Cognitive Modeling of Learning Using Big Data from a Science-Based Game Development Environment

The purpose of this study was to identify the underlying cognitive attributes used during the design and development of science-based serious educational games. Study methods rely on a modification of cognitive diagnostics, item response theory, and Bayesian estimation with traditional statistical t...

Full description

Saved in:
Bibliographic Details
Published inInternational journal of game-based learning Vol. 10; no. 4; pp. 22 - 39
Main Authors Annetta, Leonard, Lamb, Richard, Bressler, Denise M, Vallett, David B
Format Journal Article
LanguageEnglish
Published IGI Global 01.10.2020
Subjects
21st Century Skills
Artificial Intelligence
Cognitive Measurement
Cognitive Processes
Data Use
Design
Educational Games
Game Based Learning
High School Students
Science Education
Online AccessGet more information

Cover

More Information
Summary:The purpose of this study was to identify the underlying cognitive attributes used during the design and development of science-based serious educational games. Study methods rely on a modification of cognitive diagnostics, item response theory, and Bayesian estimation with traditional statistical techniques such as factor analysis and model fit analysis to examine the data and model structure. A computational model of the cognitive processing using an artificial neural network (ANN) allowed for examination of underlying mechanisms of cognition from a server-side data set and a 21st century skills assessment. ANN results indicate that the model correctly predicts successful completion of science-based serious educational game (SEG) design tasks related to 21st century skills 86% of the time and correctly predicts failure to complete SEG design tasks related to 21st century skills 78% of the time. The model also reveals the relative importance of each particular cognitive attribute within the 21st century skills framework.
ISSN:2155-6849
DOI:10.4018/IJGBL.2020100102