Automated Content Evaluation with Hierarchical Grading Models
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Solution Overview
Problem
Existing machine learning models for grading are large and cumbersome, requiring large datasets for training and are not customizable to individual grader preferences, leading to reduced accuracy with small data sets and an inability to handle unique or customized grading criteria.
Innovation Solution
Customizable machine learning models are trained using pre-existing data and iterative user feedback to adapt to specific grading preferences, allowing for efficient grading of custom prompts and small data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing machine learning models for grading are used, then grading can be automated, but the models are large and cumbersome requiring large datasets for training
Solution Approach 1:
The patent segments the grading task into multiple hierarchical levels: topic-level grading, step-level grading, and component-level grading. This segmentation allows the system to process and evaluate different aspects of student responses independently, reducing the overall data requirements compared to holistic grading approaches.
Solution Approach 2:
The system dynamically adapts the grading granularity based on the specific content and requirements of each grading task. It can switch between evaluating entire responses, intermediate steps, or specific components depending on what is most appropriate for the given context, optimizing performance with limited data.
2Measurement precision
If existing machine learning models for grading are used, then grading can be automated, but they require large datasets for training leading to reduced accuracy with small data sets
Solution Approach 1:
By breaking down grading into hierarchical levels (topic, step, component), the system can achieve high accuracy on small datasets by focusing evaluation on specific aspects rather than requiring comprehensive training data for entire responses.
Solution Approach 2:
The system performs partial evaluation by assessing only the necessary components of a response rather than requiring complete response analysis, enabling accurate grading with limited training data by focusing computational resources on critical evaluation points.
3Extent of automation
If existing machine learning models for grading are used, then grading can be automated, but they are not customizable to individual grader preferences
Solution Approach 1:
The system dynamically configures grading behavior based on user-defined preferences and requirements. It adapts the granularity level, evaluation criteria, and focus areas according to individual grader needs, making the automated grading process customizable without sacrificing automation benefits.
Solution Approach 2:
The system allows modification of grading parameters such as evaluation granularity, weightings for different components, and criteria thresholds. These parameter changes enable customization to individual grader preferences while maintaining the automated grading framework.
4Extent of automation
If existing machine learning models for grading are used, then grading can be automated, but they cannot handle unique or customized grading criteria
Solution Approach 1:
The hierarchical segmentation of grading into topic, step, and component levels provides a flexible framework that can accommodate unique and customized grading criteria by allowing different evaluation standards at each level according to specific requirements.
Solution Approach 2:
The system enables customization of grading criteria through parameter adjustments at various hierarchical levels, allowing unique evaluation standards to be implemented while maintaining automated processing of grading tasks.
Data Source
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AI summary
Systems and methods for automated content delivery and evaluation are disclosed herein. The system can include a memory. The memory can include a content library database including a plurality of problems and data for stepwise evaluation of each of the plurality of problems. The system can include at least one server. The at least one server can automatically decompose a content item into a plurality of potential steps and associate attributes with the potential steps. The at least one server can receive a response from a user for the content item, identify steps in the received response, and select a next action based the identified steps of the received response.