Transferable Feature Extraction for AI Skill Evaluation
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Solution Overview
Problem
Existing user skill evaluation models require large amounts of actual test score information for training, which is time-consuming and costly to collect, leading to lower accuracy in predicting test scores and grades, especially in domains with limited data.
Innovation Solution
An apparatus and method that extracts transferable features from a reference domain rich in data and uses an AI model to predict test scores in target domains with insufficient data, by training a basic model on common feature information and transferring it to the target domain for skill evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If actual test score information is collected directly from users through tests, then training data quantity increases, but time and cost increase significantly
Solution Approach 1:
The system performs preliminary actions by collecting and storing problem-solving data during normal learning activities before actual tests are administered. This preliminary data collection occurs naturally as students solve problems during study sessions, eliminating the need for separate data collection tests and reducing both time and cost while still accumulating sufficient training data.
Solution Approach 2:
The system uses an intermediary approach by introducing a transferable feature extraction mechanism that bridges the gap between limited actual test data and the need for comprehensive training data. By extracting features from readily available problem-solving data and transferring them to predict test scores, the system obtains sufficient training information without conducting time-consuming actual tests.
2Quantity of substance
If actual test score information is collected directly from users through tests, then training data quality improves, but cost increases significantly
Solution Approach 1:
The system performs preliminary actions by collecting and storing problem-solving data during normal learning activities before actual tests are administered. This preliminary data collection occurs naturally as students solve problems during study sessions, eliminating the need for separate data collection tests and reducing both time and cost while still accumulating sufficient training data.
Solution Approach 2:
The system uses an intermediary approach by introducing a transferable feature extraction mechanism that bridges the gap between limited actual test data and the need for comprehensive training data. By extracting features from readily available problem-solving data and transferring them to predict test scores, the system obtains sufficient training information without conducting time-consuming actual tests.
3Measurement precision
If user skill evaluation models are generated manually for each test domain, then model accuracy improves, but productivity decreases
Solution Approach 1:
The system applies universality by creating a domain-agnostic skill evaluation model that can be applied across multiple test domains. The transferable feature extraction mechanism identifies universal learning patterns that transcend specific domains, allowing a single model architecture to serve multiple purposes and domains, thereby improving productivity without sacrificing accuracy.
Solution Approach 2:
The system uses parameter changes by adjusting the input features and domain-specific parameters of a universal model rather than creating entirely new models for each domain. This allows the same base model to adapt to different domains through parameter adjustment, significantly improving model generation speed while maintaining accuracy through the transferable feature extraction mechanism.
4Measurement precision
If user skill evaluation models are generated manually for each test domain, then model performance improves, but time consumption increases
Solution Approach 1:
The system applies universality by creating a domain-agnostic skill evaluation model that can be applied across multiple test domains. The transferable feature extraction mechanism identifies universal learning patterns that transcend specific domains, allowing a single model architecture to serve multiple purposes and domains, thereby improving productivity without sacrificing accuracy.
Solution Approach 2:
The system uses parameter changes by adjusting the input features and domain-specific parameters of a universal model rather than creating entirely new models for each domain. This allows the same base model to adapt to different domains through parameter adjustment, significantly improving model generation speed while maintaining accuracy through the transferable feature extraction mechanism.
5Productivity
If test score prediction is performed without sufficient actual test data, then productivity increases, but measurement precision decreases
Solution Approach 1:
The system uses an intermediary approach by introducing a transferable feature extraction mechanism that bridges the gap between limited actual test data and the need for accurate predictions. By extracting relevant features from readily available problem-solving data and transferring them to predict test scores, the system achieves reasonable prediction accuracy without requiring extensive actual test data.
Solution Approach 2:
The system applies copying by replicating the structure and learning patterns from domains with sufficient data and applying them to domains with limited data. The transferable feature extraction mechanism copies effective feature representations and model architectures from data-rich domains, enabling accurate predictions in data-scarce domains without requiring extensive actual test data.
Data Source
AI summary
An apparatus for evaluating a skill of a user according to an embodiment of the present application including: a transferable feature extraction unit configured to receive problem response information and test score information of a reference domain from a user terminal and extract at least one transferable feature from the problem response information or the test score information; a basic model training unit configured to train a basic model for predicting a test score of a user from the transferable feature and feature information that is usable in common for comparison of skills of a plurality of users in the reference domain and a target domain in which skill evaluation of the user is desired; and a model transfer performing unit configured to transfer the basic model to a skill evaluation model for predicting a test score in the target domain.