A Novel Framework for Discovering Cognitive Models of Learning

Abstract

A cognitive model is a descriptive account or computational representation of human thinking about a given concept, skill, or domain. A cognitive model of learning, includes both a way of organizing knowledge within a subject area and an account of how humans develop accurate and complete knowledge of that subject area. Learning designers engage in a variety of practices to unpack knowledge from subject matter experts and novices to develop cognitive models of learning and use those models to guide the design of instruction or instructional technologies. Traditional approaches to eliciting and organizing knowledge, such as conducting a cognitive task analysis (CTA) [10] with experts and novices, are labor-intensive and require specific expertise that many learning designers do not have. However, learning data generated from learners' interaction with the courses, reveal how humans think about and develop knowledge. We propose a novel framework that uses learning data to discover and refine cognitive models of learning. The framework includes a Variational Autoencoder (VAE) module and a Gaussian Mixture Model (GMM) module. We provide one case study in a corporate setting to demonstrate the effectiveness of the proposed framework compared to other approaches.