Feedback Request Modifier for Volume Control
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Solution Overview
Problem
Current systems for online educational feedback lack the ability to efficiently manage and request feedback from multiple contributors, often resulting in inadequate or excessive feedback volumes, which can hinder the learning experience and evaluation processes for both learners and instructors.
Innovation Solution
A system comprising server and client hardware devices with processor-executed instructions that allow instructors and learners to shape and request feedback, using a request modifier to adjust feedback requests based on past data to achieve a desired volume, and a feedback aggregator to collect and present feedback from multiple contributors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If feedback is requested from multiple contributors without modification, then the volume of feedback increases, but the feedback may become excessive or irrelevant to the learner's needs
Solution Approach 1:
The system performs preliminary actions by analyzing past feedback data and predicting future feedback volumes before actually requesting feedback. The request modifier uses historical data to pre-calculate optimal request parameters, ensuring that feedback requests are tailored to achieve desired volumes without being excessive or insufficient.
Solution Approach 2:
The system implements a feedback loop where past feedback volumes and characteristics are analyzed to improve future feedback requests. The request modifier continuously learns from historical data patterns to refine predictions and adjustments, creating a self-improving system that optimizes feedback relevance and volume over time.
2Ease of operation
If the feedback request parameters are strictly controlled by instructors, then the feedback volume can be managed, but the system lacks adaptability to different learner needs and contexts
Solution Approach 1:
The system transitions from static, instructor-defined feedback parameters to dynamic, context-adaptive parameters. The request modifier automatically adjusts feedback request characteristics based on real-time analysis of past data and specific learner contexts, making the system flexible and adaptable while maintaining controlled feedback volumes.
Solution Approach 2:
The system enables itself to automatically optimize feedback requests without requiring constant instructor intervention. The request modifier uses historical data to self-adjust request parameters, predicting optimal feedback volumes and characteristics for different learners and contexts autonomously.
3Ease of operation
If feedback requests are customized for each learner based on past data, then the relevance of feedback improves, but the system complexity increases
Solution Approach 1:
The system uses copying by analyzing patterns from past feedback requests and results to create templates for future requests. Instead of building complex customization logic from scratch, the request modifier replicates successful patterns from historical data, simplifying the system while maintaining high relevance through data-driven template matching.
4Measurement precision
If the system collects and analyzes past feedback data to predict future feedback volumes, then the accuracy of feedback volume prediction improves, but the processing time and computational resources increase
Solution Approach 1:
The system applies partial action by focusing computational resources on analyzing only the most relevant features of past feedback data needed for volume prediction. Rather than processing all possible data elements, the request modifier identifies and analyzes key predictive features, achieving sufficient accuracy with reduced processing time and computational overhead.
Data Source
AI summary
An online feedback network provides feedback from contributors to a feedback recipient for a project. A request modifier may receive a default request from a data source and allow the feedback recipients to use the default request, modify the default request and/or allow the feedback recipient to create an initial request in requesting feedback for each feedback recipient's project from the contributors. The request modifier may also modify the default or initial request so that the request from the feedback recipient receives a desired volume, type, source or network of feedback. For instance the request modifier may increase the number of contributors receiving the request or simplify the type of requested feedback in order to increase the volume of feedback received by the feedback recipients based on previous requests for feedback and the volume of feedback received by the past requests. The submitted request may be stored for future use.