Hidden Markov models and learning in authentic situations
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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.