Information Processing for Online-Class Viewer Interest Estimation
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Solution Overview
Problem
Existing devices fail to accurately evaluate a student's learning state in online classes due to lack of consideration for the class scene and teacher's state.
Innovation Solution
An information processing device that includes an estimation method determination unit and a degree-of-interest estimation unit, which utilize parameters such as line-of-sight and class situation to estimate a viewer's interest level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a device evaluates learning state based only on student behavior information, then the evaluation process is simple, but the evaluation accuracy is insufficient
Solution Approach 1:
The patent combines multiple data sources including student behavior information, class scene images, and teacher state information into a unified evaluation system. The estimation method determination unit integrates these diverse data types to comprehensively assess learning state, thereby improving evaluation accuracy while managing system complexity through structured data fusion.
Solution Approach 2:
The system employs a multi-functional approach where a single evaluation device processes various types of data (behavioral data, visual data from class scenes, and teacher state data) through unified processing mechanisms. This allows the device to accurately evaluate learning states by considering multiple dimensions of information simultaneously.
2Measurement precision
If the evaluation does not consider class scene and teacher state, then the system is simpler, but it cannot accurately reflect actual learning conditions
Solution Approach 1:
The patent segments the evaluation process into distinct components: student behavior information processing, class scene analysis, and teacher state assessment. Each component processes specific information independently before the estimation method determination unit integrates these segmented results, ensuring comprehensive information capture while maintaining systematic processing.
Solution Approach 2:
The system adds new dimensions to the evaluation by incorporating class scene images and teacher state information alongside traditional student behavior data. This dimensional expansion allows the evaluation to capture contextual factors and environmental influences that were previously unaccounted for, thereby improving overall evaluation accuracy.
3Adaptability or versatility
If the device uses fixed estimation methods, then the system is easier to operate, but it cannot adapt to different class situations
Solution Approach 1:
The estimation method determination unit dynamically selects and adjusts estimation methods based on the current class situation and available data. Rather than using fixed methods, the system adapts its evaluation approach in real-time according to the context, making it versatile while maintaining ease of operation through automated adaptation.
Solution Approach 2:
The system changes evaluation parameters and estimation methods based on detected class situations. When different scenarios are identified (such as interactive sessions, lectures, or group work), the system adjusts the weighting and selection of parameters accordingly, enabling adaptive evaluation without requiring manual intervention.
Data Source
AI summary
An information processing device includes: an estimation method determination unit that determines an estimation method for estimating a degree of interest of a viewer who views content on the basis of at least a situation of the content; and a degree-of-interest estimation unit that estimates the degree of interest on the basis of the estimation method and a plurality of parameters including a parameter regarding a line-of-sight of the viewer.