Abstract: Systems and methods for remote intervention are disclosed herein. The system can include memory including: a user profile database; a content database; and a model database. The system can include a remote device including: a network interface; and an I/O subsystem. The system can include a content management server that can: receive a first electrical signal from the remote device; generate and send an electrical signal to the remote device directing the launch of the content authoring interface; receive a second electrical signal including content received by the content authoring interface from the remote device; identify a plurality of response demands in the received content; determine a level of the received content based on the identified plurality of response demands; determine the acceptability of the received content based on the identified plurality of response demands; and generate and send an alert to the remote device.

Abstract: Systems and methods for remote intervention are disclosed herein. The system can include memory including: a user profile database; a content database; and a model database. The system can include a remote device including: a network interface; and an I/O subsystem. The system can include a content management server that can: receive a first electrical signal from the remote device; generate and send an electrical signal to the remote device directing the launch of the content authoring interface; receive a second electrical signal including content received by the content authoring interface from the remote device; identify a plurality of response demands in the received content; determine a level of the received content based on the identified plurality of response demands; determine the acceptability of the received content based on the identified plurality of response demands; and generate and send an alert to the remote device.

Abstract: Systems and methods for automated content generation and delivery are provided herein. The system can include a memory that can include a content item library. The content library can include a hierarchical data structure having levels and a plurality of data packets, each of which data packets is linked with at least a portion of the hierarchical data structure. The system can include at least one server that can generate an assessment creation interface including a plurality of nested objects each representative of a portion of the hierarchical data structure, receive a selection of a first object and a second object from the plurality of nested objects of the assessment creation interface, generate a weighting value for each of the selected objects, and generate an assessment from data packets associated with the selected objects according to the weighting value.

Abstract: Systems and methods for remote intervention are disclosed herein. The system can include memory including: a user profile database; a content database; and a model database. The system can include a supervisor device including: a network interface; and an I/O subsystem. The system can include a content management server that can: receive a first electrical signal from the supervisor device; generate and send an electrical signal to the supervisor device directing the launch of the content authoring interface; receive a second electrical signal including content received by the content authoring interface from the supervisor device; identify a plurality of response demands in the received content; determine a level of the received content based on the identified plurality of response demands; determine the acceptability of the received content based on the identified plurality of response demands; and generate and send an alert to the supervisor device.

Abstract: Systems and methods for automated content generation and delivery are provided herein. The system can include a memory that can include a content item library. The content library can include a hierarchical data structure having levels and a plurality of data packets, each of which data packets is linked with at least a portion of the hierarchical data structure. The system can include at least one server that can identify and deliver an item within a first content domain to a user device, evaluate a response to the delivered item, generate a scalar estimated skill level with a unidimensional evaluation engine, select and present a next item based on the estimated skill level, and upon completion of an assessment, generate a vector estimated skill level with a multidimensional evaluation engine.

Abstract: Systems and methods for automated content generation and delivery are provided herein. The system can include a memory that can include a content item library. The content library can include a hierarchical data structure having levels and a plurality of data packets, each of which data packets is linked with at least a portion of the hierarchical data structure. The system can include at least one server that can generate an assessment creation interface including a plurality of nested objects each representative of a portion of the hierarchical data structure, receive a selection of a first object and a second object from the plurality of nested objects of the assessment creation interface, generate a weighting value for each of the selected objects, and generate an assessment from data packets associated with the selected objects according to the weighting value.

Abstract: Systems and methods for automated node status determination are disclosed herein. The system can include a memory. The memory can include a Q-matrix that can include data identifying a relationship between at least a portion of a standard and at least one item of an assessment. The system can include at least one server. The at least one server can provide a first interim assessment including a first plurality of items. Each of the items can correspond to a child evidence node in a Bayesian network. The at least one server can generate first evidence by evaluating responses received to the first plurality of items from the first interim assessment and can calculate a mastery probability according to the Bayesian network of at least one parent node in the Bayesian network based on the generated first evidence.

Abstract: Systems and methods for automated node status determination are disclosed herein. The system can include a memory. The memory can include a Q-matrix that can include data identifying a relationship between at least a portion of a standard and at least one item of an assessment. The system can include at least one server. The at least one server can provide a first interim assessment including a first plurality of items. Each of the items can correspond to a child evidence node in a Bayesian network. The at least one server can generate first evidence by evaluating responses received to the first plurality of items from the first interim assessment and can calculate a mastery probability according to the Bayesian network of at least one parent node in the Bayesian network based on the generated first evidence.

Abstract: Systems and methods for automated node status determination are disclosed herein. The system can include a memory. The memory can include a Q-matrix that can include data identifying a relationship between at least a portion of a standard and at least one item of an assessment. The system can include at least one server. The at least one server can provide a first interim assessment including a first plurality of items. Each of the items can correspond to a child evidence node in a Bayesian network. The at least one server can generate first evidence by evaluating responses received to the first plurality of items from the first interim assessment and can calculate a mastery probability according to the Bayesian network of at least one parent node in the Bayesian network based on the generated first evidence.

Abstract: Systems and methods for automated node status determination are disclosed herein. The system can include a memory. The memory can include a Q-matrix that can include data identifying a relationship between at least a portion of a standard and at least one item of an assessment. The system can include at least one server. The at least one server can provide a first interim assessment including a first plurality of items. Each of the items can correspond to a child evidence node in a Bayesian network. The at least one server can generate first evidence by evaluating responses received to the first plurality of items from the first interim assessment and can calculate a mastery probability according to the Bayesian network of at least one parent node in the Bayesian network based on the generated first evidence.

Abstract: Systems and methods for remote intervention are disclosed herein. The system can include memory including: a user profile database; a content database; and a model database. The system can include a supervisor device including: a network interface; and an I/O subsystem. The system can include a content management server that can: receive a first electrical signal from the supervisor device; generate and send an electrical signal to the supervisor device directing the launch of the content authoring interface; receive a second electrical signal including content received by the content authoring interface from the supervisor device; identify a plurality of response demands in the received content; determine a level of the received content based on the identified plurality of response demands; determine the acceptability of the received content based on the identified plurality of response demands; and generate and send an alert to the supervisor device.