A learning effect estimation apparatus includes a model storage memory storing a model that takes learning data as input, the learning data being data on learning results of users and being assigned with categories for different learning purposes. There is a correct answer probability generation unit that inputs the learning data to the model to generate the correct answer probability of each of the categories; a correct answer probability database that accumulates time-series data of the correct answer probability for each of the users; and a comprehension and reliability generation unit that acquires range data, the range data being data specifying a range of categories for estimating a learning effect for a specific user, and generates a comprehension.

A system for sending topic-specific digital messages from professionals to their clients with information that is more relevant and applicable to the needs of the recipients based upon prior interactions with the professional.

A system for sending topic-specific digital messages from professionals to their clients with information that is more relevant and applicable to the needs of the recipients based upon prior interactions with the professional.

A method of automated analysis of learning content's impact on agent performance according to an embodiment includes automatically determining a first set of performance metrics for an agent for a predefined first period before the agent participated in a learning module in response to notification of completion in the learning module, automatically determining a second set of performance metrics for the agent for a predefined second period after the agent participated in the learning module in response to determining that the predefined second period has elapsed, computing a first set of performance metric differences between those sets of metrics, and performing correlation analysis to determine whether the learning module significantly effects one or more performance metrics of the agent based on a plurality of performance metric differences computed for a plurality of agents, wherein the plurality of performance metric differences includes the first set of performance metric differences.

A method for assessing learning comprehension regarding a topic includes modifying a fundamental illustrative model to illustrate a first set of assessment assets of a first learning object of learning objects to produce a first assessment illustrative model. The fundamental illustrative model is based on illustrative assets of a lesson that includes the learning objects. The method further includes obtaining a first assessment response for the first assessment illustrative model. When the first assessment response is favorable, the method further includes modifying the fundamental illustrative model to illustrate a second set of assessment assets of a second learning object of the learning objects to produce a second assessment illustrative model and obtaining a second assessment response for the second assessment illustrative model.

A skill acquisition platform may receive input identifying a target role for a user, may identify users from the plurality of users that have worked in the target role, may identify, based on a similarity metric, a subset of the users that have worked in the target role and have similar career trajectories to the user, may cluster vectors of the user and vectors of the subset of the users that have worked in the target role to generate a plurality of skill groups, may generate a directed network graph that represents links between the plurality of skill groups, may identify one or more paths between a first skill group associated with the user and a second skill group associated with the target role, and may automatically generate a descriptive analysis of the one or more paths.

A device, system and method for positioning monitors in schools is provided. One or more memories store: adversarial relationship data identifying adversarial relationships between students at a school; and schedule data indicative of spatiotemporal locations of the students having the adversarial relationships. A device accesses the adversarial relationship data and the schedule data. The device determines, based on the schedule data, spatiotemporal crosspaths at the school of the students having the adversarial relationships. The device determines a ranking of the spatiotemporal crosspaths. The device assigns one or more monitors to one or more of the spatiotemporal crosspaths. The device transmits notifications to communication devices associated with the one or more monitors, the notifications to instruct the one or more monitors to move to a respective assigned spatiotemporal crosspath.

An answer evaluation method that is executed by an answer evaluating system includes acquiring information of an answer that is an evaluation target corresponding to a question, and information of a comprehensive evaluation method, which is a method of determining a comprehensive evaluation of the answer based on an evaluation on the answer with respect to one evaluation item or each of two or more evaluation items for evaluating the answer represented by the information of the answer; and outputting information representing the comprehensive evaluation of the answer and acquired based on the answer represented by the acquired information of the answer and the comprehensive evaluation method represented by the acquired information of the comprehensive evaluation method.

A service use system includes circuitry to set, for each of a plurality of attribute types, a corresponding one of a plurality of connection destinations of a service. Each of the plurality of attribute types is to be obtained from a user terminal reading a touch point through which the service is to be provided. Each of the plurality of connection destinations is indicated by connection destination information with which the user terminal is to be connected to the corresponding one of the plurality of connection destinations. The circuitry generates touch point information to be obtained in response to the user terminal reading the touch point. The touch point information includes the connection destination information.

An educational system may include a contextualizer that accesses and processes content items and build a knowledge base including educational content and relationships between content items. The system may include a user assessor unit that determines user assessment variables based on user responses to previous educational content items and generate a user model based on the user assessment variables. The system may include a recommender that navigates the knowledge base and generates recommendations of new educational content based on the user model. The recommendation can assess the user's proficiency on a given concept based on the user model and maximize the user's probability of success around the concept of which the user shows proficiency, or improve the user's proficiency around the concept the user shows weakness. In some examples, the system may acquire an expert's knowledge about certain concepts to refine the user model, increasing the accuracy of the recommender.