Content development and moderation flow for e-learning datagraph structures

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. Embodiments can exploit such embedded datagraph structures to facilitate dynamic course creation and increased course adaptability; improved measurement of student knowledge acquisition and retention, and of student and teacher performance; enhanced monitoring and responsiveness to student feedback; and access to, exploitation of, measurement of, and / or valuation of respective contributions; etc.

Description

BACKGROUND[0001]Embodiments relate generally to e-learning systems, and, more particularly, to computer-implemented creation and delivery of adaptive, interactive e-learning courses.[0002]For many years, traditional classrooms have included course materials; teachers to interpret, adapt, and deliver the course materials; and students to learn from the teachers and the course materials. The effective transfer and retention of knowledge in such environments can be improved through increased student engagement and interaction with teachers and course materials, and through increased adaptation by the teachers to the needs and learning styles of the students. However, many pedagogical efforts are frustrated by limitations of traditional classroom environments. For example, it may be difficult or impossible to physically locate students in classrooms with skilled teachers; it may be difficult or impossible for a single teacher to concurrently engage with and adapt to multiple students, p...

AI Technical Summary

Benefits of technology

The patent describes a system and method for creating and using dynamic e-learning courses using new, embedded copy structures. These structures allow for more flexible and adaptable courses that can adjust to the needs of individual students and teachers. The system can also measure and evaluate the effectiveness of the courses, providing valuable feedback on how to improve learning outcomes. Overall, the patent aims to improve the personalization and impact of e-learning courses.

Problems solved by technology

However, many pedagogical efforts are frustrated by limitations of traditional classroom environments.

For example, it may be difficult or impossible to physically locate students in classrooms with skilled teachers; it may be difficult or impossible for a single teacher to concurrently engage with and adapt to multiple students, particularly when those students have different backgrounds, levels of knowledge, learning styles, etc.

; it may be difficult to accurately, or even adequately, measure student knowledge acquisition and retention, or for teachers to adapt their teaching to implicit or explicit student feedback; it may be difficult to dynamically adapt course materials in context of static course materials (e.g., printed textbooks); it may be difficult to measure and respond to teacher or student performance across large (e.g., geographically distributed) populations; it may be difficult to measure or value respective contributions to learning by multiple teachers; etc.

A few e-learning systems have recently begun to provide limited types of adaptation.

Even with the added capabilities facilitated by computers and the Internet, many of the limitations of traditional classrooms and pedagogical approaches frustrate the efficacy of e-learning systems.

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