An analytics platform may generate one or more user interfaces based on the set of testing parameters. The analytics platform may provide the one or more user interfaces. The analytics platform may receive first test information corresponding to first respondents. The analytics platform may identify, based on the first test information and based on information identifying particular respondents of the first respondents, characteristic attributes of the particular respondents. The analytics platform may train, based on the values of the characteristic attributes and performance information associated with the first respondents, a predictive model. The analytics platform may obtain the second test information corresponding to the one or more second respondents. The analytics platform may determine the predicted performance information associated with the one or more second respondents using the predictive model. The analytics platform may perform an action based on the predicted performance information.

An analytics platform may generate one or more user interfaces based on the set of testing parameters. The analytics platform may provide the one or more user interfaces. The analytics platform may receive first test information corresponding to first respondents. The analytics platform may identify, based on the first test information and based on information identifying particular respondents of the first respondents, characteristic attributes of the particular respondents. The analytics platform may train, based on the values of the characteristic attributes and performance information associated with the first respondents, a predictive model. The analytics platform may obtain the second test information corresponding to the one or more second respondents. The analytics platform may determine the predicted performance information associated with the one or more second respondents using the predictive model. The analytics platform may perform an action based on the predicted performance information.

In a system for monitoring and assessing subject response to programmed physical training, a client application provides an intake form, into which subject information, such as contact information, demographics and history may be entered. At least part of the subject information may be stored in a central repository for aggregation with other subject data, for analysis and reporting. Parameterized descriptions of exercises produce a binary string for each exercise, the resulting in a binary map of an entire exercise system, such as the PILATES system. Using a digital session planner, a practitioner selects from filtered lists of exercises to generate a customized exercise sequence for a subject. After the training session, the binary strings for the session are aggregated and a summary of the session displayed for user and/or practitioner. Session data may be uploaded to the repository for aggregation with data from other subjects/session for analysis and reporting.

A characteristic evaluation apparatus (1) cumulatively stores, in a measurement history DB (13), measurement information (measurement date and time, measurement value and the like) that is obtained by measuring, by a weight scale (3) and the like, biological information or behavior information of a subject person. An arithmetic processing unit (11) evaluates psychological characteristics of the subject person with respect to a biological change or a behavioral change as a goal for the subject person based on a history of the measurement information of the subject person that is stored in the measurement history DB (13). A communication unit (14) outputs evaluation results evaluated by the arithmetic processing unit (11) with respect to the psychological characteristics of the subject person.

A characteristic evaluation apparatus (1) cumulatively stores, in a measurement history DB (13), measurement information (measurement date and time, measurement value and the like) that is obtained by measuring, by a weight scale (3) and the like, biological information or behavior information of a subject person. An arithmetic processing unit (11) evaluates psychological characteristics of the subject person with respect to a biological change or a behavioral change as a goal for the subject person based on a history of the measurement information of the subject person that is stored in the measurement history DB (13). A communication unit (14) outputs evaluation results evaluated by the arithmetic processing unit (11) with respect to the psychological characteristics of the subject person.

A system and a method for generating stress level information for an individual and stress level resilience information for an individual, which includes a stress information processing module configured to process stress information for the individual, the stress information for the individual comprising at least two of psychometric information for the individual, physiological information for the individual, behavioural information for the individual, and cognitive function information for the individual.

A system and a method for generating stress level information for an individual and stress level resilience information for an individual, which includes a stress information processing module configured to process stress information for the individual, the stress information for the individual comprising at least two of psychometric information for the individual, physiological information for the individual, behavioural information for the individual, and cognitive function information for the individual.

Systems and methods for automated and direct network positioning are disclosed herein. The system can include memory that can include a content library database and a structure sub-database. The system can include at least one server. The at least one server can: receive a data packet previously unassociated with the hierarchy in the structure sub-database; identify one of the plurality of positions within the hierarchy for the data packet; receive user information; present a series of assessment data packets to the user; receive a response from the user subsequent to presentation of each of the assessment data packets; evaluate the received responses; adjust a location of the user within the hierarchy based on the evaluating of the received responses; and present a content data packet to the user.

Systems and methods for automated and direct network positioning are disclosed herein. The system can include memory that can include a content library database and a structure sub-database. The system can include at least one server. The at least one server can: receive a data packet previously unassociated with the hierarchy in the structure sub-database; identify one of the plurality of positions within the hierarchy for the data packet; receive user information; present a series of assessment data packets to the user; receive a response from the user subsequent to presentation of each of the assessment data packets; evaluate the received responses; adjust a location of the user within the hierarchy based on the evaluating of the received responses; and present a content data packet to the user.

Disclosed herein are system and method for generative assessment item development, encoding & analysis, wherein the generative assessment item includes a question and a plurality of possible responses to the question, the plurality of possible responses comprising at least two responses that are not independent, and wherein the plurality of responses are non-dichotomous.