Understanding test takers' choices in a self-adapted test: A hidden Markov Modeling of process data

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Abstract

With the rise of more interactive assessments, such as simulation- and game-based assessment, process data are available to learn about students' cognitive processes as well as motivational aspects. Since process data can be complicated due to interdependencies in time, our traditional psychometric models may not necessarily fit, and we need to look for additional ways to analyze such data. In this study, we draw process data from a study on self-adapted test under different goal conditions (Arieli-Attali, 2016) and use hidden Markov models to learn about test takers' choice making behavior. Self-adapted test is designed to allow test takers to choose the level of difficulty of the items they receive. The data includes test results from two conditions of goal orientation (performance goal and learning goal), as well as confidence ratings on each question. We show that using HMM we can learn about transition probabilities from one state to another as dependent on the goal orientation, the accumulated score and accumulated confidence, and the interactions therein. The implications of such insights are discussed.

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Arieli-Attali, M., Ou, L., & Simmering, V. R. (2019). Understanding test takers’ choices in a self-adapted test: A hidden Markov Modeling of process data. Frontiers in Psychology, 10(FEB). https://doi.org/10.3389/fpsyg.2019.00083

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