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: Provided is a method of training a neural network model for calculating an uncertainty index, the method including: obtaining a reference answering data set of a plurality of reference users, calculating expected score information of the reference user from the reference answering data set; obtaining actual score information of the reference user; obtaining a training set on the basis of the reference answering data set, the expected score information, and the actual score information, the training set including label information that is defined as a difference between the expected score information and the actual score information; and training a first neural network model for calculating an uncertainty index related to accuracy of the expected score information of the reference user from the reference answering data set using the training set.

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: A learning content evaluation apparatus includes a problem information processing unit configured to generate a problem embedding vector on the basis of problem information included in pre-collected problem content; an artificial intelligence (AI) model training unit configured to generate AI learning information including a weight determined using a result of training an AI model on the basis of the problem embedding vector and a user embedding vector, in which solution result data of a user for the pre-collected problem content is reflected; and a correct answer probability prediction unit configured to calculate correct answer probability information about a probability of being answered correctly by the user for the added problem, on the basis of a problem embedding vector of the added problem content and the AI learning information.

Abstract: A learning content recommendation system according to an embodiment includes: a solution result data collection unit configured to communicate with a user terminal in a wired or wireless manner to collect solution result data for a problem solved by a user; a latent factor calculation unit configured to calculate one or more latent factors serving as a basis element for predicting the probability of a correct answer from the solution result data; and an embedding performance unit configured to generate, from discrete values of the solution result data, an initial embedding vector including consecutive numbers graspable by an artificial neural network on the basis of the latent factors, and weight-adjust the initial embedding vector to determine the weight-adjusted initial embedding vector as an imbedding vector to be used for training the artificial neural network.

Abstract: The present invention relates to a user knowledge tracing method with more improved accuracy, and an operating method for a user knowledge tracing system including a plurality of encoder neural networks and a plurality of decoder neural networks includes: inputting exercise information to a k-th encoder neural network and inputting response information to a k-th decoder neural network; generating query data, which is information on an exercise for which a user is to predict a correct answer probability, by reflecting a weight to the response information and generating attention information to be used as a weight for the query data by reflecting the weight to the exercise information; and training the user knowledge tracing system by using the attention information as the weight for the query data.