Date of Award
Doctor of Philosophy
Arther C Graesser
The dissertation investigates predictors of shallow versus deep learning within a serious game known as Operation ARA. The 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. Students are expected to learn about 11 topics of research methodology across three distinct modules that target factual information, application of reasoning to specific cases, and question generation. The first goal of this dissertation is to blend Evidence-Centered Design(ECD) and educational data mining (EDM) in an effort to identify and discover the best predictors of shallow versus deep level learning. In line with ECD, time-honored constructs of time-on-task, discrimination, generation, and scaffolding were selected as constructs because there is a large research history supporting their importance to learning. The first study included 192 college students who participated in a pretest-interaction-posttest study. These data were used to discover the best predictors of learning across the training experiences. The second study (N = 81) confirmed that varying types of knowledge (shallow vs. deep) are acquired across the different training modules of the game in an ablation experiment that manipulated the presence or absence of a module. Results revealed distinctly different patterns of predictors of deep versus shallow learning for students across the training environments of the game. Specifically, more interactivity may be important for environments contributing to shallow learning whereas generation and discrimination may be more important in training environments supporting deepr learning. However, in certain training environments the positive impact of generation may be at the price of discrimination.
dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.
Forsyth, Carolyn McGregor, "Predicting Learning: A Fine-Grained Analysis of Learning within a Serious Game" (2014). Electronic Theses and Dissertations. 1037.