Search

Studies of educational games often treat them as "black boxes" (Black and Wiliam in Phi Delta Kappan 80: 139-48, 1998; Buckley et al. in Int J LearnTechnol 5:166-190, 2010; Buckley et al. in J Sci Educ Technol 13: 23-41, 2010) and measure their effectiveness by exposing a treatment group of students to the game and comparing their performance on an external assessment to that of a control group taught the same material by some other method. This precludes the possibility of monitoring, evaluating, and reacting to the actions of individual students as they progress through the game. To do that, however, one must know what to look for because superficial measures of success are unlikely to identify unproductive behaviors such as "gaming the system." (Baker in Philipp Comput J, 2011; Downs et al. in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, USA, 2010) The research reported here advances the ultimate goal of creating educational games that can provide real time, meaningful feedback on the progress of their users, enabling teachers or the game itself to intervene in a timely manner. We present the results of an in-depth analysis of students' actions in "Geniventure," an interactive digital game designed to teach genetics to middle and high school students. "Geniventure" offers a sequence of challenges of increasing difficulty and records students' actions as they progress. We analyzed the resulting log files, taking into account not only whether a student achieved a certain goal, but also the quality of the student's performance on each attempt. Using this information, we quantified students' performance and correlated it to their learning gain as estimated by scores on identical multiple-choice tests administered before and after exposure to "Geniventure." This analysis was performed in classes taught by teachers who had participated in professional development as part of a research project. A two-tailed paired-sample t-test of mean pre-test and post-test scores in these classes indicates a significant positive difference with a large effect size. Multivariate regression analysis of log data finds no correlation between students' post-test scores and their performance on "practice" challenges that invite experimentation, but a highly significant positive correlation with performance on "assessment" challenges, presented immediately following the practice challenges, that required students to invoke relevant mental models. We repeated this analysis with similar results using a second group of classes led by teachers who implemented "Geniventure" on their own after the conclusion of, and with no support from, the research project.

Teaching and understanding evolution is challenging because students must synthesize several biological processes that are traditionally taught separately. We developed a set of free online lessons and activities centered on a single evolutionary phenomenon -- why deer mice have different fur colors in different subpopulations -- to help high school students better understand how these different biological processes, operating at different scales, work together to influence a single organismal trait. Through scaffolded investigations, guiding questions, model building, and pointed analysis, students learn how ecology, cellular biology, molecular biology, genetic inheritance, and population genetics all work together to influence a shift in fur color over time. Using an innovative multilevel simulation, students manipulate and examine these different processes from the population level all the way down to the DNA level. In this article, we describe the lessons and materials we have developed for high school biology students. We outline the learning goals and highlight the major components of the technology and activities in each lesson. We then provide information on how to access our curricular and support materials, and conclude with user feedback from our pilot testing.

This study examined students' genetics learning in a game-based environment by exploring the connections between the expectancy-value theory of achievement motivation and flow theory. A total of 394 secondary school students were recruited and learned genetics concepts through interacting with a game-based learning environment. We measured their science self-efficacy, science outcome-expectancy beliefs, flow experience, feelings of frustration, and conceptual understanding before and after playing the game, as well as their game satisfaction. Mixed-model ANOVA, correlation tests, and path analysis were run to answer our research questions. Based on the results, we found that the game had a significant impact on students' conceptual understanding of genetics. We also found an acceptable statistical model of the integration between the two theories. Flow experience and in-game performance significantly impacted students' posttest scores. Moreover, science outcome-expectancy belief was found to be a significant predictor of students' flow experiences. In contrast, science self-efficacy and pretest scores were found to be the most significant factors influencing the feeling of frustration during the game. The results have practical implications with regard to the positive role that an adaptive game-based genetics learning environment might play in the science classroom. Findings also underscore the role the teacher should play in establishing productive outcome expectations for students prior to and during gameplay.

