AdaptiCode
Personalized and Adaptive Learning for Mastering Data Structures and Algorithms
Created on 7th April 2024
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AdaptiCode
Personalized and Adaptive Learning for Mastering Data Structures and Algorithms
The problem AdaptiCode solves
People can use AdaptiCode to efficiently master Data Structures and Algorithms (DSA). Whether they are beginners aiming to establish a solid foundation or experienced individuals seeking to hone their skills, AdaptiCode offers a personalized learning experience tailored to their specific needs. By assessing the user's proficiency level and recommending targeted problems from platforms like LeetCode, AdaptiCode ensures that learners focus on areas where improvement is needed the most. It helps users streamline their learning process, saving time and effort by providing relevant and adaptive challenges. Tailored Learning Path: AdaptiCode evaluates your DSA proficiency and crafts a personalized learning journey based on your strengths and weaknesses.
Problem Recommendations: Get targeted problem recommendations from platforms like LeetCode, ensuring you focus on areas where improvement is needed.
Adaptive Challenges: As you progress, AdaptiCode dynamically adjusts, presenting increasingly challenging problems to push your skills to new heights.
Efficient Learning: Say goodbye to wasted time on irrelevant topics. AdaptiCode streamlines your learning process by focusing on what matters most to you.
Continuous Improvement: Track your progress and see how far you've come. AdaptiCode keeps you motivated on your journey to DSA mastery.
Challenges we ran into
During the development of AdaptiCode, we encountered several hurdles that tested our problem-solving abilities and creativity.
LeetCode API Limitations: Utilizing the LeetCode API posed a significant challenge due to its lack of comprehensive documentation and rate limiting constraints. To overcome this obstacle, we devised an alternative approach by building our own dataset of LeetCode problems. We wrote scripts to scrape and compile a comprehensive collection of problems, ensuring that we had access to the necessary data without being constrained by the limitations of the API. This solution allowed us to maintain flexibility and control over our data while providing a seamless user experience.
Recommendation Algorithm: Developing an effective strategy to recommend problems based on user profiles and actions proved to be a complex task. This challenge required extensive brainstorming sessions and iterative refinement of our approach. Through collaborative effort and critical analysis, we identified key points and devised a robust recommendation algorithm that aligns closely with user needs and learning objectives.
By addressing these challenges with creativity and determination, we were able to develop innovative solutions that enhance the functionality and user experience of AdaptiCode.
Tracks Applied (1)
Best Use of MongoDB Atlas
Our project, AdaptiCode, aligns perfectly with Major League Hacking's Best Use of MongoDB Atlas track by leveraging Mong...Read MoreMajor League Hacking
Technologies used