Abstract: Systems and methods are provided by which a machine learning model may be executed to determine the probability that a given user will respond correctly to a given assessment item of a digital assessment on their first attempt. The machine learning model may process feature data corresponding to the user and the assessment item in order to determine the probability. The feature data may be calculated periodically and/or in real time or near-real time according to a machine learning model definition based on assessment data corresponding to the user's activity and/or based on responses submitted by all users to the assessment item and/or to content related to the assessment item.

Abstract: Systems and methods of the present invention provide for storing textbook data, a glossary, and problems within a database; identifying a problem's guided solution, and a keyword within the solution matching an entry within the glossary, from which a skill tag is associated. The disclosed system then automatically generates an assessment including an assessment problem associated with the skill. If an incorrect response is received for the assessment problem, the database is updated to associate a user that input the response with the assessment problem and a skill. The system then automatically generates a customized exercise assignment associated in the database with the skill.

Abstract: Systems and methods are provided by which an adaptive learning engine may be executed to determine the probability that a given user will respond correctly to a given assessment item of a digital assessment on their first attempt. The adaptive learning engine may apply one or more machine learning models to feature data corresponding to the user and the assessment item in order to determine the probability. The feature data may be calculated periodically and/or in real time or near-real time according to a machine learning model definition based on assessment data corresponding to the user's activity and/or based on responses submitted globally by users to the assessment item and/or to content related to the assessment item. Based on the correct first attempt probability, the adaptive learning engine may identify and recommend assessment items for which a user should be preemptively assigned credit.

Abstract: 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.

Abstract: Systems and methods of the present invention provide for storing textbook data, a glossary, and problems within a database; identifying a problem's guided solution, and a keyword within the solution matching an entry within the glossary, from which a skill tag is associated. The disclosed system then automatically generates an assessment including an assessment problem associated with the skill. If an incorrect response is received for the assessment problem, the database is updated to associate a user that input the response with the assessment problem and a skill. The system then automatically generates a customized exercise assignment associated in the database with the skill.

Abstract: Systems and methods for content provisioning are disclosed herein. The method includes receiving content corresponding to at least one source document, parsing the content, identifying segments from the parsed content, generating a networked grouping of the segments, receiving historical user information about a plurality of users, training a model by using the historical user information, receiving activities of a user, parsing the activities of the user, identifying components from the parsed activities, correlating the components with the segments, extracting features from the activities of the user based on the correlation, and using the trained model to estimate a mastery level of the user based on the features.

Abstract: Systems and methods are provided by which an adaptive learning engine may select a machine learning model service to determine a probability that a user will respond correctly to a given assessment item of a digital assessment on their first attempt. The adaptive learning engine may receive a request identifying the user, the assessment item, and request data. A model selector may generate a model reference corresponding to a model definition based on the request data. The feature data to be retrieved and/or calculated may be defined by the model definition. The feature data may be processed by a model service executing a machine learning model selected by the adaptive learning engine based on the model definition. Based on the probability output by the model, the adaptive learning engine may whether the user should be preemptively assigned credit for the assessment item.

Abstract: Systems and methods are provided by which an adaptive learning engine may be executed to determine the probability that a given user will respond correctly to a given assessment item of a digital assessment on their first attempt. The adaptive learning engine may apply one or more machine learning models to feature data corresponding to the user and the assessment item in order to determine the probability. The feature data may be calculated periodically and/or in real time or near-real time according to a machine learning model definition based on assessment data corresponding to the user's activity and/or based on responses submitted globally by users to the assessment item and/or to content related to the assessment item. Based on the correct first attempt probability, the adaptive learning engine may identify and recommend assessment items for which a user should be preemptively assigned credit.

Abstract: Systems and methods are provided by which a machine learning model may be executed to determine the probability that a given user will respond correctly to a given assessment item of a digital assessment on their first attempt. The machine learning model may process feature data corresponding to the user and the assessment item in order to determine the probability. The feature data may be calculated periodically and/or in real time or near-real time according to a machine learning model definition based on assessment data corresponding to the user's activity and/or based on responses submitted by all users to the assessment item and/or to content related to the assessment item.

Abstract: 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.

Abstract: Systems and methods for automated content delivery and evaluation are disclosed herein. The system can include a memory. The memory can include a content library database including a plurality of problems and data for stepwise evaluation of each of the plurality of problems. The system can include at least one server. The at least one server can automatically decompose a content item into a plurality of potential steps and associate attributes with the potential steps. The at least one server can receive a response from a user for the content item, identify steps in the received response, and select a next action based the identified steps of the received response.

Abstract: Systems and methods for automated content delivery and evaluation are disclosed herein. The system can include a memory. The memory can include a content library database including a plurality of problems and data for stepwise evaluation of each of the plurality of problems. The system can include at least one server. The at least one server can automatically decompose a content item into a plurality of potential steps and associate attributes with the potential steps. The at least one server can receive a response from a user for the content item, identify steps in the received response, and select a next action based the identified steps of the received response.

Abstract: Systems and methods for content provisioning are disclosed herein. The method includes receiving content corresponding to at least one source document, parsing the content, identifying segments from the parsed content, generating a networked grouping of the segments, receiving historical user information about a plurality of users, training a model by using the historical user information, receiving activities of a user, parsing the activities of the user, identifying components from the parsed activities, correlating the components with the segments, extracting features from the activities of the user based on the correlation, and using the trained model to estimate a mastery level of the user based on the features.