Research on the use of digital games suggests they can enhance students' learning; however, teachers often play an important role in mediating gameplay and a game's educational goals. The purpose of the study was to investigate implementation approaches of nine biology teachers using an immersive digital game in their science classes, focusing on factors that contributed to their ability to instruct with the game, and how their enactment of the game influenced the class experience. Analysis of teacher data, which included daily feedback and pre- and post-implementation surveys, multiple classroom observations, teaching artifacts, and an extended interview, identified a range of individual instructional decisions as well as similarities and differences across the cohort. Most notably, a pattern of instructional orchestration emerged, resembling co-teaching--a reciprocal and supportive "relationship" between the teacher and the game. The game informed teachers' thinking about their genetics curriculum and enhanced their instructional practice, while teachers leveraged digital tools to shape students' gameplay and to improve on what the game offered. Key descriptive findings are discussed, identifying digital game features that may improve teacher instruction with games in classrooms.

When experienced analysts explore data in a rich environment, they often transform the dataset. For example, they may choose to group or filter data, calculate new variables and summary measures, or reorganize a dataset by changing its structure or merging it with other information. Such actions background, highlight, or even fundamentally change particular features of the data, allowing different types of questions to be explored. We call these actions data moves. In this paper, we argue that paying explicit attention to data moves, as well as their purposes and consequences, is necessary for educators to support student learning about data. This is especially needed in an era when students are expected to develop critical literacy around data and engage in purposeful, self-directed exploration of large and often complex datasets.

Research-based digital games hold great potential to be effective tools in supporting next-generation science learning. However, as with all instructional materials, teachers significantly influence their implementation and contribute to their effectiveness. To more fully understand the contributions and challenges of teacher implementation of digital games, we studied the replacement of existing high school biology genetics lessons over a 3- to 6-week period with Geniverse, an immersive, game-like learning environment designed to be used in classrooms. The Geniverse materials infuse virtual experimentation in genetics with a narrative of a quest to heal a genetic disease; incorporate the topics of meiosis and protein synthesis with inheritance; and include the science practices of explanation and argumentation. The research design involved a quasi-experiment with 48 high school teachers and about 2000 students, student science content knowledge and argumentation outcome measures, and analysis using hierarchical linear modeling. Results indicate that when Geniverse was implemented as the designers intended, student learning of genetics content was significantly greater than in the comparison, business-as-usual group. However, a wide range of levels of Geniverse implementation resulted in no significant difference between the groups as a whole. Students' abilities to engage in scientific explanation and argumentation were greater in the Geniverse group, but these differences were not statistically significant. Observation, survey, and interview data indicate a range of barriers to implementation and teacher instructional decisions that may have influenced student outcomes. Implications for the role of the teacher in the implementation of game-based instructional materials are discussed.

This article reports on the potential of higher education science faculty involvement in a K-12 scientist-teacher partnership as a source of faculty professional development.

Evolution is often challenging for high school students to understand because it encompasses an array of interlinked processes that occur across a broad swath of biological scales. To help address this, we have developed a set of freely accessible, online, and interactive lessons that focus on the evolution of sweet garden peas from their starchy tasting ancestors. Gregor Mendel first explored the genetics of garden peas in the mid-1800s; our materials help students explore the basis of the R and r alleles from genetic, protein, cell function, artificial selection, and population genetics perspectives. These Next Generation Science Standards (NGSS)–linked lessons integrate concepts across scales and are designed to be used in a flexible order, with support provided to teachers on how to choose a sequence that meets their students’ needs. Throughout, students act as scientists as they uncover how multiple processes at disparate scales all worked together in the evolution of sweet and wrinkled peas from ancestors that were starchy and round. [ABSTRACT FROM AUTHOR]

Teaching and understanding evolution is challenging because students must synthesize several biological processes that are traditionally taught separately. We developed a set of free online lessons and activities centered on a single evolutionary phenomenon - why deer mice have different fur colors in different subpopulations - to help high school students better understand how these different biological processes, operating at different scales, work together to influence a single organismal trait. Through scaffolded investigations, guiding questions, model building, and pointed analysis, students learn how ecology, cellular biology, molecular biology, genetic inheritance, and population genetics all work together to influence a shift in fur color over time. Using an innovative multilevel simulation, students manipulate and examine these different processes from the population level all the way down to the DNA level. In this article, we describe the lessons and materials we have developed for high school biology students. We outline the learning goals and highlight the major components of the technology and activities in each lesson. We then provide information on how to access our curricular and support materials, and conclude with user feedback from our pilot testing. [ABSTRACT FROM AUTHOR]