Abstract: 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.

Abstract: The present disclosure relates to a method and device for providing questions for learning. According to the present disclosure, a method by which a server provides questions for question includes step a of displaying learning content on a first region of a screen displayed on a terminal of a learner; step b of fixedly displaying an AI object on a second region of the screen; step c of determining whether a learning progress of the learner satisfies a predetermined object activation condition; and step d of displaying an AI message corresponding to the object activation condition on a third region of the screen adjacent to the AI object while the AI object is activated, when the learning progress satisfies the object activation condition. According to the present disclosure, a user interface may be adaptively changed according to a question solving situation of a learner, thereby inducing continuous learning of a user.

Abstract: 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.

Abstract: The present invention relates to a method for estimating, by a learning data analysis server, a predicted score of a user for a test question, the method comprising: step a of establishing a question database including a plurality of questions, of collecting solving result data of a plurality of users for the questions, and of estimating a correct answer probability of a random user for a random question by using the solving result data; step b of establishing, from the question database, at least one set of mock test questions similar to a set of external test questions which has been set without using the question database; and step c of estimating, for the random user who has not solved the mock test question set, a predicted score for the mock test question set by using the correct answer probability of the user for each question constituting the mock test question set, and providing the estimated predicted score as a predicted score for the external test questions.

Abstract: The present invention relates to a method for establishing a diagnostic question set, of a data analysis framework, for a new user, the method comprising: step a of establishing a question database including a plurality of questions, of collecting solving result data of the user for the questions, and of applying the solving result data to the data analysis framework, thereby calculating modeling vector(s) of the questions and/or the user; step b of extracting, from the question database, at least one candidate question for establishing the diagnostic question set; step c of identifying a user for whom solving result data for the candidate question exist, and another question for which solving result data of the user exist; step d of applying only the solving result data of the user for the candidate question to the data analysis framework, thereby calculating a modeling vector of a virtual user; step e of applying the modeling vector of the virtual user, thereby calculating a virtual correct answer probabili

Abstract: The present invention relates to a method for displaying learning content of a terminal and an application program therefor. The method for displaying learning content of a terminal of the present invention comprises the steps of: displaying text with a group of questions on a first layer; displaying a first question card corresponding to a first question on a second layer; and when a continuous touch gesture in a touch input is received in the second layer, displaying, on the second layer, a second question card corresponding to a second question according to the touch gesture, wherein the question card includes one or more examples that are selectable by a learner. According to the present invention, it is possible to effectively display learning content including a large amount of information such as multiple choice questions on a limited size screen.

Abstract: The present disclosure relates to a method for providing a customized educational content by an electronic device, the method including: a step a of generating a set of tags of sub-learning elements by listing a learning element of a particular subject in a tree structure, and designating analysis groups of the tags; a step b of generating structural information of a question by indexing an incorrect-answer tag to each of distractors of the question; a step c of inquiring about a result of a user test so as to calculate a user conquest rate for each of the analysis groups; and a step d of providing a user-customized question by using at least one of the structural information of the question and the user conquest rate.

Abstract: 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.