Information Processing Apparatus for Adaptive Word Review Scheduling
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Solution Overview
Problem
Existing electronic dictionaries lack an efficient mechanism to prioritize and schedule word reviews based on user search history and learning patterns, leading to ineffective learning support.
Innovation Solution
An information processing apparatus and method that acquires user search history data, determines the frequency of word quiz output by setting conditions based on search frequency and retention patterns, and schedules push notifications for word reviews using spaced repetition techniques to optimize learning timing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If quizzes are repeatedly output to users, then learning reinforcement is improved, but user annoyance and disengagement increase
Solution Approach 1:
The system implements spaced repetition by outputting quizzes at periodically increasing intervals based on user performance. Instead of repeated daily quizzes, the system schedules reviews at optimized intervals (e.g., 1 day, 3 days, 7 days, 15 days) that reinforce learning while minimizing user annoyance through appropriate timing spacing.
Solution Approach 2:
The system dynamically changes the frequency parameter of quiz output based on user mastery level. For words marked as mastered, the quiz frequency is reduced or stopped; for unmastered words, frequency is increased. This adaptive parameter adjustment balances learning reinforcement with user experience.
2Quantity of substance
If word review frequency is increased for all searched words, then learning coverage is improved, but learning effectiveness decreases due to information overload
Solution Approach 1:
The system applies different review frequencies to different words based on local user needs. Each word receives a customized review schedule according to its difficulty level and user mastery status, rather than applying a uniform review frequency to all words. This localized approach optimizes learning effectiveness for each specific word.
Solution Approach 2:
The system segments the user's vocabulary into different categories (mastered, partially mastered, unmastered) and applies different review strategies to each segment. This segmentation allows the system to manage learning coverage efficiently by focusing intensive review on problematic words while reducing or eliminating review for mastered words.
3Measurement precision
If the system tracks multiple registration information items for each word, then learning analysis precision is improved, but system complexity increases
Solution Approach 1:
The system extracts only the most critical registration information items needed for quiz frequency determination (e.g., search count, mastery status) from the complete set of available user data. By selecting and processing only essential information, the system maintains high learning analysis precision while avoiding the complexity of processing and managing all possible user interaction data.
Data Source
AI summary
An information processing apparatus comprises at least one processor that executes a program stored in a storage unit, the processor being configured to: acquire a plurality of registration information items relating to a word searched by a user; determine whether or not each of the registration items relating to the word satisfies a condition that is set in association with each of the registration information items, and derive the number of registration information items that satisfy the condition; and set a frequency of outputting a quiz relating to the word in a case where the quiz is repeatedly output to a value corresponding to the derived number.