Open AccessStatistical Methods for Assessments in Simulations and Serious Games
Author(s) -
Fu Jianbin,
Zapata Diego,
Mavronikolas Elia
Publication year - 2014
Publication title -
ets research report series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.235
H-Index - 5
ISSN - 2330-8516
DOI - 10.1002/ets2.12011
Subject(s) - computer science , outcome (game theory) , process (computing) , bayesian probability , scale (ratio) , data mining , machine learning , artificial intelligence , data science , mathematics , physics , mathematical economics , quantum mechanics , operating system
Simulation or game‐based assessments produce outcome data and process data. In this article, some statistical models that can potentially be used to analyze data from simulation or game‐based assessments are introduced. Specifically, cognitive diagnostic models that can be used to estimate latent skills from outcome data so as to scale these assessments are presented under the framework of Bayesian networks; 5 prospective data mining methods that can be employed to discover problem‐solving strategies from process data are described. Some studies in the literature that apply some of these methods to analyze simulation or game‐based assessments are presented as application examples. Recommendations are provided for selecting appropriate scaling and data mining methods for these assessments; future directions of research are proposed.