STATE ESTIMATION APPARATUS, QUESTION RECOMMENDATION APPARATUS, STATE ESTIMATION METHOD, QUESTION RECOMMENDATION METHOD, AND PROGRAM
Provided is a technique for recommending a question suitable for use in future study to a learner. Included are a correct answer rate prediction unit that estimates a predicted correct answer rate of a question by using a learned neural network from an input vector obtained from a test result of a learner of K questions or by using a decoder of a learned neural network from a latent variable vector obtained from an input vector obtained from the test result of the learner of the K questions, and a question selection unit that selects a question to be recommended to the learner from among selection candidate questions by using a reference predicted correct answer rate that is a predicted correct answer rate to be a reference for recommending a question to be solved and predicted correct answer rates of the selection candidate questions among the K questions.
1 - 2 . (canceled)
3 . A question recommendation apparatus comprising:
setting input information as information indicating one of a positive state, a negative state, or an unknown state,
setting an input vector as a vector obtained from K pieces (K is an integer of 2 or more) of the input information x 1 , . . . , x K by expressing the input information using two bits of a positive information bit set to 1 in a case where the input information is information indicating the positive state, or set to 0 in a case where the input information is information indicating the unknown state or information indicating the negative state, and a negative information bit set to 1 in a case where the input information is information indicating the negative state, or set to 0 in a case where the input information is information indicating the unknown state or information indicating the positive state,
setting p(x) as a probability that the input information x is information indicating the positive state,
setting an output vector as a vector having probabilities p(x 1 ), . . . , p(x K ) for the K pieces of input information x 1 , . . . , x K as elements,
a processing circuitry configured to record a parameter of a learned neural network, including an encoder that calculates a latent variable vector having a latent variable as an element from the input vector and a decoder that calculates an output vector from the latent variable vector, that has been learned by repeating parameter update processing of uprating parameters of the encoder and the decoder, using a loss function including a loss term that has a larger value as the probability p(x) for the input information x is smaller in a case where the input information x is information indicating the positive state, has a larger value as the probability p(x) for the input information x is larger in a case where the input information x is information indicating the negative state, and is substantially 0 in a case where the input information x is information indicating the unknown state;
setting the K pieces of input information as test results of K questions, and setting the positive state, the negative state, and the unknown state as a correct answer, a wrong answer, and no answer, respectively,
calculate an output vector from an input vector obtained from test results x k (k=1, . . . , K) of a learner of the K questions by using the learned neural network, select a probability corresponding to a question of a selection candidate for a question to be recommended to the learner from elements p(X k ) (k=1, . . . , K) of the output vector, and obtain the probability as a predicted correct answer rate of the question of the selection candidate for the question to be recommended to the learner; and
setting p i_1 , . . . , p i_M (where M is an integer of 1 or more and K or less, i m (m=1, . . . , M) satisfies 1≤i m ≤K, and i m and i m′ (m≠m′) are different from each other) as predicted correct answer rates of the questions i 1 , . . . , i M that are the selection candidates for the question to be recommended to the learner among the K questions, and setting a reference predicted correct answer rate as a predicted correct answer rate that is reference for recommending a question to be solved,
select questions i m_1 , . . . , i m_N to be recommended to the learner from among the questions i 1 , . . . , i M by using the reference predicted correct answer rate and the predicted correct answer rates p i_1 , . . . , p i_M of the questions i 1 , . . . , i M that are the selection candidates for the question to be recommended to the learner among the K questions.
