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Technology, Knowledge and Learning
Erschienen in: Technology, Knowledge and Learning 2/2020

26.08.2019 | Original research

Predicting Learning in a Multi-component Serious Game

verfasst von: Carol M. Forsyth, Arthur Graesser, Keith Millis

Erschienen in: Technology, Knowledge and Learning | Ausgabe 2/2020

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Abstract

The current study investigated predictors of shallow versus deep learning within a serious game known as Operation ARA. This game uses a myriad of pedagogical features including multiple-choice tests, adaptive natural language tutorial conversations, case-based reasoning, and an E-text to engage students. The game teaches 11 topics in research methodology across three distinct modules that target factual information, application of reasoning to specific cases, and question generation. The goal of this investigation is to discover predictors of deep and shallow learning by blending Evidence-Centered Design (ECD) with educational data mining. In line with ECD, time-honored cognitive processes or behaviors of time-on-task, discrimination, generation, and scaffolding were selected because there is a large research history supporting their importance to learning. The study included 192 college students who participated in a pretest-interaction-posttest design. These data were used to discover the best predictors of learning across the training experiences. Results revealed distinctly different patterns of predictors of deep versus shallow learning for students across the training environments of the game. Specifically, more interactivity is important for environments contributing to shallow learning whereas generation and discrimination is more important in training environments supporting deeper learning. However, in some training environments the positive impact of generation may be at the price of decreased discrimination.
Vorheriger Artikel Fueling Prediction of Player Decisions: Foundations of Feature Engineering for Optimized Behavior Modeling in Serious Games
Nächster Artikel Scaffolding Content and Context: A Revision of Gregory Bateson’s Learning Theory Through a Micro-level Analysis of How Learning Takes Place in the Computer Game StarCraft 2

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Metadaten
Titel
Predicting Learning in a Multi-component Serious Game
verfasst von
Carol M. Forsyth
Arthur Graesser
Keith Millis
Publikationsdatum
26.08.2019
Verlag
Springer Netherlands
Erschienen in
Technology, Knowledge and Learning / Ausgabe 2/2020
Print ISSN: 2211-1662
Elektronische ISSN: 2211-1670
DOI
https://doi.org/10.1007/s10758-019-09421-w

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