LEARNING CONTENT RECOMMENDATION APPARATUS, SYSTEM, AND OPERATION METHOD THEREOF FOR DETERMINING RECOMMENDATION QUESTION BY REFLECTING LEARNING EFFECT OF USER

1 . A learning content recommendation apparatus for determining a recommended question by reflecting a learning effect of a user, the learning content recommendation apparatus comprising:

a predicted score calculator configured to, on the basis of user information including a question previously solved by a user and a response of the user to the question, calculate predicted score information including a maximum predicted score, which is a predicted score obtained when the user correctly answers a candidate question, and a minimum predicted score, which is a predicted score obtained when the user incorrectly answers the candidate question;

a correct answer rate predictor configured to predict correct answer rate information, which is a probability that the user correctly answers the candidate question, on the basis of the user information; and

a recommended question determiner configured to calculate an expected score on the basis of one or more of the predicted score information, the correct answer rate information, and a degree of learning, and determine a recommended question according to the expected score,

wherein the recommended question determiner includes:

a learning degree calculator configured to calculate the degree of learning, which is a probability that, after a first question, which has been previously solved incorrectly by the user, being learned by the user, the user solves a second question that is the same as or similar to the first question again and answers the second question correctly; and

an expected score calculator configured to calculate a first expected score, in which the degree of learning is not reflected, on the basis of one or more of the predicted score information and the correct answer rate information, and calculate a second expected score, in which the degree of learning is reflected, on the basis of one or more of the first expected score, the maximum predicted score, and the degree of learning.

2 . The learning content recommendation apparatus of claim 1 , further comprising:

a sampler configured to receive question information from a question database and sample candidate questions for determining the recommended question; and

a user information storage configured to provide the user information to the predicted score calculator and the correct answer rate predictor for artificial intelligence prediction and store response information according to question solving of the user.

3 . The learning content recommendation apparatus of claim 2 ,

wherein the correct answer rate predictor is configured to predict the correct answer rate using an artificial neural network model related to one or more among a recursive artificial neural network (RNN), a long short-term memory (LSTM), a bidirectional LSTM, and a transformer structure-artificial neural network, and

in the transformer structure-artificial neural network, the question information is input to an encoder side and the response information is input to a decoder side to predict the correct answer rate.

4 . The learning content recommendation apparatus of claim 1 ,

wherein the expected score calculator includes a first algorithm, and

wherein the first algorithm calculates the first expected score on the basis of the predicted score information and the correct answer rate information.

5 . The learning content recommendation apparatus of claim 1 ,

wherein the expected score calculator includes a second algorithm, and

wherein the second algorithm calculates the second expected score on the basis of the predicted score information, the correct answer rate information, and the degree of learning.

6 . An operation method of a learning content recommendation apparatus for determining a recommended question by reflecting a learning effect of a user, the operation method comprising:

sampling, by a sampler, a candidate question for determining a recommended question;

receiving, by a predicted score calculator, the candidate question from the sampler and, on the basis of user information including a question previously solved by a user and a response of the user to the question, calculating predicted score information including a maximum predicted score, which is a predicted score obtained when the user correctly answers the candidate question, and a minimum predicted score, which is a predicted score obtained when the user incorrectly answers the candidate question;

receiving, by a correct answer rate predictor, the candidate question from the sampler and predicting correct answer rate information, which is a probability that the user correctly answers the candidate question, on the basis of the user information;

receiving, by a recommended question determiner, the predicted score information from the predicted score calculator and receiving the correct answer rate information from the correct answer rate predictor to calculate an expected score on the basis of one or more of the predicted score information, the correct answer rate information, and a degree of learning, and determining the recommended question according to the expected score; and

transmitting the recommended question to a user terminal,

wherein the determining the recommended question includes:

calculating the degree of learning, which is a probability that after a first question, which has been previously solved incorrectly by the user, being learned by the user, the user solves a second question that is the same as or similar to the first question again and answers the second question correctly;

calculating a first expected score, in which the degree of learning is not reflected, on the basis of one or more of the predicted score information and the correct answer rate information; and

calculating a second expected score, in which the degree of learning is reflected, on the basis of one or more of the first expected score, the maximum predicted score, and the degree of learning.