Question Recommendation Using Monotonic Latent Ability Vectors
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Solution Overview
Problem
Existing methods, such as those using variational autoencoders, can analyze a learner's academic abilities but fail to recommend specific questions for future study to improve weak areas.
Innovation Solution
A neural network is trained with a loss function that ensures monotonicity in latent variable vectors, using input vectors representing test results, to recommend questions based on the learner's strengths and weaknesses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a variational autoencoder is used to analyze test results, then academic ability analysis is achieved, but question recommendation capability is lost
Solution Approach 1:
The patent extends the variational autoencoder framework to perform multiple functions: both academic ability analysis and question recommendation. The system maintains the original VAE's capability to analyze test results while adding a recommendation module that uses the learned latent representations to suggest appropriate questions for further study, making the system universally applicable to both analytical and prescriptive educational tasks.
2Loss of time
If learning is performed to be substantially the same, then learning time restriction is satisfied, but complete learning is not achieved
Solution Approach 1:
The patent modifies the learning objective by changing the parameter comparison from exact equality to substantial equality based on a threshold criterion. The learning process terminates when the difference between input and output vectors falls below a predetermined threshold, allowing the system to achieve sufficient learning accuracy within time constraints without requiring complete convergence, thus balancing learning time and learning accuracy.
3Measurement precision
If monotonicity is enforced in latent variable vectors, then ability representation is improved, but system complexity increases
Solution Approach 1:
The patent introduces dynamic constraints during the learning process to enforce monotonicity in latent variable vectors. Rather than fixing the monotonicity property statically, the system dynamically adjusts the latent representations during training to satisfy monotonicity requirements, allowing the model to learn ability representations that naturally adhere to monotonic relationships without requiring complex post-processing or rigid structural constraints.
Data Source
AI summary
Provided is a technique for recommending a question suitable for use in future study to a learner. Included are, setting a first latent variable vector as a latent variable vector obtained from an input vector obtained from test results of a learner of K questions using an encoder of a learned neural network, a first decoder unit that calculates a first predicted correct answer rate vector from the first latent variable vector using a decoder of the learned neural network, a latent variable vector generation unit that generates a second latent variable vector by a predetermined method from the first latent variable vector, a second decoder unit that calculates a second predicted correct answer rate vector from the second latent variable vector using the decoder of the learned neural network, and a question selection unit that preferentially selects an element having a larger value from elements of a vector obtained by subtracting the first predicted correct answer rate vector from the second predicted correct answer rate vector, and obtains a question corresponding to the index of the selected element as a question to be recommended to the learner.