personalized E-learning Recommendation system
Objective
The primary objective of the "Personalized E-learning Recommendation System" is to deliver a tailored learning experience to users by recommending courses based on individual preferences, learning behavior, and academic performance. The system aims to enhance user engagement by offering relevant content through the use of machine learning algorithms like Decision Tree, Random Forest, and XGBoost. Another key objective is to integrate pathways, allowing recommendations to adjust based on real-time feedback and progress. Additionally, the system strives to improve learning outcomes, optimize knowledge acquisition, and reduce dropout rates by providing students with personalized, goal-oriented, and accessible educational experiences, all while ensuring smooth integration with existing e-learning platforms.
Abstract
ABSTRACT:
The "Personalized E-learning Recommendation System" aims to transform online education by offering tailored content and course suggestions to learners based on their individual preferences, learning behaviors, and performance. The system utilizes machine learning algorithms, including Decision Tree, Random Forest, and XGBoost, to analyze user data such as academic history, interests, and activity patterns. By integrating pathways and real-time feedback, the system ensures that recommendations align with the learner’s skill levels and goals. The system’s modular architecture facilitates smooth integration with existing e-learning platforms and is scalable to accommodate a wide range of users. Focusing on boosting engagement, improving learning outcomes, and minimizing dropout rates, this solution empowers learners with a personalized and dynamic educational experience, accessible across various devices. The project is built using Python, Flask, and modern front-end technologies to create a user-friendly, responsive interface.
Keywords: Personalized Learning, E-learning, Recommendation System, Machine Learning, Decision Tree, Random Forest, XGBoost, Educational Technology, Learning Analytics.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
4.2 Hardware Requirements:
u Processor - I3/Intel Processor
u Hard Disk -160 GB
u RAM - 8 GB
4.3 Software Requirements:
u Operating System : Windows 7/8/10 .
u Server side Script : HTML, CSS & JS.
u IDE : Pycharm.
u Libraries Used : Numpy, IO, OS, Django, keras.
u Technology : Python 3.6+.
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