Automated Feedback Revision Using Sentiment Grouping
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Solution Overview
Problem
Electronic feedback in learning systems often conveys a different tone than intended, can be ineffective or unhelpful, and may be challenging to provide effectively in an electronic format.
Innovation Solution
A method for automatically revising feedback in electronic learning systems using machine-learned models to identify sentiment groups, process feedback text data, and generate suggested revisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users provide electronic feedback to evaluate other users, then feedback can be electronically recorded and transmitted, but the feedback may convey a different tone than intended and may be ineffective or even offensive
Solution Approach 1:
The patent introduces an intermediary system that processes electronic feedback between the sender and recipient. This intermediary analyzes the feedback text, detects emotional tone, and provides suggestions for improvement before the feedback is delivered. This resolves the contradiction by preserving the original electronic feedback format while adding a mediating layer that ensures the intended tone is maintained, making the feedback more effective and less likely to be offensive.
2Productivity
If electronic feedback is used to evaluate submissions, then feedback can be efficiently distributed, but it may be challenging to provide effective feedback in an electronic format
Solution Approach 1:
The patent implements a self-service mechanism where the feedback system automatically analyzes and improves its own output. The system uses natural language processing and sentiment analysis to self-evaluate the feedback being generated, then automatically suggests or applies improvements to enhance tone and effectiveness. This maintains the efficiency of electronic feedback distribution while improving quality through automated self-correction capabilities.
3Reliability
If users submit feedback electronically, then feedback can be stored and tracked in the system, but the feedback may convey a different tone to recipients than intended by the user
Solution Approach 1:
The patent implements a feedback loop where the system analyzes the emotional tone of submitted feedback and provides real-time suggestions for improvement. The system uses sentiment analysis to detect emotional content, compares it against intended tone, and returns recommendations to the user before final submission. This creates a reliable feedback delivery mechanism that preserves emotional tone information through iterative refinement based on system feedback.
Data Source
AI summary
Systems and methods for automatically revising feedback in an electronic learning system are provided. The method involves operating at least one processor to: receive feedback text data submitted by a user to evaluate another user; identify a plurality of sentiment groups in the feedback text data, each sentiment group consisting of a portion of the feedback text data associated with a common sentiment; select at least one feedback processing module to process each sentiment group, the at least one feedback processing module comprising at least one machine-learned model; for each sentiment group, process the corresponding portion of the feedback text data using the at least one machine-learned model to determine at least one suggested revision for the portion of the feedback text data; and generate a revised version of the feedback text data indicating each suggested revision for the feedback text data.