Riiid Inc.

Yeong Min Cha

Jae We Heo

Dong Min Shin

Jae Min Lee

Byung Soo Kim

Jung Hyun Cho

Young Jun Jang

Young Duck Choi

Jin Eon Baek

Young Nam Lee

Jin Hwan Lee

Hyun Bin Loh

See Woo Lee

Young Ku Lee

Chan Bae

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

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

Disclosed herein is a method of providing user-customized learning content in a service server, which includes a) for a specific subject, configuring a problem database including at least one of multiple-choice problems each including at least one example, providing the problem to user devices, and collecting example selection data of users for the problem from the user devices, b) estimating a probability of right answer to the problem for each of the users using the example selection data of each of the users, and assuming that any user selects an example of any problem, calculating, for each problem, a change rate of probabilities of right answer to all problems contained in the problem database for the user, and sorting the problems contained in the problem database in the order of the high change rate to recommend them to the user.

Machine learning method, apparatus, and computer program for providing personalized educational content based on learning efficiency

Disclosed is a method of providing user-customized learning content. The method includes: a step a of configuring a question database including one or more multiple-choice questions having one or more choice items and collecting choice item selection data of a user for the questions; a step b of calculating a modeling vector for the user based on the choice item data and generating modeling vectors for the questions according to each choice item; and a step c of calculating choice item selection probabilities of the user based on the modeling vectors of the user and the modeling vectors of the questions.

Method, apparatus, and computer program for operating machine-learning framework

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Seongnam, South Korea

Yeong Min Cha