GPU Workflow Manager for Digital Assessment Item Response Modeling
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Solution Overview
Problem
Current systems lack effective methods for evaluating the latent abilities of responders to digital assessments and assessing the quality of digital assessment items, particularly in virtual computing environments, where existing technologies fail to accurately determine item parameters and responder abilities.
Innovation Solution
A system utilizing a workflow manager module with general-purpose graphics processing units (GPGPU) to apply a modified two-parameter item response theory model, performing maximum likelihood estimation and gradient descent optimization to determine item parameters such as difficulty, discrimination, and hint change values, and estimating responder abilities based on assessment data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computing methods are used to evaluate item parameters and responder abilities, then the system complexity remains manageable, but the measurement precision and reliability of assessment quality evaluation deteriorate
Solution Approach 1:
The patent replaces traditional CPU-based computational methods with GPU-based parallel processing to substitute the mechanical computation approach. This enables more complex and precise item parameter estimation (difficulty, discrimination, hint change values) through parallel maximum likelihood estimation across multiple assessment items, thereby improving measurement precision while managing system complexity through specialized hardware utilization
Solution Approach 2:
The patent changes the computational parameters by transitioning from sequential CPU processing to parallel GPU processing. This parameter change enables simultaneous calculation of multiple item parameters across large datasets, improving estimation accuracy and reliability while maintaining manageable system complexity through standardized GPU architecture
2Reliability
If comprehensive item parameter estimation is performed to improve assessment quality, then the reliability of ability evaluation improves, but the processing time and computational resources increase
Solution Approach 1:
The patent segments the computational task by dividing the assessment items into separate processing units that can be handled simultaneously on the GPU. Each assessment item's parameters (difficulty, discrimination, hint change) are calculated independently through parallel threads, enabling comprehensive parameter estimation without proportional increase in total processing time
Solution Approach 2:
The patent implements continuous parallel computation where multiple item parameters are estimated simultaneously rather than sequentially. The GPU maintains continuous processing of multiple assessment items in parallel, ensuring that comprehensive reliability evaluation is achieved without significant time penalty compared to traditional sequential methods
3Measurement precision
If maximum likelihood estimation is applied to determine item parameters, then the measurement precision of difficulty and discrimination values improves, but the computational complexity increases
Solution Approach 1:
The patent substitutes complex sequential maximum likelihood estimation computations with parallel GPU-based algorithms. This substitution maintains the mathematical rigor of MLE for estimating difficulty and discrimination values while distributing the computational complexity across multiple parallel processing units, making the algorithm more manageable and efficient
Data Source
AI summary
Systems and methods of the present invention provide for estimating latent ability of responders to a digital assessment in the form of ability scores and estimating item parameters an assessment item of the digital assessment including difficulty scores and discrimination scores. Maximum likelihood estimation may be performed based on an item response theory model to estimate the item parameters. Supervisory, extraction, and worker modules of a workflow manager module may initiate general purpose graphics processing unit instances and cause these instances to perform the maximum likelihood estimation calculations. The item response theory model may be a two parameter model that is modified to account for changes in difficulty caused by the use of hints.