On Alternative Measures for Dynamic Large-Scale Online Learning

Abstract

The purpose of this thesis project is to propose a theoretical construct to configure forward- looking alternative measures for dynamic large-scale online learning. This unifying construct could function as a platform and pedagogy agnostic learning object situated architecture system, which measures multidirectional and multidimensional learning interactivity between the learner, the course content, and larger sociocultural system dynamics of multiple inputs and outputs. Current centralized asynchronous time-based instructional metrics quantified by credit hours of educational attainment and targeted performance outcomes demonstrate transparency and flexibility constraints, which are characteristic of closed systems. Whereas, the proposed mutually reinforcing multi-inputs and outputs for impact (MIOI) capture mechanism and its learning object feature could improve transparency and flexibility efficiencies in large-scale online learning settings. Theoretically, the proposed construct could reconfigure inefficient antecedent bundles of time-based instruction into measurable decentralized synchronous large-scale learning configurations. This open system approach of dynamic multiple inputs and outputs can be further optimized by video and MOOC ecosystem technologies. As such, the proposed MIOI capture mechanism construct could sequence the multiplicity of configurative unit operations into actionable online learning at many scales of efficiency to redefine what it means to be educated in the Digital Age. Therefore, the stated theoretical construct to establish alternative measures for dynamic large-scale online learning has been proposed to advance future large-scale digital learning processes and efficiencies.