Riiid Inc.

Young Duck Choi

Byung Soo Kim

Jae We Heo

Jung Hyun Cho

Yeong Min Cha

Dong Min Shin

Jin Eon Baek

See Woo Lee

Young Nam Lee

Hyun Bin Loh

June Young Park

Jin Hwan Lee

Pil Jae Chae

Seo Yon Park

Chan Bae

Provided is an apparatus for training a knowledge tracking model, which is an apparatus for predicting a correct answer probability of a user on the basis of data augmentation, the apparatus including: a problem-solving data storage unit configured to store problem-solving data in which a problem solved by a user and a response of the user to the problem are mapped; a data augmentation performing unit configured to receive the problem-solving data from the problem-solving storage unit and convert the problem-solving data to generate augmented data; a regularization performing unit configured to receive the augmented data from the data augmentation performing unit and perform a regularization operation using a regularization loss function determined on the basis of a data augmentation method that is performed; and a model training unit configured to input the augmented data to a knowledge tracking model, allow the knowledge tracking model to learn a weight representing a relationship between a problem-solving record included in the augmented data and a correct answer probability of the user, and train the knowledge tracking model for predicting a correct answer probability of the user for a specific problem on the basis of the weight.

Apparatus and system for training knowledge tracking model based on data augmentation and operation method thereof

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 kencoder neural network and input response information to kdecoder 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.

System for predicting user drop-out rate and tracking user knowledge based on artificial intelligence learning and method therefor

According to an embodiment of the present disclosure, a pre-training modeling system for predicting an educational element using a pre-trained AI model includes: a pre-training unit configured to pre-train an AI model, through an evaluation element that can be automatically collected every time a user solves an individual question by receiving an interaction element according to an offline user's operation from a user terminal, classifying an AI element of educational content into the interaction element and the educational element, and intersecting the interaction element and the educational element; a fine-tuning unit configured to perform an additional training operation with respect to the pre-trained AI model, according to an educational element to be predicted; and an educational element prediction unit configured to predict, on the basis of the fine-tuned AI model, one or more pieces of information among test scores, grades, qualifications, and review information indicating whether the user got a correct answer when re-solving the same question, which are information that must be separately input from the user because the amount of information automatically collected every time the user solves an individual question is small or cannot be automatically collected.

Pre-training modeling system and method for predicting educational element

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Seoul, South Korea

Young Duck Choi