Electronic Text Retention Enhancement via Adaptive Display Interventions
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Solution Overview
Problem
Users reading electronic text often lose focus and concentration, leading to poor retention of information, especially for those with conditions like attention-deficit/hyperactivity disorder or dyslexia, with no existing solutions to identify degraded retention levels and provide enhancement actions.
Innovation Solution
A system that collects and analyzes user data such as eye-tracking, device interaction, and biometric data to determine if a retention action is needed, then applies actions like altering text characteristics, zoom levels, or letter scrambling to enhance retention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users read electronic text continuously without intervention, then reading speed is maintained, but information retention deteriorates
Solution Approach 1:
The system implements periodic retention actions by issuing interventions at regular intervals or when degradation thresholds are reached. The processor continuously monitors user data and issues retention actions periodically when needed, rather than continuously, thereby balancing information retention improvement with minimal disruption to reading flow and time consumption.
2Loss of information
If retention actions are issued frequently, then information retention improves, but reading flow is disrupted
Solution Approach 1:
The system employs feedback mechanisms by monitoring user data (eye-tracking, device interactions, biometric signals) and using this information to dynamically adjust when and what retention actions to issue. This closed-loop approach ensures actions are issued only when degradation is detected, optimizing the balance between improving retention and maintaining reading efficiency without excessive interventions.
Solution Approach 2:
The system applies partial action by issuing retention actions selectively rather than continuously. Retention actions are triggered only when specific conditions are met (e.g., when user data indicates degradation), avoiding excessive interventions that would disrupt reading flow while still providing sufficient stimulation to improve information retention where needed.
3Measurement precision
If user data collection is comprehensive, then retention action accuracy improves, but system complexity increases
Solution Approach 1:
The system leverages multi-functionality by using a single computing device to perform multiple roles: displaying electronic text, collecting various user data (eye-tracking, device interactions, biometric signals), processing this data to determine retention levels, and issuing retention actions. This universal approach consolidates complexity into one device rather than requiring separate specialized systems for each function.
Solution Approach 2:
The system implements self-service by utilizing the device's existing capabilities and resources to collect user data and process it for retention determination. The device leverages its own sensors, processors, and display mechanisms rather than requiring external specialized equipment, thereby reducing overall system complexity while maintaining comprehensive data collection for accurate retention monitoring.
Data Source
AI summary
Aspects of the present disclosure relate to enhancing reading retention of users reading electronic text. A set of user data associated with a user currently reading electronic text on a device is received, the set of user data indicative of a reading retention of the user. The set of user data is analyzed to determine whether a retention enhancement action should be issued. In response to a determination that a retention action should be issued, the retention enhancement action is issued at the device the user is currently reading electronic text on.