Application that asks users CS based questions that are solved by inserting code which is checked by a series of algorithmns. This project aims to enhance our existing CS problem-solving platform by expanding the range of topics beyond 2D Arrays/Arrays. It also includes the development of a recommendation engine that suggests problems based on a user's skill level and a prediction feature to estimate a user's AP score based on their problem-solving proficiency. Additionally, the project focuses on improving the scoring system by incorporating more test cases for partial scoring, handling complex inputs, and providing helpful feedback to users.
Visit the deployed site: https://monkeygame.rohanj.dev/
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Expanded Topic Coverage: The platform will include a wider range of topics besides 2D Arrays/Arrays. Users will be able to explore and solve problems related to various CS concepts.
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Recommendation Engine: A recommendation engine will be implemented to suggest problems suited to a user's skill level. This recommendation will be based on an assessment of the user's proficiency, potentially utilizing a diagnostic evaluation.
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AP Score Prediction: The system will predict a user's AP (Advanced Placement) score based on their ability to solve specific problems. The prediction will provide an estimate of the user's performance in the AP exam.
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Enhanced Scoring System: The scoring mechanism will be improved by incorporating additional test cases for partial scoring. This will involve the inclusion of more complex inputs and edge cases to accurately evaluate the user's solution. Furthermore, the feedback provided to users will be enhanced to offer more helpful insights.
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Data Structures and Operations: The project will leverage various data structures, such as an online database, to store user-submitted code and their corresponding scores. CRUD operations (Create, Read, Update, Delete) will be implemented to manage user data. Sorting, data filtering, and query operations will be used to organize problems based on difficulty and categories.
The following technical components are necessary to implement the project effectively:
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Online Database: An online database will be utilized to store user code submissions and scores. CRUD operations (Create, Read, Update, Delete) will be implemented to manage user data effectively.
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Sorting and Filtering: Problems will be sorted and filtered based on their difficulty level. This functionality will be used for recommending problems to users and allowing users to browse problems by categories.
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Data Structures: Proper implementation of data structures will be essential to support efficient querying, filtering, and sorting of problems and user data.
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Solving Problems: Users can browse through available problem categories and select a problem of interest. The platform will guide users through solving the problem by presenting the necessary code template and any additional instructions.
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Recommendation Engine: Users will receive personalized problem recommendations based on their skill level. The recommendation engine will analyze user performance and suggest suitable problems for further practice.
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AP Score Prediction: Users can track their progress and receive an estimated AP score based on their problem-solving ability. This feature aims to provide users with valuable insights into their preparedness for the AP exam.
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Scoring and Feedback: The scoring system will evaluate user-submitted code against a range of test cases, including complex inputs and edge cases. Users will receive detailed feedback highlighting areas for improvement and suggestions for optimizing their solutions.
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Data Management: The online database will store user code submissions and their associated scores. Users can access their historical data and track their progress over time. CRUD operations will be available for managing user data effectively.
We welcome contributions to this project. To contribute, follow these steps:
Fork the repository
Create a new branch for your feature or bug fix.
Implement your changes and ensure that the codebase remains in good quality.
Write appropriate tests to validate the functionality of your changes.
Submit a pull request with a descriptive title and a summary of your changes.
Participate in the code review process by addressing any feedback or suggestions.
This project is licensed under the MIT LICENSE
To build and run the project locally, follow these steps:
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Clone the repository to your local machine using the following command:
git clone https://github.com/supermengman/macro-monkeys.git -
Navigate to the project directory:
cd macro-monkeys -
Install Vercel globally by running the following command:
npm install -g vercel -
Login to your Vercel account
vercel login -
Configure the project settings:
vercel init
Follow the prompts to set up the project on Vercel. Choose the appropriate options based on your preferences and project requirements.
- Build and deploy the project:
vercel --prod
This command will build the project and deploy it to Vercel. The --prod flag specifies that the deployment should be made to the production environment.
OR
visit vercel.com and link your github repository following the prompts on the website
- Deploy backend: Backend Instructions
After deploying backend, connect it to a domain. Then, change the api requests in this repo to use the new domain as a base.
- Access the deployed application: Once the deployment process is complete, Vercel will provide you with a unique URL where your application is hosted. You can access the application by visiting that URL in a web browser.
Note: The exact URL and any custom domain configuration may vary based on your Vercel project settings.