Riiid Inc.

Chan Bae

Byung Soo Kim

Jae We Heo

Jung Hyun Cho

Yeong Min Cha

Hyun Bin Loh

Jin Hwan Lee

Dong Min Shin

Young Duck Choi

Jin Eon Baek

Young Nam Lee

Pil Jae Chae

Seo Yon Park

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

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Seoul, South Korea

Chan Bae