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. Markov models have been used in psychology since the mid-fifties (Miller, 1952; Steiner & Greeno, 1969) to infer cognitive states from sequences of data in learning experiments. They are now considered very general tools for integrating large sets of longitudinal observations (Langeheine, Stern, & van de Pol, 1994), from implicit learning (Visser, Raijmakers, & van der Maas, 2009) to well-being (Eid, & Langeheine, 2007). They have also been used in the classroom context to study negotiations betwee