Personalized Learning System Using Semantic Scoring and User Data Filtering
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Solution Overview
Problem
Traditional electronic learning technologies provide a one-size-fits-all approach, failing to adapt to individual students' weaknesses, strengths, and learning styles, leading to inefficient use of study time and limited access to diverse learning materials, resulting in poor performance and high attrition rates.
Innovation Solution
A learning system that utilizes unsupervised machine learning to generate structured learning assets by tokenizing text documents, creating semantic models, scoring passages, selecting candidate knowledge items, filtering based on user data, and generating personalized learning assets, thereby optimizing study time and integrating diverse digital content sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a one-size-fits-all curriculum is provided to all students, then the course structure is simple and easy to implement, but student performance deteriorates and attrition increases due to inability to adapt to individual learning needs
Solution Approach 1:
The patent implements dynamic curriculum adaptation by continuously adjusting the learning path based on real-time student performance data. The system modifies course content, difficulty level, and pacing individually for each student, transforming the static one-size-fits-all curriculum into a dynamic, personalized learning experience that adapts to student needs throughout the course.
Solution Approach 2:
The system enables students to self-regulate their learning by automatically receiving personalized recommendations and feedback. The automated performance tracking and adaptive curriculum delivery allows students to manage their own learning pace and focus on areas needing improvement without requiring manual intervention or complex student effort to identify their own needs.
2Adaptability or versatility
If students are given a broad selection of source material, then learning flexibility and depth are improved, but the complexity of selecting and organizing appropriate materials increases
Solution Approach 1:
The system automatically performs the complex task of selecting, organizing, and presenting appropriate learning materials to students based on their individual performance data and learning objectives. This eliminates the need for students to manually navigate extensive source material collections, reducing their cognitive load while maintaining access to diverse and relevant content.
Solution Approach 2:
The system continuously monitors student performance and uses this feedback to dynamically adjust material recommendations. By analyzing real-time data on student interactions and performance, the system refines its selection of source materials to better match individual learning needs, creating a feedback loop that improves adaptability while managing complexity automatically.
3Adaptability or versatility
If students manually identify their weaknesses and strengths, then they can personalize their study approach, but time consumption increases and performance deteriorates due to student burden
Solution Approach 1:
The system performs automated self-assessment by continuously tracking student performance and generating personalized learning recommendations without requiring explicit student input. The automated analysis of performance data eliminates the time-consuming manual self-diagnosis process while still providing highly personalized study guidance, effectively transferring the self-service function from student to system.
Solution Approach 2:
The system implements continuous automated feedback loops that monitor student performance and immediately generate personalized recommendations. This real-time feedback mechanism eliminates the need for students to manually assess their own strengths and weaknesses, as the system continuously measures performance and adapts recommendations accordingly, saving time while maintaining personalization.
4Device complexity
If limited source material is provided in the course, then the course is easier to manage and deliver, but student learning is restricted and performance deteriorates
Solution Approach 1:
The system provides universal access to a comprehensive library of source materials while maintaining easy course management. By building a robust, multi-functional content repository that can be dynamically queried and presented in various formats, the system enables rich learning experiences without requiring manual curation or complex course structure management for each individual student path.
Data Source
AI summary
Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.