Neural Network Assessment Engine for Student Knowledge State
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Solution Overview
Problem
Existing knowledge space theory-based approaches fail to accurately assess a student's knowledge state due to the large amount of data in online learning systems, leading to inconsistencies and inaccuracy in assessments, as they do not output probabilities consistent with feasible states after each question.
Innovation Solution
A neural network-based assessment system that uses a probabilistic determination to identify a student's knowledge state and adaptively select questions, generating probability estimates consistent with the feasible knowledge states by updating feature vectors based on student responses, and employing a recurrent neural network to iteratively assess and refine the knowledge state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If knowledge space theory-based probabilistic search is used to assess student knowledge state, then the system can handle large amounts of educational content, but the assessment accuracy decreases and inconsistencies occur
Solution Approach 1:
The patent replaces the traditional probabilistic search mechanism (mechanical system) with a neural network-based assessment engine. The neural network learns optimal assessment strategies from historical data and directly predicts knowledge states, substituting the step-by-step probabilistic search with a learned predictive model that achieves both scalability and accuracy.
Solution Approach 2:
The patent introduces an intermediary component - the neural network model - that mediates between the large volume of educational content and the assessment task. This intermediary processes and synthesizes information from the knowledge structure and student responses, producing consistent probability estimates without requiring exhaustive search through all possible knowledge states.
2Adaptability or versatility
If traditional probabilistic search approaches are used, then the system can process knowledge structures, but the reliability of probability outputs decreases
Solution Approach 1:
The patent implements feedback mechanisms where the neural network's probability outputs are continuously refined based on student responses. The system uses the predicted knowledge states to select subsequent questions and updates probability estimates iteratively, ensuring consistency with the knowledge structure while adapting to individual student performance patterns.
Solution Approach 2:
The patent performs preliminary training of the neural network model using historical assessment data before actual assessments. This preliminary action allows the model to learn the relationships between student responses and knowledge states in advance, ensuring reliable and consistent probability outputs during actual assessments without requiring complex runtime computations.
3Adaptability or versatility
If the number of feasible knowledge states is expanded to cover more content, then the system can assess more topics, but the uncertainty in knowledge state identification increases
Solution Approach 1:
The patent segments the large knowledge structure into hierarchical levels and clusters of related items. The neural network processes this segmented structure efficiently, learning patterns within each segment and combining them to produce accurate overall knowledge state predictions, thereby managing uncertainty even as coverage expands.
Data Source
AI summary
Methods and systems relating to the use of a neural network model executed by a processing device to determining an initial knowledge state of a student relating to a subject. A vector representation of a set of items associated with the subject is generated. The neural network model is executed to generate an assessment including at least a portion of the set of items relating to the subject. A first item of the set of items is provided to the student and a first response to the first item is received from the student. Based on the first response to the first item, an updated vector representation is generated. Based on the updated vector representation and the initial knowledge state, a first set of probabilities associated with an updated knowledge state of the student corresponding to the set of items relating to the subject is generated.