Abstract: Provided are a device and method for recommending educational content. The method includes acquiring a user's search information, acquiring a candidate webpage set on the basis of the search information, classifying candidate webpages included in the candidate webpage set into a first webpage group and a second webpage group, determining a target webpage on the basis of classification results, and transmitting the determined target webpage.

Abstract: Provided are a method of recommending preceding educational content and an educational content recommendation device for recommending preceding educational content. The method includes acquiring a language model of which training has been completed, updating the language model by tuning the language model to acquire a target language model, and determining preceding educational content through the target language model. The acquirement of the target language model includes acquiring an educational data set including first clustering data and second clustering data and updating the language model to predict a probability that the first clustering data is a next token of the second clustering data.

Abstract: Provided are a device and method for assessing a user's learning ability. The method includes acquiring target assessment data of a target user and a reference user related to a target domain, wherein the target assessment data includes question data related to the target domain and answer data of each of the target user and the reference user for the question data, acquiring a target neural network model of which training has been completed, acquiring comparison information representing the target user's ability in relation to the reference user's ability in the target domain through the target neural network model, and calculating the target user's virtual score in the target domain on the basis of the comparison information.

Abstract: Provided are a device and method for recommending educational content. The method includes acquiring a target user's learning data which includes log data including question data related to a question previously answered by the target user and answer data related to the target user's answer to the question, acquiring a question database including at least one candidate question, calculating the target user's predicted correct answer rate information for the candidate question on the basis of the candidate question and the learning data, acquiring the target user's ability information related to at least some of the log data, and determining recommendation content on the basis of the target user's ability information.

Abstract: Provided are a method of training a neural network for calculating a learning ability and a method of calculating a user's learning ability. The method of training a neural network includes acquiring an assessment database including data, which includes question information answered by a user at a second time point earlier than a first time point, the user's answer information to the question information, and the user's score information in a second assessment system, acquired from the second assessment system different from a first assessment system, generating an answer sequence from the assessment database by matching the answer information with the score information to prepare a training set, preparing a neural network for calculating the user's score information in the second assessment system on the basis of the answer information in the second assessment system, and training the neural network with the training set.

Abstract: According to an embodiment of the present invention, a method of recommending educational content includes acquiring search information of a user, extracting searched question information based on the search information, acquiring a solution content set related to the question information, the solution content set including first solution information and second solution information, calculating learning ability information of the user based on the search information, calculating an index related to an expected educational effect based on the learning ability information and the solution content set, selecting target solution content from the solution content set based on the index, and transmitting the target solution content.

Abstract: According to an embodiment of the present invention, a method of recommending educational content may include acquiring search information of a user; acquiring a candidate webpage set based on the search information, the candidate webpage set including a first webpage and a second webpage; calculating knowledge level information of the user based on the search information; calculating a first index related to an expected educational effect when the first webpage is provided to the user based on the knowledge level information and first content information included in the first webpage; calculating a second index related to an expected educational effect when the second webpage is provided to the user based on the knowledge level information and second content information included in the second webpage; selecting a target webpage based on the first index and the second index; and transmitting the target webpage.

Abstract: Provided are a device and method for recommending educational content. The method includes acquiring a user's learning data, wherein the learning data includes at least one of the user's first learning ability information at a first time point, the user's second learning ability information at a second time point, and the user's question answering information, acquiring the user's target learning ability information on the basis of the learning data, determining a neural network model on the basis of the target learning ability information, distributing resources corresponding to the determined neural network model, and acquiring educational content to be recommended to the user through the determined neural network model.

Abstract: According to an embodiment of a recommending educational content method includes: acquiring search information of target user; acquiring learning set information based on the search information; acquiring a search database of a plurality of users based on the leaning set information, the search database including user identification information and a reference value allocated according to whether the user searches for a question included in the learning set information; allocating a feature value according to whether to search for at least one question included in the learning set information based on the search information; generating a first matrix based on the reference value of the search database and the feature value related to the target user; transforming the first matrix into a second matrix based on similarity of the reference value and the feature value; and calculating a learning ability score of the target user based on the second matrix.

Abstract: The present invention relates to a user knowledge tracking method with more improved accuracy. An operation method of a user drop-out rate prediction system including a plurality of encoder neural networks and a plurality of decoder neural networks may include the steps of: inputting question information to a kth encoder neural network and inputting response information to a kth decoder neural network; generating query data, which is information about a question of which the correct answer probability is desired to be identified by the user, by reflecting a weight to the response information, and generating attention information to be used as a weight for the query data by reflecting a weight to the question information; and learning the user drop-out rate prediction system using the attention information as a weight for the query data.

