System design and architecture of an online, adaptive, and personalized learning platform

Abstract

The author proposes that personalized learning can be brought to traditional and nontraditional learners through a new type of asynchronous learning platform called Guided Learning Pathways (GLP). The GLP platform allows learners to intelligently traverse a vast field of learning resources, emphasizing content only of direct relevance to the learner and presenting it in a way that matches the learner's pedagogical preference and contextual interests. GLP allows learners to advance towards individual learning goals at their own pace, with learning materials catered to each learner's interests and motivations. Learning communities would support learners moving through similar topics. This thesis describes the software system design and architecture required to support Guided Learning Pathways. The author provides detailed information on eight software applications within GLP, including specific learning benefits and features of each. These applications include content maps, learning nuggets, and nugget recommendation algorithms. A learner scenario helps readers visualize the functionality of the platform. To describe the platform's software architecture, the author provides conceptual data models, process flow models, and service group definitions. This thesis also provides a discussion on the potential social impact of GLP in two areas: higher education institutions and the broader economy.