Highlights 1. We designed an experience integrating augmented reality and environmental probes. 2. This combination of technologies had benefits for both teachers and for learners. 3. Gains were revealed on both affective and content dimensions of learning. 4. These technologies facilitated student-centered instructional practices. 5. EcoMOBILE promoted science understanding more than previous field trips without AR and probeware.

Although the relationships between family income and student identification for gifted programming are well documented, less is known about how school and district wealth are related to student identification. To examine the effects of institutional and individual poverty on student identification, we conducted a series of three-level regression models. Students of poverty are generally less likely to be identified for gifted services, even after controlling for prior math and reading achievement. Furthermore, school poverty predicts the percentage of gifted students identified in a school. Within districts, even after controlling for reading and math scores, the poorer schools in a district have lower identification rates. Whereas students of poverty are generally less likely to be identified for gifted services, poor students in poor schools are even less likely to be identified as gifted.

a b s t r a c tUsing latent growth models, we explored: (a) The effect of middle school students' (n ¼ 189) preintervention science self-efficacy and science interest on their initial interest in an Ecosystems MultiUser Virtual Environment (EcoMUVE) and the rate of change in their interest in EcoMUVE; and (b) the mediating effect of students' initial interest in EcoMUVE and rate of change in interest on students' postintervention science self-efficacy and interest in science. Results showed that: (1) students' preintervention self-efficacy for science had an effect both on students' triggered situational interest for EcoMUVE and on students' maintained situational interest for EcoMUVE; (2) both triggering and maintaining situational interest in EcoMUVE were important in developing students' science selfefficacy. In fact, maintained situational interest was the stronger predictor; and (3) maintained situational interest for EcoMUVE translated into individual interest for the science content. Results support and extend social cognitive theory as well as models of interest development.

We explored Grade 6 students' (n = 202) self-efficacy, epistemic beliefs, and science interest over a 10-day virtual ecology curriculum. Pre-and post-surveys were administered, and analyses revealed that (1) students became more self-efficacious about inquiring scientifically after participating in the activity; (2) students on average evinced a shift toward more constructivist views about the role of authority in justifying scientific claims; (3) students who identified more strongly with being a science person evinced greater gains in self-efficacy, developed a less constructivist view about the role of authority in justifying claims, and became more interested in science overall; and (4) students who held an incremental theory of ability evinced greater gains in self-efficacy. We discuss the implications of these findings for science educators and instructional designers in the design and use of immersive virtual worlds for middle school science students.

No abstract

Many pedagogical innovations aim to integrate engineering design and science learning. However, students frequently show little attempt or have difficulties in connecting their design projects with the underlying science. Drawing upon the Cultural‐Historical Activity Theory, we argue that the design tools available in a learning environment implicitly shape knowledge development as they mediate students’ design actions. To explore the roles of tools in design‐science integrated learning environments, this study investigated how secondary students’ tool‐mediated design actions were linked with their science learning in a tool‐rich design environment with minimal explicit guidance. Eighty‐three ninth‐grade students completed an energy‐efficient home design challenge in a simulated environment for engineering design supported by rich design tools. Results showed that students substantially improved their knowledge as a result of designing with the tools. Further, their learning gains were positively associated with three types of design actions—representation, analysis, and reflection—measured by the cumulative counts of relevant computer logs. In addition, these design actions were linked with learning gains in ways that were consistent with their theoretical impacts on knowledge development. These findings suggest that, instead of being passive components in a learning environment, tools considerably shape design processes, and learning paths. As such, tools offer possibilities to help bridge the design‐science gap. © 2017 The Authors. Journal of Research in Science Teaching Published by Wiley Periodicals, Inc. J Res Sci Teach 9999:1049–1096, 2017

We report on data collected at 3 time points during a 1-year intervention designed to teach a purposive sample of geoscience faculty members (n ϭ 29) from 27 universities throughout the United States how to identify and address issues related to diversity, equity, and inclusion in their departments. For the intervention we used mixed-reality simulations to help participants practice specific skills to address common situations in geoscience departments. The intervention also included an intensive 3-day workshop and 3 journal clubs. Using a Bayesian analytical approach we explored: (a) general trends in participants' self-and collective efficacy for identifying and addressing diversity, equity, and inclusion over a 1-year period; (b) relationships between self-efficacy and collective efficacy; and (c) demographic factors that explain variation in self-and collective efficacy. Results showed that self-and collective efficacy rose sharply from preintervention to 5 months after beginning. Although both self-and collective efficacy retreated toward baseline at the 1-year mark, only 1-year self-efficacy was still credibly higher than preintervention. Also, preintervention self-efficacy predicted 5-month collective efficacy. Efficacy beliefs varied as a function of race/ethnicity. Only collective efficacy varied as a function of academic rank. We discuss these findings in relation to social-cognitive theory and the literature regarding the use of digital learning environments to address diversity, equity, and inclusion.

This article considers a set of well-researched default assumptions that people make in reasoning about complex causality and argues that, in part, they result from the forms of causal induction that we engage in and the type of information available in complex environments. It considers how information often falls outside our attentional frame such that covariation falls short, mechanisms can be nonobvious, and the testimony that others offer is typically subject to the same constraints as our own perceptions. It underscores the importance of multiple modes of causal induction used in support of one another when discerning and teaching about causal complexity. It considers the importance of higher order reflection on the nature of causality that recognizes the challenging features of complex causality and how it interacts with human causal cognition.An expansive literature exists on how humans engage in causal induction. Laboratory and classroom research reveals fairly sophisticated reasoning in these confined contexts and offers models that effectively account for how people reason. However, engaging effectively in causal induction in a complex world is a vastly more complicated task and challenging to account for. Reasoning well about our world requires cognitive flexibility in perceiving and attending to the features and parameters of a problem space and in considering how patterns are structured. It necessitates looking beyond immediate constraints and events to reason about extended temporal and spatial frames and about processes and steady states. It involves detecting nonlinear, indirect, and interactive relationships and considering agentive and nonagentive causes-including those that might be deemed passive and nonintentional. Human cognition appears to be heuristicdriven in ways that may be adaptive in some instances and yet in others can derail an ability to discern and understand these complex causal features.This article reviews findings from disparate literatures on the types of assumptions that people typically make about the nature of causality, the heuristics that they engage, and how they constrain causal inference and limit causal searches. It considers the possibility that reasoning characterized by covariance accounts, broadly speaking, and causal Bayes net (CBN) accounts specifically, may contribute to the robustness of these default assumptions when reasoning about particular forms of causal complexity. It examines how knowledge of mechanism and testimony may interact with covariance accounts in instances when people are limited in their ability to draw inferences based upon the available information and constraints of everyday contexts. The article argues that discerning causality in a complex world necessitates flexible Volume 8-Number 3