Systems and methods for providing a universal platform for at-home health testing and diagnostics are provided herein. In particular, a health testing and diagnostic platform is provided to connect medical providers with patients and to generate a unique, private testing environment. In some embodiments, the testing environment may facilitate administration of a medical test to a patient with the guidance of a proctor. In some embodiments, the patient may be provided with step-by-step instructions for test administration by the proctor within a testing environment. The platform may display unique, dynamic testing interfaces to the patient and proctor to ensure proper testing protocols and accurate test result verification.

The disclosure discloses a method for analyzing educational big data on the basis of maps. The method includes acquiring educational resource data and storing the educational resource data into databases according to certain data structures; constructing theme map layers for each analysis theme, classifying and indexing data according to the analysis themes, and superimposing the theme map layers onto base maps to form data maps; analyzing data of the theme map layers according to the analysis themes and acquiring theme analysis results; extracting the data of the multiple theme map layers in target regions, fusing the data and acquiring region analysis results; acquiring learning preference of users; combining the learning preference of the users according to content of user requests and searching the region analysis results in response to the user requests. The disclosure further discloses a system for analyzing the educational big data on the basis of the maps.

A method for estimating concept understanding is provided. The method includes the steps of: generating concept-specific correctness/incorrectness sequence data with respect to at least one user, with reference to data on a result of the at least one user solving at least one question associated with at least one concept; and estimating a first user's understanding of a first concept using a concept-specific understanding estimation model that is trained on the basis of the concept-specific correctness/incorrectness sequence data.

In order to achieve the above object, an information processing apparatus according to an embodiment of the present technology includes an evaluation unit. The evaluation unit sets an evaluation value relating to an educational situation on the basis of a correlation between educator action information, which is a classification result obtained by classifying an action of an educator into one of a plurality of educator action patterns, and learner action information, which is a classification result obtained by classifying an action of a learner into one of a plurality of learner action patterns.

A computer implemented method for determining the veracity of a completed task for validating the completed task towards the progression of a goal is disclosed. The completed task is a medical procedure performed by a first party. The goal is to compile a predetermined number of completed tasks required by a third party. The method includes receiving and storing registration data from a first party, second party, and third party. The method receives medical case information from the first party. The method causes an SMS message to be sent to the second party for soliciting approval of the case data. If an approval is received, then the method generates a validated medical case record. The method stores the validated medical case record to the blockchain and mints a token associated with the validated medical case. The method tracks the progression towards the goal based on validated medical cases.

Provided are a device and method for recommending educational content. The method includes acquiring a target user's learning data which includes log data including question data related to a question previously answered by the target user and answer data related to the target user's answer to the question, acquiring a question database including at least one candidate question, calculating the target user's predicted correct answer rate information for the candidate question on the basis of the candidate question and the learning data, acquiring the target user's ability information related to at least some of the log data, and determining recommendation content on the basis of the target user's ability information.

A system and process for dynamic security monitoring of test taking. The system combines historical information about the test taker or relevant conditions, and includes sensors which objectively monitor test taker's actions, inactions, and involuntary response to the test given before and during the test, and at a specific test location. The information collected from the sensors is compared to a plurality of predetermined individual risk factors, which indicate a possibility of test fraud by the individual test taker to create a test event risk profile. The individual risk profile is combined with non-individual specific risk factors to create a holistic test event security profile. Security resources are then dynamically assigned to or removed from the test event based on the unique test event security profile.

Provided are a method of training a neural network for calculating a learning ability and a method of calculating a user's learning ability. The method of training a neural network includes acquiring an assessment database including data, which includes question information answered by a user at a second time point earlier than a first time point, the user's answer information to the question information, and the user's score information in a second assessment system, acquired from the second assessment system different from a first assessment system, generating an answer sequence from the assessment database by matching the answer information with the score information to prepare a training set, preparing a neural network for calculating the user's score information in the second assessment system on the basis of the answer information in the second assessment system, and training the neural network with the training set.

This disclosure includes systems and techniques that verify an educational quality of online videos, employing a platform that enables experts to endorse and/or recommend videos that are more readily accessible to end users. Certain aspects include a computing device for a service provider that receives a query from a user for video content. The computing device forwards the query to a hosting service, and in response to the query, the computing device receives, from the hosting service, a list of video content items it hosts. The computing device identifies, for each video content item within the list of video content items, an endorsement value associated with one or more experts. In some aspects, the computing device generates an aggregated list of video content items from a plurality of lists of video content items based on endorsement values. The computing device provides recommendations to the user using the endorsement values.

A surgical skill training system includes: a data collecting unit configured to collect actual surgical skill data on a patient of an operating surgeon; an image providing server configured to generate a 3-dimensional (3D) surgical image for surgical skill training, based on the actual surgical skill data; and a user device configured to display the 3D surgical image, wherein the image providing server includes: a patient image generating unit configured to generate a patient image, based on patient information of the patient; a surgical stage classifying unit configured to classify the actual surgical skill data into actual surgical skill data for each surgical stage performed by the operating surgeon; and a 3D image generating unit configured to generate the 3D surgical image by using the patient image, and feature information detected from the actual surgical skill data.