Abstract: Systems, methods, and non-transitory, machine-readable media to facilitate adaptive processing and content control are disclosed. Content composites may be created and configured according to a computational model that may include a hierarchical ordering of the content composites using a hierarchical data structure. The configured content composites may be presented with a graphical user interface of an endpoint device. Metrics of interactions with interface elements corresponding to the configured content composites may be determined using a processing device that monitors inputs. The computational model may be automatically trained using the metrics of interactions to create an adapted computational model. Adapted content composites may be created and configured according to the adapted computational model that may include a second hierarchical ordering using a second hierarchical data structure. The adapted content composites may be presented with the graphical user interface.

Abstract: Content composites may be collected in a data storage and may include audio and/or visual content. Content composites may be created and configured according to a computational model that comprises a hierarchical ordering of the content composites using a tree data structure and may be presented with a graphical user interface. Metrics of user interactions with the configured content composites may be determined using a processing device that monitors user inputs. The computational model may be automatically trained using the metrics to create an adapted computational model. Adapted content composites may be created and configured according to the adapted computational model that comprises of a second hierarchical ordering of the adapted content composites using a second tree data structure and specifications of different content composites. The adapted content composites may be presented with the graphical user interface of the user device and a second graphical user interface.

Abstract: A system and method for providing customized content recommendations to a user based on extrapolated data is described. The system may receive an answer to a question from a user and determine a second unanswered question having a threshold relationship level with the question. The system may compute a predicted probability that the user would answer the second question correctly, calculate a first comparative skill level of the user among a cohort of similar users, and rank the first comparative skill level of the user against a second comparative skill level of the cohort to determine a skill gap of the user. Further, in some implementations, the system may generate a search query based on the skill gap of the user, and determine recommended content customized to the skill gap of the user.

Abstract: Systems, methods, and non-transitory, machine-readable media to facilitate adaptive processing and content control are disclosed. Content composites may be created and configured according to a computational model that may include a hierarchical ordering of the content composites using a hierarchical data structure. The configured content composites may be presented with a graphical user interface of an endpoint device. Metrics of interactions with interface elements corresponding to the configured content composites may be determined using a processing device that monitors inputs. The computational model may be automatically trained using the metrics of interactions to create an adapted computational model. Adapted content composites may be created and configured according to the adapted computational model that may include a second hierarchical ordering using a second hierarchical data structure. The adapted content composites may be presented with the graphical user interface.

Abstract: Content composites may be collected in a data storage and may include audio and/or visual content. Content composites may be created and configured according to a computational model that comprises a hierarchical ordering of the content composites using a tree data structure and may be presented with a graphical user interface. Metrics of user interactions with the configured content composites may be determined using a processing device that monitors user inputs. The computational model may be automatically trained using the metrics to create an adapted computational model. Adapted content composites may be created and configured according to the adapted computational model that comprises of a second hierarchical ordering of the adapted content composites using a second tree data structure and specifications of different content composites. The adapted content composites may be presented with the graphical user interface of the user device and a second graphical user interface.