Mario Martinez-Garza

Exploring Newtonian mechanics in a conceptually-integrated digital game: Comparison of learning and affective outcomes for students in Taiwan and the United States

This study investigates the potential of a digital game that overlays popular game-play mechanics with formal physics representations and terminology to support explicit learning and exploration of Newtonian mechanics. The analysis compares test data, survey data, and observational data collected during implementations in Taiwan and the United States with students in grades 7-9. Results demonstrate learning on some core disciplinary measures and high levels of learner engagement, indicating the potential benefits of this genre of conceptually-integrated games, but also suggesting that further research and development will be needed to more fully harness this potential. Encouragingly, striking similarities were observed across the two countries in terms of learning and engagement, suggesting that this genre of learning games may prove suitable for engaging students in active exploration of core science concepts across multiple countries.

Bilingual language supports in online science inquiry environments

Research over the past fifteen years has investigated and developed online science inquiry environments to support students engaging in authentic scientific inquiry practices. This research has focused on developing activity structures and tools to scaffold students in engaging in different aspects of these practices, but relatively little of this research has explored linguistic supports for language minority students studying science in their non-native language. These students are simultaneously learning science and the surrounding academic language in their second language. This study investigates the potential value of providing 8th grade Spanish-speaking English language learners access to content and supports in both English and Spanish as opposed to an English-only format in an online science inquiry environment. Learning outcomes are compared between the two conditions on an immediate post-test in English, a delayed post-test in English, a delayed post-test in Spanish, and a written essay in English in the form of a letter to the governor. The outcomes suggest significant benefits for providing ELL students with access to content and supports in both English and Spanish as opposed to the English-only format. The findings of this study carry important policy implications in light of the growing English-only political movements in the United States and similar political movements in other countries.

Digital games and the US National Research Council’s science proficiency goals

This review synthesises research on digital games and science learning as it supports the goals for science proficiency outlined in the report by the US National Research Council on science education reform. The review is organised in terms of these research-based goals for science proficiency in light of their alignment with current science education standards and reform documents worldwide. Overall, the review suggests that digital games can support science learning across the four strands but also suggests that there are few strong quantitative studies examining some of the strands. Much of the research conducted to date has centred primarily on the potential of games to scaffold conceptual knowledge, engagement and participation. Less research has focused on epistemological understanding and science process skills. While much debate has asked whether digital games are ‘good’ or ‘bad’ for learning, the research across the strands highlights that the design of digital games, rather than their medium, ultimately determines their efficacy for learning.

Driving Assessment of Students’ Explanations in Game Dialog Using Computer-Adaptive Testing and Hidden Markov Modeling

How might research on metacognition, conceptual change, and students’ explanations inform the design of dialog systems in digital games to foster science learning and engagement? How might research on computer-adaptive testing (CAT) and hidden Markov modeling support online diagnostic modeling of students’ learning behaviors and understanding? How might a game analyze their problem-solving steps as well as explanation constructs in that dialog? This chapter explores these questions. Our goals involve (1) scaffolding students’ explicit articulation of connections between intuitive understandings and disciplinary concepts in the game environment while (2) providing mechanisms for online tracking of players’ evolving understanding to support adaptive scaffolding and provide formative and summative diagnostic information to teachers and researchers. Essentially, we propose that integrating research on conceptual change, scientific explanations, metacognition, CAT, and hidden Markov modeling in a digital game environment could simultaneously diagnose the formative and summative aspects of students’ understanding, and in this process provide an environment that fosters deep science learning. We first define the challenge of scaffolding deep learning in games at the cognitive level by contrasting constraint-based reasoning and model-based reasoning. We then explore the role of self-explanation in supporting model-based reasoning and dynamic assessment of students’ reasoning. This is followed by an outline of a model for game-based dialog to support explanation generation and analysis. This explanation dialog model leverages CAT techniques and hidden Markov modeling to develop and refine an ongoing analysis of a students’ understanding within the game based on students’ explanations within the dialog and their other actions within the game.

Advances in Assessment of Students' Intuitive Understanding of Physics through Gameplay Data

In this paper, the authors present advances in analyzing gameplay data as evidence of learning outcomes using computational methods of statistical analysis. These analyses were performed on data gathered from the SURGE learning environment Martinez-Garza, Clark, & Nelson, 2010. SURGE is a digital game designed to help students articulate their intuitive concepts of motion physics and organize them toward a more normative scientific understanding. Various recurring issues of assessment, which pervade assessment of learning in games more generally, prompted the authors to consider whether gameplay actions of learners in the context of the game can be analyzed to produce evidence of learning. The authors describe their approach to the analysis of game play in terms of qualitative assessment that the authors believe may lay the groundwork for the application of similar computationally-intensive techniques in other educational game contexts.

Two Systems, Two Stances: A Novel Theoretical Framework for Model-Based Learning in Digital Games

Recent reviews of quantitative research suggest that some but not all digital games add value when used as pedagogical tools. A more sophisticated cognitive theory of learning is required to guide the advance of educational games through improvements in design, scaffolding, and assessments. This chapter extends and improves existing mental model-based hypotheses about learning in games, particularly in terms of science learning and seeks to conceptualize simulation and game-based learning within a more general two-system theory of human cognition.

Commentary: Deep analysis of epistemic frames and passive participants around argumentation and learning in informal learning spaces ☆

Abstract Our commentary first discusses three points of interest highlighted by the current studies in terms of breadth of measured behaviors and characteristics, the commensurability of designs, and the importance and challenge of analyzing learning by passive participants. We then discuss how datamining strategies might be organized to support future research building on these points of interest.

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.