Hidden Markov models and learning in authentic situations

Abstract

This paper introduces Hidden Markov Models for the analysis of authentic learning data from an applied field. For illustrative purposes, it shows how classical 2-state all-or-none models can be extended to adequately fit the competence development process of nursery apprentices in a clinical context. It also presents some of the main underlying ideas, such as model specifications, parameters estimation, model selection, the Viterbi algorithm, and goodness-of-fit issues.