Community Models to Enhance Adaptive Instruction

Abstract

This paper discusses the need and methods to develop community-based persona (learner models) to tie together key learner attributes and learning outcomes (e.g., knowledge acquisition) with the goal of facilitating the validation of adaptive instructional strategies and tactics. Adaptive instruction, sometimes referred to as differentiated instruction, is a learning experience tailored to the needs and preferences of each individual learner or team in which strategies (recommendations and plans for action) and tactics (actions by the tutor) are selected with the aim of optimizing learning, performance, retention, and the transfer of skills between the instructional environment (usually provided by an Intelligent Tutoring System or ITS) and the work or operational environment where the skills learned will be applied. Adaptive instructional systems (AISs) use human variability and other learner attributes along with instructional conditions to select appropriate strategies and tactics. This is usually accomplished through the use of machine learning techniques, but large amounts of data are needed to reinforce the learning of these algorithms over time. We propose a method to develop community models more quickly by enabling diverse groups to contribute the results of their experiments and training data in a common instructional domain to a cloud-based model that could be shared by various instructional applications.