An intelligent adaptive recommendation method based on an MCM model. The method includes acquiring historical data of errors on knowledge points of all students, acquiring error-cause labels of current student, calculating error-cause priority value P(E) for each error-cause label of current student, and extracting, according to at least one of MCM labels corresponding to each error-cause label, MCM learning resources corresponding to at least one of MCM labels from a preset content management system, sorting error-cause labels according to descending order of error-cause priority value P(E), extracting part or all of MCM learning resources from MCM learning resources corresponding to at least one error-cause label according to sorting result and pushing part or all of MCM learning resources to current student, and when current student finishes learning MCM learning resources corresponding to each MCM label, pushing errors on knowledge points corresponding to each MCM label to current student.

An intelligent adaptive recommendation method based on an MCM model. The method includes acquiring historical data of errors on knowledge points of all students, acquiring error-cause labels of current student, calculating error-cause priority value P(E) for each error-cause label of current student, and extracting, according to at least one of MCM labels corresponding to each error-cause label, MCM learning resources corresponding to at least one of MCM labels from a preset content management system, sorting error-cause labels according to descending order of error-cause priority value P(E), extracting part or all of MCM learning resources from MCM learning resources corresponding to at least one error-cause label according to sorting result and pushing part or all of MCM learning resources to current student, and when current student finishes learning MCM learning resources corresponding to each MCM label, pushing errors on knowledge points corresponding to each MCM label to current student.

A context-aware adaptive data processing application is described. One or more computing servers establish connections with multiple user terminals to provide an application to the user terminals. The provided application is executable at the user terminals via web browser or a dedication application installed at a user terminal, for example. The provided application is context-aware to dynamically adapt to a user's changing circumstances.

A context-aware adaptive data processing application is described. One or more computing servers establish connections with multiple user terminals to provide an application to the user terminals. The provided application is executable at the user terminals via web browser or a dedication application installed at a user terminal, for example. The provided application is context-aware to dynamically adapt to a user's changing circumstances.

A system and method for implementing interactive media content is provided. Interactive media content is received for communication to a user through at least wireless earpieces. User biometrics are measured utilizing the wireless earpieces. A user condition associated with the user biometrics is determined. Branching patterns of the interactive media content are modified in response to the user condition. The interactive content may be a game or story.

A method and system comprising an adaptive learning platform having a backend component for managing 3D lessons for display on an interface of a user device. A 3D lesson comprises a plurality of scenarios with choices which lead to a next scenario of the 3D lesson. A learner after completing the 3D lesson can analyze results of a completed 3D lesson using an analyzer unit of the adaptive learning platform. In addition, the platform also recommends other 3D lessons based on analytics performed on the learner user's results.

A method and system comprising an adaptive learning platform having a backend component for managing 3D lessons for display on an interface of a user device. A 3D lesson comprises a plurality of scenarios with choices which lead to a next scenario of the 3D lesson. A learner after completing the 3D lesson can analyze results of a completed 3D lesson using an analyzer unit of the adaptive learning platform. In addition, the platform also recommends other 3D lessons based on analytics performed on the learner user's results.

An interface for an educational tool on an electronic device is described. The interface comprises a main menu to display at least an icon for the educational tool. The main menu appears on a display screen of the electronic device. The interface also comprises a summary menu to list a subset of at least one function of the educational tool. The summary menu is accessed directly from the main menu when a user selects the icon for the educational tool. The interface further comprises an exhibitory window to display a rhyming riddle function of the educational tool selected by the user from the subset listed on the summary menu. The rhyming riddle function presents the user with a rhyming riddle to be solved.

A data collection, display, and report generation platform has a first input interface configured to present a learning module comprising a series of questions and answers on a plurality of successive question/answer screens comprising a plurality of radio buttons, at least one of the radio buttons configured to accept both a first input action and a subsequent second input action, each providing a different visual indication. The first and second input action each indicate a different confidence level of a learner's answer. A display dashboard displays a plurality of data visualizations of metrics of misinformation and struggle of plurality of learners based on a plurality of answers collected through the first input interface, and comprises one or more bar graph displays, one or more heat map displays, and one or more sorting tools configured to alter the one or more bar graph displays or one or more heat map displays.

A data collection, display, and report generation platform has a first input interface configured to present a learning module comprising a series of questions and answers on a plurality of successive question/answer screens comprising a plurality of radio buttons, at least one of the radio buttons configured to accept both a first input action and a subsequent second input action, each providing a different visual indication. The first and second input action each indicate a different confidence level of a learner's answer. A display dashboard displays a plurality of data visualizations of metrics of misinformation and struggle of plurality of learners based on a plurality of answers collected through the first input interface, and comprises one or more bar graph displays, one or more heat map displays, and one or more sorting tools configured to alter the one or more bar graph displays or one or more heat map displays.