Riiid Inc.

Jin Eon Baek

Byung Soo Kim

Jae We Heo

Jung Hyun Cho

Yeong Min Cha

Dong Min Shin

Young Duck Choi

Young Nam Lee

Hyun Bin Loh

Jin Hwan Lee

See Woo Lee

Pil Jae Chae

Seo Yon Park

Chan Bae

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

Growing community of inventors

Seoul, South Korea

Jin Eon Baek