Stephen S. Killingsworth

Digital Games, Design, and Learning A Systematic Review and Meta-Analysis

In this meta-analysis, we systematically reviewed research on digital games and learning for K–16 students. We synthesized comparisons of game versus nongame conditions (i.e., media comparisons) and comparisons of augmented games versus standard game designs (i.e., value-added comparisons). We used random-effects meta-regression models with robust variance estimates to summarize overall effects and explore potential moderator effects. Results from media comparisons indicated that digital games significantly enhanced student learning relative to nongame conditions ( g ¯ = 0.33, 95% confidence interval [0.19, 0.48], k = 57, n = 209). Results from value-added comparisons indicated significant learning benefits associated with augmented game designs ( g ¯ = 0.34, 95% confidence interval [0.17, 0.51], k = 20, n = 40). Moderator analyses demonstrated that effects varied across various game mechanics characteristics, visual and narrative characteristics, and research quality characteristics. Taken together, the results highlight the affordances of games for learning as well as the key role of design beyond medium.

Concepts about the capabilities of computers and robots: a test of the scope of adults' theory of mind

We have previously demonstrated that people apply fundamentally different concepts to mechanical agents and human agents, assuming that mechanical agents engage in more location-based, and feature-based behaviors whereas humans engage in more goal-based, and category-based behavior. We also found that attributions about anthropomorphic agents such as robots are very similar to those about computers, unless subjects are asked to attend closely to specific intentional-appearing behaviors. In the present studies, we ask whether subjects initially do not attribute intentionality to robots because they believe that temporary limits in current technology preclude real intelligent behavior. In addition, we ask whether a basic categorization as an artifact affords lessened attributions of intentionality. We find that subjects assume that robots created with future technology may become more intentional, but will not be fully equivalent to humans, and that even a fully human-controlled robot will not be as intentional as a human. These results suggest that subjects strongly distinguish intelligent agents based on intentionality, and that the basic living/mechanical distinction is powerful enough, even in adults, to make it difficult for adults to assent to the possibility that mechanical things can be fully intentional.

Contextual Markup and Mining in Digital Games for Science Learning: Connecting Player Behaviors to Learning Goals

Digital games can make unique and powerful contributions to K-12 science education, but much of that potential remains unrealized. Research evaluating games for learning still relies primarily on pre- and post-test data, which limits possible insights into more complex interactions between game design features, gameplay, and formal assessment. Therefore, a critical step forward involves developing rich representations for analyzing gameplay data. This paper leverages data mining techniques to model learning and performance, using a metadata markup language that relates game actions to concepts relevant to specific game contexts. We discuss results from a classroom study and identify potential relationships between students’ planning/prediction behaviors observed across game levels and improvement on formal assessments. The results have implications for scaffolding specific activities, that include physics learning during gameplay, solution planning and effect prediction. Overall, the approach underscores the value of our contextualized approach to gameplay markup to facilitate data mining and discovery.