Personalized Learning Content Recommendation via Probability Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional educational content is not personalized, leading to inefficient learning as it does not account for individual differences in problem-solving abilities and learning efficiency, resulting in decreased student interest and effectiveness.
Innovation Solution
A method using a machine learning framework to analyze user problem-solving data, calculating learning efficiency, and recommending content based on probability of correct answers and problem difficulty, excluding already mastered topics to focus on areas of improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If educational content is provided in uniform packages to all students, then the content delivery is simple and standardized, but the learning efficiency and student interest decrease due to lack of personalization
Solution Approach 1:
The patent segments the uniform educational content package into individual problem units, each with associated example data. By dividing the content into discrete, analyzable units, the system can selectively recommend specific problems to individual students based on their performance, transforming a monolithic package into a personalized learning path without requiring complete customization of the entire content set.
Solution Approach 2:
The system changes the parameter of content selection from static (fixed package) to dynamic (probabilistic recommendation). By calculating the probability of correct answer for each student and using this to determine which problems to recommend, the system adapts the educational content parameters based on individual student characteristics and performance data.
2Productivity
If students solve all problems in a workbook to ensure comprehensive learning, then the learning coverage is complete, but the time efficiency decreases and student motivation is lost
Solution Approach 1:
The patent applies partial action by recommending only a subset of problems from the complete workbook to each student. Instead of requiring students to solve all 700+ problems, the system calculates which specific problems will be most beneficial based on probability of correct answer, allowing students to focus on a partial set of high-value problems that maximize learning efficiency.
Solution Approach 2:
The system uses feedback from student performance data (example selection data) to continuously refine problem recommendations. By analyzing which examples students select and their correctness, the system adjusts the probability calculations and recommends subsequent problems that target specific learning gaps, creating a feedback loop that optimizes learning efficiency over time.
3Measurement precision
If educational content is customized for each student based on individual performance, then the learning efficiency increases, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent introduces an intermediary computational layer that processes student performance data through probability calculations. This intermediary system (the recommendation engine) acts as a mediator between raw student data and personalized content selection, using mathematical probability models to translate performance data into specific problem recommendations without requiring complex artificial intelligence or machine learning systems.
Solution Approach 2:
The system enables self-service by automatically collecting example selection data from students and autonomously generating personalized problem recommendations without requiring manual teacher intervention. The automated data collection and probability-based recommendation system allows the platform to serve each student individually at scale without proportionally increasing operational complexity.
Data Source
AI summary
Disclosed herein is a method of providing user-customized learning content in a service server, which includes a) for a specific subject, configuring a problem database including at least one of multiple-choice problems each including at least one example, providing the problem to user devices, and collecting example selection data of users for the problem from the user devices, b) estimating a probability of right answer to the problem for each of the users using the example selection data of each of the users, and assuming that any user selects an example of any problem, calculating, for each problem, a change rate of probabilities of right answer to all problems contained in the problem database for the user, and sorting the problems contained in the problem database in the order of the high change rate to recommend them to the user.