Hidden Markov models and learning in authentic situations
Léon Harvey · Tutorials in Quantitative Methods for Psychology · 2011
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 allor-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 wellbeing (Eid, & Langeheine, 2007).They have also been used in the classroom context to study negotiations between actors (Weingart, Prietula, Hyder, & Genovese, 1999), peer scaffolding (Pata, Lehtinen, & Sarapuu, 2006) emerging from interactions between students in a synchronous network environment, and to compare counselling methods used by effective and ineffective students (Duys and Headrick, 2004).Some advanced models are also developed to account for sequential decision processes (Fu & Anderson, 2006;Littman, 2010;Niv, 2009).In this paper, the process of elaborating a Hidden Markov Model (HMM) is presented for tutorial purposes.It reaffirms some of the main ideas underlying anterior tutorial works (Visser, Raijmakers, & Molenaar, 2002;Wickens, 1982) and proposes, for illustrative purposes, to extend the classical 2-state model to observations from a clinical field.Modeling such data from an applied field is an important contribution.It suggests that HMMs are of great practical value when synthesizing competence development processes in authentic learning situations.More specifically, some HMMs will be built to illustrate how the interactions between a nursery supervisor and her apprentices can be analyzed to grasp the hidden process of competence growth of apprentices in a clinical context.Meanwhile, the tutorial discusses some important issues, such as model specifications, parameter estimation, model selection and goodness-of-fit issues.