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.

There is provided an information processing apparatus including a processor that obtains, from a first P2P database, evaluation information for evaluating learning of a user, which is obtained by an acquisition device, and performs evaluation on learning performed by the user on the basis of the evaluation information.

Methods, systems and media for context-aware estimation of student attention in online learning are described. An attention monitoring system filters or restricts the time periods in which student attention is monitored or assessed to those time periods in which student attention is important. These time periods of high attention importance may be determined by processing data from the teacher, such as audio data representing the teacher's voice and/or visual presentation data representing slides or other visual material being presented to the students. Various types of presenter data from the teacher and attendee data from the students may be used in assessing the importance of attention and each student's attention during each time period. The presenter may be provided with feedback in various forms showing student attention performance aggregated or segmented according to various criteria.

Systems and methods are presented to disclose a gamified startup ecosystem wherein the participants comprising various real and role-playing stakeholders of the startup ecosystem are enabled and incentivized to interact with each other in their respective roles in a plurality of ways that draw upon gamification to make the participants' experiences interactive, valuable, interesting, educational, and rewarding for them in their various roles.

A geolocationing system and method for providing awareness in a multi-space environment, such as a hospitality environment or educational environment, are presented. In one embodiment of the geolocationing system, a vertical and horizontal array of gateway devices is provided. Each gateway device includes a gateway device identification providing an accurately-known fixed location within the multi-space environment. Each gateway device includes a wireless transceiver that receives a beacon signal from a proximate wireless-enabled personal locator device. The gateway devices, in turn, send gateway signals to a server, which determines an estimated location of the wireless-enabled personal locator device.

Systems and techniques to apply NFT content to immersive environment generation within an NFT platform are illustrated. One embodiment includes a method for rendering content. The method receives, from one or more sensory instruments, sensory input. The method processes the sensory input into a background source. The method receives a non-fungible token (NFT), wherein the NFT includes one or more character modeling elements. The method processes the one or more character modeling elements from the NFT into a character source. The method produces an immersive environment including features from the background source and features from the character source.

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.

In the prediction device, the acquisition means acquires student data related to the student. The preprocessing means generates training data based on the student data. The learning means generates at least one model that predicts the promotion situation of students based on the training data, by machine learning. The prediction means predicts the promotion situation of a subject student from the student data of the subject student using the generated model.

A system 80 for personalized e-leaming includes a Hierarchical Knowledge Tracing (HKT) model unit 81 which includes two sequential models comprising of a lower level model and a higher level model, wherein the lower level model estimates and updates the estimate of knowledge state of a learner from a question-response data of the learner while the learner is active (in-session) on the e-leaming application and predicts the probability of answering a question within the domain using the estimate of knowledge state, and the higher level model updates the knowledge state estimate of the lower level model when a new session starts.

A skills learning method for a student gathers objective data relating to the student in response to various stimuli, and produces a predicted feedback units as a function of the objective data using a machine learning-base classifier. The method can include training a neural network using objective data of student interactions and associated subjective assessments of a skill of each objective data. The method includes receiving a new dataset with objective data of a new student and an associated subjective assessment of a skill of the first student represented by the new objective data. A predicted assessment of the skill of the new objective data is calculated by inputting the new objective data into the neural network. The method can include updating the neural network by combining the initial dataset and the new dataset and recompiling the neural network to fit the model dataset based on a learning algorithm.