A concentration-level estimation device includes an obtainer which obtains task information indicating a type of a task executed by a user among types of tasks; a sensor which outputs motion information indicating a feature of a motion of the user who executes the task, based on a result of sensing obtained from a sensing device; a storage which stores profiles concerning habits of the user for the types of tasks; and a calculator which calculates a concentration level of the user using the motion information and a profile corresponding to the type of the task indicated in the task information among the profiles stored in the storage.

A system for administering, evaluating, and monitoring a subject’s compliance with task performance requirements within an action programme, or for detection of noncompliance including substance abuse, driving under influence, and untruthful testimony giving under law enforcement setting, comprising optical sensors for capturing subject’s pupillary responses, blinking eye movements, eye movements, point-of-gaze, head pose, and facial expression of a subject. The system can also be applied in neurocognitive disorder diagnosis. The subject’s affective and cognitive states estimation based on the captured sensory data during a diagnosis test is feedback to the system to drive the course of the compliance or cognitive test, adaptively change the test materials, and influence the subject’s affective and cognitive states. The estimated affective and cognitive states in turn provide a more accurate reading of the subject’s condition.

The present disclosure relates a method, device, electronic apparatus, and system of providing information for breathing training, and storage medium. The method of providing information for breathing training includes receiving first data input on a first display interface, the first data including breathing training time data; and presenting output data on a second display interface, the output data including breathing beat image prompting an exhaling action and an inhaling action by using alternate graphics.

A method for improving learning comprehension regarding a lesson includes modifying a fundamental illustrative model to illustrate a first set of learning assets of a first learning object using an illustration approach to produce a first learning illustrative model. The lesson includes learning objects which includes the first learning object. The fundamental illustrative model is based on illustrative assets of the lesson. The method further includes obtaining a first comprehension evaluation for the first learning illustrative model and modifying the illustration approach based on the first comprehension evaluation for the first learning illustrative model to produce an updated illustration approach. The method further includes modifying the fundamental illustrative model to illustrate a second set of learning assets of a second learning object using the updated illustration approach to produce a second learning illustrative model.

A method for improving learning comprehension regarding a lesson includes modifying a fundamental illustrative model to illustrate a first set of learning assets of a first learning object using an illustration approach to produce a first learning illustrative model. The lesson includes learning objects which includes the first learning object. The fundamental illustrative model is based on illustrative assets of the lesson. The method further includes obtaining a first comprehension evaluation for the first learning illustrative model and modifying the illustration approach based on the first comprehension evaluation for the first learning illustrative model to produce an updated illustration approach. The method further includes modifying the fundamental illustrative model to illustrate a second set of learning assets of a second learning object using the updated illustration approach to produce a second learning illustrative model.

Systems and methods are disclosed herein for recommending an educational course to a user, and may comprise receiving data records associated with availability of a plurality of educational courses at one or more institutions; receiving educational course data and educational course focus data associated with the user; receiving prior user data records comprising prior user educational course data and prior user educational course focus data; determining index scores for each of the plurality of educational courses based upon a similarity between the educational course data and prior user educational course data, and based upon a similarity between the educational course focus data and prior user educational course focus data; and providing a recommended educational course from the plurality of educational courses to the user based upon the determined index scores.

Systems and methods are disclosed herein for recommending an educational course to a user, and may comprise receiving data records associated with availability of a plurality of educational courses at one or more institutions; receiving educational course data and educational course focus data associated with the user; receiving prior user data records comprising prior user educational course data and prior user educational course focus data; determining index scores for each of the plurality of educational courses based upon a similarity between the educational course data and prior user educational course data, and based upon a similarity between the educational course focus data and prior user educational course focus data; and providing a recommended educational course from the plurality of educational courses to the user based upon the determined index scores.

Systems and methods for conducting automated skills mastery assessments in an e-learning environment may include assessing learner engagements with learning resources to produce mastery assessments, using historic interaction data derived through the engagements to train machine learning algorithm(s) to forecast evaluation outcomes of engagements based on engagement patterns indicative of skill fading, imparting learning, initial level of mastery, and/or a difficulty of acquiring mastery, applying the learning algorithm(s) to historic user interactions with learning resources to produce predicted evaluation outcomes, and, based on any differences between predicted outcomes and actual outcomes, refining parameter(s) of a mastery assessment parameter set used in calculating the mastery assessments, where a portion of the parameters correspond to attribute(s) of connections between the learning resources and skills of a skill hierarchy. The connections may be represented by logical indicators of relationships defined between the learning resources and the skills.

Systems and methods for processing and analyzing data collected in connection with online examinations are described herein. The methods may be implemented by one or more computing devices and may include flagging one or more proctoring events indicated by data that was obtained in connection with an exam session of a test taker, the one or more flagged proctoring events being potentially associated with one or more exam rule violations. A point value may be assigned to each of the one or more flagged proctoring events. The point value of at least one of the one or more flagged proctoring events may be adjusted with a weight, the weight being obtained based on data other than mere occurrence of the one or more flagged proctoring events. An overall risk level may be generated based on an overall score obtained by tallying together the adjusted point value or values and unadjusted point value or values, if there are any, of all of the one or more flagged proctoring events, the overall risk level indicating likelihood of exam rule violation in connection with the exam session.

Systems and methods for processing and analyzing data collected in connection with online examinations are described herein. The methods may be implemented by one or more computing devices and may include flagging one or more proctoring events indicated by data that was obtained in connection with an exam session of a test taker, the one or more flagged proctoring events being potentially associated with one or more exam rule violations. A point value may be assigned to each of the one or more flagged proctoring events. The point value of at least one of the one or more flagged proctoring events may be adjusted with a weight, the weight being obtained based on data other than mere occurrence of the one or more flagged proctoring events. An overall risk level may be generated based on an overall score obtained by tallying together the adjusted point value or values and unadjusted point value or values, if there are any, of all of the one or more flagged proctoring events, the overall risk level indicating likelihood of exam rule violation in connection with the exam session.