Abstract: The present invention is to predict a correct answer probability of a user for a specific question with higher accuracy, and provide learning content having more increased efficiency. A method for operating a learning content recommendation system includes transmitting question information including information on a plurality of questions to a user, receiving solving result information that is the user's response for the plurality of questions, and training a user characteristic model based on the question information and the solving result information, wherein the training of the user characteristic model includes assigning a weight to the user characteristic model based on a degree of influence on a correct answer probability in a sequence of questions input to the user characteristic model.

Abstract: The present disclosure relates to a system capable of increasing the accuracy of a prediction result when predicting a user drop-out rate or predicting a correct answer probability of a user in an online learning environment, and further relates to an operation method of the system. A system according to the present disclosure may include a plurality of encoder neural networks and a plurality of decoders, and may input question information to a kth encoder neural network and input response information to kth decoder neural network to learn the system, thereby predicting the user drop-out rate information and the users correct answer probability information with higher accuracy on the basis of the learned system.

Abstract: An apparatus for evaluating a skill of a user according to an embodiment of the present application including: a transferable feature extraction unit configured to receive problem response information and test score information of a reference domain from a user terminal and extract at least one transferable feature from the problem response information or the test score information; a basic model training unit configured to train a basic model for predicting a test score of a user from the transferable feature and feature information that is usable in common for comparison of skills of a plurality of users in the reference domain and a target domain in which skill evaluation of the user is desired; and a model transfer performing unit configured to transfer the basic model to a skill evaluation model for predicting a test score in the target domain.

Abstract: A method and apparatus for correcting missing values in data are provided. A method of correcting missing values in basic data according to an embodiment includes a data extraction step, a prediction model configuration step, a first correction step, and a second correction step. The method corrects missing values in data by repeating the steps of generating a prediction model for correcting the missing value and correcting the missing value with the use of the prediction model.

Abstract: A hyperparameter optimization method performed by a hyperparameter optimization apparatus to optimize hyperparameters of a model includes calculating an evaluation score for each of a plurality of hyperparameter samples constituting a first hyperparameter sample set by applying each of the hyperparameter samples to a target model, performing weighted clustering of the hyperparameter samples by using the calculated evaluation scores as weights and constructing a second hyperparameter sample set based on the clustering result.

Abstract: Provided is a learning problem recommendation system for recommending problems through unification of forms of a score probability distribution. In some embodiments, the system generates a first problem candidate list by combining a preset number of problems, predicts a probability distribution of expected scores that a user will receive after the user solves the problems, determines a second problem candidate list on the basis of a result of comparing an extracted value extracted from a graph of the probability distribution of the expected scores to a preset reference value, predicts a learning effect that the user will have after the user solves the problems, determines a third problem candidate list on the basis of the learning effect, and determines a recommended problem list to recommend by filtering the first problem candidate list, the second problem candidate list, and the third problem candidate list according to a predetermined order.

Abstract: A learning content recommendation apparatus system, or method may be provided for determining a recommended question by reflecting a learning effect of a user. The apparatus, system or method may include: 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 and a minimum predicted score; a correct answer rate predictor configured to predict correct answer rate information, which is a probability that the user correctly answers the a 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 configured to determine a recommended question according to the expected score.

Abstract: Provided is a user score prediction device capable of reducing the amount of data used and increasing a prediction speed by predicting a score using only response comparison information obtained by mutually comparing responses of a plurality of users according to an embodiment of the present disclosure.

Abstract: A method and apparatus for correcting missing values in data are provided. A method of correcting missing values in basic data according to an embodiment includes a data extraction step, a prediction model configuration step, a first correction step, and a second correction step. The method corrects missing values in data by repeating the steps of generating a prediction model for correcting the missing value and correcting the missing value with the use of the prediction model.

Abstract: A hyperparameter optimization method performed by a hyperparameter optimization apparatus to optimize hyperparameters of a model includes calculating an evaluation score for each of a plurality of hyperparameter samples constituting a first hyperparameter sample set by applying each of the hyperparameter samples to a target model, performing weighted clustering of the hyperparameter samples by using the calculated evaluation scores as weights and constructing a second hyperparameter sample set based on the clustering result.