With the development of online learning, more and more students’ demand for interactive and personalized study experience are growing. However, traditional learning platform lack of communication and social interaction, hardly encourage students to learn constantly. MindQuest is dedicated to improving students’ learning motivation and efficiency through Q&A system, socialized study, game-based motivation and AI personalized recommendations.
MindQuest: Ask, Learn, Conquer–Brainpower is the Best Superpower! MindQuest provides us a real-time collaborative online learning environment, allow students to ask and doing real-time interaction on course materials, to improve deeply discussion outside the class. This platform can enhance interaction by liking, commenting, subscribe experts or contributors and other features like these. Meanwhile, platform also introduced gamification mechanism such as points, levels and badges to encourage students to participate in actively. More than that, AI personalized recommendation system can automatically recognise students’ learning demands and provides suggestions related to relevant questions and learning resources.
Name: Xuanrui Zhou
Student number: 49072538
MindQuest is a simplistic way to help students learn effectively. The general idea is using an intuitive, accessible approach, via platforms, students can easily ask questions, have discussions and get highly quality answers.
Here are some core features in the platform: interactive Q&A system, real-time documents collaboration and annotation, socialized study, gamification mechanism, AI personalized recommendations, real-time notification system, user authentication and resource integration etc.
For students, they need to login first, once login successfully, they can enter the course page to select relevant course and visit this course’s all materials. When they have problems when learning, they can select texts in the document, click “add question”, enter the question descriptions add choose questions’ category, then finally published questions. Other users can find this question in the appropriate document paragraph or search these types of questions to view and answer it, meanwhile, AI will automatically determine question type and then recommend similar questions and references. Users can also use search function to search course materials, relevant questions and similar discussions. When student user click subscribe button, they will receive notifications automatically once the question being answered. For those answers mark as authoritative by staff will automatically be pushed to students who subscribe this question too. Students can gain points and improve ranks by answer questions, get likes and complete mission.
For staff, they also need to log in first, then enter the management page, choose their responsible courses and upload relevant course materials. They can also enter Q&A system, answer students’ questions or view students’ answers, and mark those helpful answers as “authoritative”. Staff can see most common question type and students participate in discussion active level in data page. At the same time, staff can answer questions in PPT, PDF or other course documents directly, all students can see the answers in real-time.
For first-time users, both students and staff, once login successfully, the system will automatically enter in green hand tutorial page, help users quickly familiar with MindQuest.
The scope for the minimal viable product is primarily the ability to：
Basic Q&A system (support for questions, answers, likes, sorting and subscription).
Real-time document collaboration and annotation (Course materials like PDF files can be highlighted and discussed).
Basic user authentication (JWT login and personal home page).
Keyword search and filtering.
Real-time notification system (WebSocket support for instant messaging updates).
The key quality attributes most important for this project is Scalability, Availability, Consistency and Extensibility. It’s worth noting that security is just as important, but it will be considered during the design and implementation phase of the architecture.
This platform currently has a sizable user base that will continue to grow over time. The system must have strong scalability due to the big user base. The dynamic scalability of service modules can be supported by a microservice design and containerized deployment (e.g., Docker and Kubernetes), while highly concurrent queries can be handled by distributed databases and caches (e.g., Redis). By guaranteeing high throughput, low latency, and overall excellent performance, this strategy will contribute to the system’s linear expansion in processing capacity, improving the system’s scalability.
During busy study times, homework due dates, and final revision weeks, students use the system a lot. To avoid login issues or system problems, the system must function reliably. The platform may be used continuously thanks to the deployment of several nodes and load balancing, which increases performance utilization. A technique for automatic failover in the event of a service anomaly.
To prevent conflicts, the data must be consistent when numerous users work together to mark course documents in real-time. To guarantee that real-time actions like annotation, commenting, liking, and following are synchronized across several ends, strong consistency techniques are employed. To manage cooperative update scenarios, use optimistic concurrency control and transaction mechanisms.
Future access to other useful modules, such AI conversation assistants, must be supported by the platform. The system interface must continue to be low-coupling and modular in order to facilitate plug-and-play functionality extension. Include additional features that support the customization requirements of various courses and adjust to different learning scenarios without sacrificing the functionality of the current system.
Besides a basic implementation of the MVP the core attributes must also be achieved. These will be determined by the following criteria：
We can simulate 1,000 concurrent students viewing the quiz content and annotated papers using a user load test (such as JMeter or K6) and track response time and system load. Check to see if performance rises linearly with load by noting the average system response time, CPU, and resource usage during normal access.
We can use uptime monitoring tools to check the platform’s running status and CPU load on a regular basis, add a fault injection mechanism to simulate node outages, verify that the system can be recovered in less than ten seconds, and test whether the backup node can take over the request without any problems in order to ensure the success of the deployment plan.
When two users mark, like, comment, and subscribe to the same section of a document nearly simultaneously, we need to test whether the system can combine correctly or flag a dispute. To confirm the stability and dependability of the data synchronization mechanism, test concurrent write operations using version control policies. Verify whether the incomplete update state is avoided by using the transaction method.
We need to determine if adding an AI question recommendation module requires extending a microservice without modifying the database structure in order to provide functionality without changing the core module. The amount of the code modifications required to add functionality must also be documented, and it must be ensured that the changes have no adverse effects and only affect the target module. Additionally, make sure the CI/CD process can be used to securely and automatically test the new feature after it becomes live.