Search-History Hint Selection for Personalized Language Learning
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Solution Overview
Problem
Conventional language learning systems lack the ability to provide personalized hint information based on user search history, leading to inefficient learning support.
Innovation Solution
An information processing system that includes a server and terminal device, which acquire search history information to determine and present hint information tailored to the user's proficiency level by analyzing search history conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional language learning systems present fixed hint information for test questions, then the system structure remains simple, but the learning effectiveness and user engagement deteriorate due to lack of personalization
Solution Approach 1:
The system performs preliminary actions by acquiring and storing search history information about words before the test is taken. This pre-collected data is then used to determine appropriate hint information, allowing the system to provide personalized hints without complex real-time analysis during the test itself.
Solution Approach 2:
The system utilizes feedback from user search behavior (search history information) to dynamically determine hint information. By analyzing what words users have searched for and their search patterns, the system adapts the hint content to match the user's actual learning needs and proficiency level.
2Adaptability or versatility
If the system provides generic hint information without analyzing user behavior, then the system complexity remains low, but the adaptability to different user proficiency levels deteriorates
Solution Approach 1:
The system enables self-service by using the user's own search history information as the basis for determining personalized hint information. The user's past search behavior automatically serves as the input data needed to customize the hint content, eliminating the need for manual user profiling or complex adaptive algorithms.
Solution Approach 2:
The system changes parameters by selecting different hint information based on the discrimination result of search history conditions. Depending on whether the search history satisfies predetermined conditions, the system presents different types or levels of hint information, thereby adapting to varying user proficiency levels through parameter selection rather than complex transformation.
3Loss of information
If the system presents comprehensive hint information for all test questions, then the information completeness is high, but the loss of time for processing and presenting information increases
Solution Approach 1:
The system extracts only the necessary hint information based on the user's specific search history and the particular test question being asked. Rather than presenting all available hint information, the system selectively extracts and presents only what is relevant to the user's demonstrated needs, reducing processing time while maintaining information completeness for the specific context.
Data Source
AI summary
A central processing unit (CPU) of an information processing device acquires search history information on a word related to a question asked to a user and determines hint information related to the question to be presented to the user based on a discrimination result obtained by determining whether or not the acquired search history information satisfies a predetermined search history condition.