4 . A question recommendation apparatus comprising:
setting input information as information indicating one of a positive state, a negative state, or an unknown state,
setting an input vector as a vector obtained from K pieces (K is an integer of 2 or more) of the input information x 1 , . . . , x K by expressing the input information using two bits of a positive information bit set to 1 in a case where the input information is information indicating the positive state, or set to 0 in a case where the input information is information indicating the unknown state or information indicating the negative state, and a negative information bit set to 1 in a case where the input information is information indicating the negative state, or set to 0 in a case where the input information is information indicating the unknown state or information indicating the positive state,
setting p(x) as a probability that the input information x is information indicating the positive state,
setting an output vector as a vector having probabilities p(x 1 ), . . . , p(x K ) for the K pieces of input information x 1 , . . . , x K as elements,
a processing circuitry configured to record a parameter of a decoder of a learned neural network, including an encoder that calculates a latent variable vector having a latent variable as an element from the input vector and a decoder that calculates an output vector from the latent variable vector, that has been learned by repeating parameter update processing of uprating parameters of the encoder and the decoder, using a loss function including a loss term that has a larger value as the probability p(x) for the input information x is smaller in a case where the input information x is information indicating the positive state, has a larger value as the probability p(x) for the input information x is larger in a case where the input information x is information indicating the negative state, and is substantially 0 in a case where the input information x is information indicating the unknown state;
setting the K pieces of input information as test results of K questions, and setting the positive state, the negative state, and the unknown state as a correct answer, a wrong answer, and no answer, respectively,
calculate an output vector from a latent variable vector corresponding to an input vector obtained from test results X k (k=1, . . . , K) of a learner of the K questions by using the decoder of the learned neural network, select a probability corresponding to a question of a selection candidate for a question to be recommended to the learner from elements p(X k ) (k=1, . . . , K) of the output vector, and obtain the probability as a predicted correct answer rate of the question of the selection candidate for the question to be recommended to the learner; and
setting p i_1 , . . . , p i_M (where M is an integer of 1 or more and K or less, i m (m=1, . . . , M) satisfies 1≤i m ≤K, and i m and i m′ (m≠m′) are different from each other) as predicted correct answer rates of the questions i 1 , . . . , i M that are the selection candidates for the question to be recommended to the learner among the K questions, and setting a reference predicted correct answer rate as a predicted correct answer rate that is reference for recommending a question to be solved,
select questions i m_1 , . . . , i m_N to be recommended to the learner from among the questions i 1 , . . . , i M by using the reference predicted correct answer rate and the predicted correct answer rates p i_1 , . . . , p i_M of the questions i 1 , . . . , i M that are the selection candidates for the question to be recommended to the learner among the K questions.
5 . The question recommendation apparatus according to claim 3 , wherein
the questions i m_1 , . . . , i m_N to be recommended to the learner include only questions that have not taken by the learner.
6 . The question recommendation apparatus according to claim 3 , wherein
the processing circuitry selects questions in which the predicted correct answer rates p i_m (m=1, . . . , M) are included in a predetermined range including the reference predicted correct answer rate as the questions i m_1 , . . . , i m_N to be recommended to the learner from among the questions i 1 , . . . , i M of the selection candidates for the question to be recommended to the learner among the K questions.
7 . The question recommendation apparatus according to claim 3 , wherein
the processing circuitry preferentially selects questions in which the predicted correct answer rates p i_m (m=1, . . . , M) are close to the reference predicted correct answer rate as the questions i m_1 , . . . , i m_N to be recommended to the learner from among the questions i 1 , . . . , i M of the selection candidates for the question to be recommended to the learner among the K questions.
8 - 11 . (canceled)
12 . A non-transitory computer-readable storage medium which stores a program for causing a computer to function as the question recommendation apparatus according to claim 3 .
13 . The question recommendation apparatus according to claim 4 , wherein
the questions i m_1 , . . . , i m_N to be recommended to the learner include only questions that have not taken by the learner.
14 . The question recommendation apparatus according to claim 4 , wherein
the processing circuitry selects questions in which the predicted correct answer rates p i_m (m=1, . . . , M) are included in a predetermined range including the reference predicted correct answer rate as the questions i m_1 , . . . , i m_N to be recommended to the learner from among the questions i 1 , . . . , i M of the selection candidates for the question to be recommended to the learner among the K questions.
15 . The question recommendation apparatus according to claim 4 , wherein
the processing circuitry preferentially selects questions in which the predicted correct answer rates p i_m (m=1, . . . , M) are close to the reference predicted correct answer rate as the questions i n_1 , . . . , i m_N to be recommended to the learner from among the questions i 1 , . . . , i M of the selection candidates for the question to be recommended to the learner among the K questions.
16 . A non-transitory computer-readable storage medium which stores a program for causing a computer to function as the question recommendation apparatus according to claim 4 .