Learning Path Generation Using Structured Graphs
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Solution Overview
Problem
Users face challenges in efficiently navigating and understanding specific topics from scattered information across multiple webpages and structured courses, which are either too broad or require consulting a human expert, making it difficult to get a tailored and time-bound learning experience.
Innovation Solution
A method and system using machine learning to extract key phrases from learning resources, determine their context, form key phrase groups, establish relationships, and generate a structured graph to create a personalized learning path tailored to the user's understanding and time requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a user navigates through multiple webpages from different websites to learn a topic, then access to vast learning resources is achieved, but the time and effort required to understand the topic increases significantly
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between the user and the scattered learning resources. This system automatically crawls, parses, and structures information from multiple webpages, creating a synthesized learning path that presents consolidated information to the user, thereby reducing the time and effort required to navigate and understand the topic.
Solution Approach 2:
The patent extracts essential information from multiple webpages by identifying and pulling out key content, removing redundant or irrelevant material. This extraction process creates a condensed, focused learning resource that maintains the value of multiple sources while eliminating the time cost of navigating through them all.
2Ease of operation
If a user enrolls in a structured course to learn a topic, then a systematic learning approach is provided, but the course covers broad syllabus that includes unnecessary content for specific purposes
Solution Approach 1:
The patent applies local quality by customizing the learning path to match the user's specific needs and goals. Instead of providing a uniform broad syllabus, the system adjusts the content scope, depth, and focus locally according to what the user actually requires, presenting only the relevant portions of the topic while maintaining systematic organization.
Solution Approach 2:
The patent makes the learning path dynamic and adaptive, allowing it to be customized based on user feedback, performance, and specific learning objectives. The system can adjust the scope and sequence of content in real-time, expanding or contracting the syllabus to match the user's specific purposes rather than following a fixed broad curriculum.
3Adaptability or versatility
If a user consults a human expert to learn a topic, then personalized guidance is received, but the process of finding, validating, and meeting the expert is complex and time-consuming
Solution Approach 1:
The patent creates an automated system that copies or replicates the functionality of human expert guidance through AI algorithms, machine learning models, and automated content analysis. This digital copy provides personalized guidance without requiring the user to go through the complex process of finding, validating, and scheduling meetings with actual human experts.
Solution Approach 2:
The patent enables self-service learning where the system automatically analyzes user needs, generates personalized learning paths, and provides guidance without human intervention. The automated system serves itself by continuously improving through machine learning, eliminating the need for users to engage with human experts while still providing adaptive, personalized support.
4Quantity of substance
If scattered information from multiple webpages is used for learning, then comprehensive coverage of a topic is achieved, but the information is difficult to navigate and comprehend efficiently
Solution Approach 1:
The patent segments the comprehensive but scattered information from multiple webpages into organized, manageable units. It divides the content into logical sections, topics, and subtopics with clear hierarchies, making the vast information coverage navigable and comprehensible while maintaining the completeness of the original sources.
Solution Approach 2:
The patent merges information from multiple scattered webpages into a unified, integrated learning structure. By combining related content from different sources and presenting it in a consolidated format with cross-references and synthesized explanations, it maintains comprehensive coverage while dramatically improving navigability and comprehension.
Data Source
AI summary
This disclosure relates generally to information processing, and more particularly to method and system for generating a learning path for a topic. The method may include extracting a plurality of key phrases from each of a plurality of learning resources related to the topic, determining a learning context for each of the plurality of learning resources based on the plurality of key phrases, forming a set of key phrase groups from among the plurality of key phrases for each of the plurality of learning resources, determining a relationship among the key phrases in each of the set of key phrase groups based on the learning context, generating a structured graph for the plurality of learning resources based on the plurality of key phrases and the relationship among the key phrases, and generating the learning path for the topic based on the structured graph for the plurality of learning resources.