service layers and REST API endpoints. Implemented core models (Flashcard, FlashcardSession, Material, AnswerFC, Streak), created controllers and unit tests, and delivered RESTful CRUD operations for quiz data. This work provides a scalable data layer, accelerates frontend integration, and improves test coverage and maintainability.
April 2025 monthly summary for IP-A2-2025/Quizzy: Delivered foundational backend infrastructure for quiz data management, establishing service layers and REST API endpoints. Implemented core models (Flashcard, FlashcardSession, Material, AnswerFC, Streak), created controllers and unit tests, and delivered RESTful CRUD operations for quiz data. This work provides a scalable data layer, accelerates frontend integration, and improves test coverage and maintainability.
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