E-Learning System Personalized Learning Path Generation
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Solution Overview
Problem
E-learning platforms fail to provide users with a structured sequence of web-based materials that enhance understanding of a topic, as search engines return unordered results based on relevance rather than educational progression.
Innovation Solution
A computer-implemented method that segments primary web-based content into subtopics, retrieves supplemental materials from reference users' browsing histories, and generates a personalized sequence of web-based materials to build upon previous results, enhancing user comprehension through a cloud computing environment and neural network analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If search engines return results based on relevance algorithms, then users receive search results quickly, but users cannot learn from other users' experiences and results are not ordered by educational value
Solution Approach 1:
The system pre-processes and analyzes browsing histories of reference users before a query is submitted. User profiles, material sequences, and behavioral patterns are prepared in advance, enabling the system to quickly generate educational sequences without real-time analysis delays
Solution Approach 2:
The system introduces an intermediary layer between the search engine and the user. This intermediary (the e-learning system) receives search results, re-ranks them based on educational sequences derived from reference user behaviors, and presents them in an optimized order that balances speed and educational value
2Device complexity
If e-learning platforms provide unordered search results, then system complexity is reduced, but user understanding and comprehension of topics deteriorates
Solution Approach 1:
The system automatically generates personalized learning sequences without requiring manual curation by educators. It self-adjusts based on reference user behaviors, automatically analyzing browsing patterns and generating optimized material sequences, reducing the need for human intervention while maintaining high learning quality
Solution Approach 2:
The system dynamically changes the ordering parameter of search results from simple relevance scores to educational progression sequences. By transforming the ranking criterion based on analyzed user behaviors, it improves learning outcomes without fundamentally redesigning the entire system architecture
3Quantity of substance
If supplemental materials are retrieved from multiple reference users, then material diversity and completeness improve, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis and aggregation of reference user browsing histories in advance. User profiles and material preferences are pre-processed and stored in optimized data structures, enabling rapid retrieval and sequence generation when queries are submitted without processing delays
Solution Approach 2:
The system retrieves browsing data from a selected subset of reference users rather than analyzing all available users. By strategically choosing representative reference users whose behaviors best match the query context, it achieves sufficient material diversity without the computational overhead of processing excessive data
Data Source
AI summary
Aspects of the invention include receiving an identity of a primary web-based material from a user, wherein the primary web-based material describes a topic. Subparts of the primary web-based material are indexed. Supplemental web-based materials accessed by a plurality of reference users are retrieved, wherein the supplemental web-based materials relate to the subparts. A sequence of supplemental web-based materials is generated for the user to follow to gain an understanding of the topic.