Learning Platform Integrating Memory Retention Analysis
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Solution Overview
Problem
Existing learning platforms separate learning and testing processes, leading to user inconvenience, inefficiency, and limited methods for assessing memory retention rates, which do not account for individual memory characteristics and the timing of reviews.
Innovation Solution
A learning platform that integrates learning and testing using machine learning to calculate memory retention rates, employing a memory assist server with a log data collector, data pre-processor, and machine learning module to train an artificial neural network, providing a single channel for user convenience and enhanced learning efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If learning and testing processes are separated into different channels, then each process can be independently managed, but user convenience deteriorates due to requiring multiple sign-ins and the learning process becomes tedious
Solution Approach 1:
The patent merges the learning process and testing process into a single integrated learning platform. Users sign in once to access both learning materials and testing functions, eliminating the need for multiple sign-ins. The platform provides a unified interface where users can seamlessly transition between learning content and assessments, thereby improving user convenience while maintaining independent management of different functions through modular architecture.
2Ease of manufacture
If traditional testing methods are used to check learning progress, then implementation is simple, but measurement precision deteriorates because existing methods cannot accurately assess memory retention rates considering individual memory characteristics and review timing
Solution Approach 1:
The patent transforms the assessment approach by introducing multiple parameters including individual memory characteristics, review timing, and spaced repetition intervals. The system dynamically adjusts testing parameters based on user performance data and memory retention patterns. This enables precise measurement of memory retention rates by considering how different factors affect individual users, moving beyond simple pass/fail testing to a nuanced assessment model.
3Stability of the object's composition
If existing learning methods provide theoretically appropriate learning cycles, then learning structure is established, but productivity deteriorates because they fail to account for individual user memory characteristics and optimal review timing
Solution Approach 1:
The patent makes the learning cycle dynamic by automatically adjusting review timing and spacing based on individual user memory characteristics and performance data. The system transitions from fixed theoretical learning cycles to adaptive cycles that optimize review intervals for each user. This dynamic adjustment enables the platform to identify and reinforce optimal review timing, thereby improving learning efficiency while maintaining a structured approach to skill acquisition.
Data Source
AI summary
The present invention relates to a learning platform to stimulate the user to review by obtaining a memory retention rate in a user using machine learning. According to an embodiment, a learning platform for obtaining a memory retention rate using machine learning comprises a memory assist server configured to generate a dataset of questions and answers including one or more questions and answers thereto. A user terminal is configured to receive the dataset of questions and answers from the memory assist server, to prepare the dataset of questions and answers in accordance with a predetermined rule for testing, and to output the prepared dataset as a test to the user. The memory assist server is configured to calculate the memory retention rate using an artificial network trained using learning data including as a feature a percentage of correct answers for the dataset of questions and answers.