Abstract: Embodiments measure knowledge levels of students with respect to knowledge entities as they proceed through a course datagraph macrostructure, and dynamically adapt aspects of the macrostructure (and/or its embedded microstructures) to optimize knowledge acquisition of the students in accordance with their knowledge level. For example, a course consumption platform can parse the macrostructure and embedded microstructures to identify next microstructures to present to the student in such a way that dynamically adapts knowledge entities of the course to a student as a function of the student's present knowledge level associated with the student and difficulty levels of the presented microstructures.

Abstract: Embodiments of the invention provide dynamically adaptive e-learning courses built on integrated datagraph macrostructures and microstructures. For example, an e-learning course includes a number of knowledge entities, each having one or more lesson and practice components. The knowledge entities can be instantiated as nodes of a course datagraph macrostructure, linked together via edges having edge attributes that can define a course flow relationship among the knowledge entities. Each node in the datagraph macrostructure can include its own set of datagraph microstructures. For example, each knowledge entity can include lesson and/or practice datagraph microstructures that include numbers of respective step objects (e.g., lesson step objects and/or practice step objects), each associated with defined responses, and each linked (e.g.

Abstract: Embodiments relate to authoring, consuming, and exploiting dynamically adaptive e-learning courses created using novel, embedded datagraph structures, including course macrostructures with embedded lesson microstructures and practice microstructures. For example, courses can be defined by nodes and edges of directed graph macrostructures, in which each node includes one or more directed graph microstructures for defining lesson and practice step objects of the courses. The content and attributes of the nodes and edges can adaptively manifest higher level course flow relationships and lower level lesson and practice flow relationships.

Abstract: Embodiments relate to authoring, consuming, and exploiting dynamically adaptive e-learning courses created using novel, embedded datagraph structures, including course macrostructures with embedded lesson microstructures and practice microstructures. For example, courses can be defined by nodes and edges of directed graph macrostructures, in which each node includes one or more directed graph microstructures for defining lesson and practice step objects of the courses. The content and attributes of the nodes and edges can adaptively manifest higher level course flow relationships and lower level lesson and practice flow relationships.

Abstract: Embodiments relate to authoring, consuming, and exploiting dynamically adaptive e-learning courses created using novel, embedded datagraph structures, including course macrostructures with embedded lesson microstructures and practice microstructures. For example, courses can be defined by nodes and edges of directed graph macrostructures, in which each node includes one or more directed graph microstructures for defining lesson and practice step objects of the courses. The content and attributes of the nodes and edges can adaptively manifest higher level course flow relationships and lower level lesson and practice flow relationships.

Abstract: Embodiments measure knowledge levels of students with respect to knowledge entities as they proceed through a course datagraph macrostructure, and dynamically adapt aspects of the macrostructure (and/or its embedded microstructures) to optimize knowledge acquisition of the students in accordance with their knowledge level. For example, a course consumption platform can parse the macrostructure and embedded microstructures to identify next microstructures to present to the student in such a way that dynamically adapts knowledge entities of the course to a student as a function of the student's present knowledge level associated with the student and difficulty levels of the presented microstructures.

Abstract: Embodiments relate to authoring, consuming, and exploiting dynamically adaptive e-learning courses created using novel, embedded datagraph structures, including course macrostructures with embedded lesson microstructures and practice microstructures. For example, courses can be defined by nodes and edges of directed graph macrostructures, in which each node includes one or more directed graph microstructures for defining lesson and practice step objects of the courses. The content and attributes of the nodes and edges can adaptively manifest higher level course flow relationships and lower level lesson and practice flow relationships.