Search

The Next Generation Science Standards (NGSS) have spurred renewed interest in the epistemologies that students adopt as they engage in science practices. One framework for characterizing students' epistemologies is the "epistemologies in practice" framework (Berland et al. (2016), "Journal of Research in Science Teaching," 53(7), 1082-1112), which focuses on students' meaningful use of four epistemic considerations: Nature, Generality, Justification, and Audience. To date, research based on the framework has primarily examined students' use of the epistemic considerations in the context of diagrammatic modeling. However, with computational technologies becoming more prevalent in science classrooms, the framework could be applied to investigate students' engagement in computational modeling. Moreover, computational modeling could be particularly beneficial to a fast-growing population of multilingual learners (MLs) in the U.S. K-12 context, who benefit from leveraging multiple meaning-making resources (e.g., code, dynamic visualization). This study examined MLs' meaningful use of four epistemic considerations in the context of computational modeling in an elementary science classroom. Fifth-grade MLs (N = 11) participated in two interviews about computational models they had developed as part of two NGSS-designed instructional units that integrated computational modeling (in addition to other model types). Findings indicated that, while students used all four epistemic considerations across the interviews, some considerations (Nature and Generality) were used more frequently than others (Justification and Audience). Beyond diagrammatic modeling, "computational" modeling offered unique affordances for MLs to meaningfully use the considerations as well as to communicate this use, though not without some emergent challenges. Overall, this study highlights the promise of computational modeling for providing a rich sense-making and meaning-making context for MLs to use epistemic considerations. The study also highlights the importance of attending to both epistemic and linguistic aspects of MLs' science learning as well as the potential of interdisciplinary research for studying this learning.

Computerized classification of student answers offers the possibility of instant feedback and improved learning. Open response (OR) questions provide greater insight into student thinking and understanding than more constrained multiple choice (MC) questions, but development of automated classifiers is more difficult, often requiring training a machine learning system with many human-classified answers. Here we explore a novel intermediate constraint question format called WordBytes (WB) where students assemble one-sentence answers to two different college evolutionary biology questions by choosing, then ordering, fixed tiles containing words and phrases. We found WB allowed students to construct hundreds to thousands of different answers (= 20 tiles), with multiple ways to express correct and incorrect answers with different misconceptions. We found humans could specify rules for an automated WB grader that could accurately classify answers as correct/incorrect with Cohen's kappa = 0.88, near the measured intra-rater reliability of two human graders and the performance of machine classification of OR answers (Nehm et al., 2012). Finer-grained classification to identify the specific misconception had lower accuracy (Cohen's kappa < 0.75), which could be improved either by using a machine learner or revising the rules, but both would require considerably more development effort. Our results indicate that WB may allow rapid development of automated correct/incorrect answer classification without collecting and hand-grading hundreds of student answers.

This research investigates a design and development approach to improving science teachers' access to effective professional development (PD) in a fully online, asynchronous environment. Working with a small number of teachers, this study explores how a design combining social capital mechanisms with essential teacher learning and PD characteristics supported teachers' abilities to participate in the online course and collaboratively build knowledge. Teachers' perceptions of their experiences both in surveys and interviews demonstrated high satisfaction with the quality and usability of the PD, including positive beliefs related to the social capital elements of tie quality, depth of interaction, and access to expertise. Further transactivity analyses of their interactions in course discussions showed higher levels of collaborative discourse resulting from prompts that specifically targeted the exchange of information over those that asked teachers to reflect about their content understanding or their classroom practice. Implications for this design for asynchronous online PD approaches to reach more teachers are discussed.

Purpose: The overarching goal of the research is to understand strategies that can support utility and access to high-quality teacher professional development (PD). This study aims to examine the design and delivery of an online asynchronous course for science teachers using the edX massively online open course (MOOC) platform. The conceptual framework considers three areas of research: high-quality PD characteristics for K12 teachers, the development of social capital and known challenges in MOOC and computer-supported collaborative learning and participation. Design/methodology/approach: This is an empirical mixed-methods study that details the design of the PD course and implementation strategies that instantiate the conceptual framework. The authors collected three data sources from 41 teachers who completed the course. These included post course satisfaction surveys, teacher semi structured interviews and discussion board contributions. Findings: Survey findings revealed high satisfaction among teachers in the areas of overall course design, module construction and delivery and usability of materials in teaching. Interview findings showed positive perceptions of the social capital framing in developing tie quality, trust, depth of interactions and access to expertise. Analyses of discussion board contributions also demonstrated high degrees of information exchange resulting from prompts intentionally constructed to foster collaboration. Practical implications: This study offers a set of strategies to build networked teacher PD communities in asynchronous online PD platforms and shows promising evidence of addressing quality and access issues. Social implications: Designing experiences to build teachers' social capital shows promising potential to support high quality PD that may, in turn, raise the quality of science education for students and classrooms both locally in the US and globally. Originality/value: The conceptual framework provides a novel approach to theorizing and operationalizing best practices for teacher PD and online participation.

The recent next generation science standards in the United States have emphasized learning about complex systems as a core feature of science learning. Over the past 15 years, a number of educational tools and theories have been investigated to help students learn about complex systems; but surprisingly, little research has been devoted to identifying the supports that teachers need to teach about complex systems in the classroom. In this paper, we aim to address this gap in the literature. We describe a 2-year professional development study in which we gathered data on teachers' abilities and perceptions regarding the delivery of computer-supported complex systems curricula. We present results across the 2 years of the project and demonstrate the need for particular instructional supports to improve implementation efforts, including providing differentiated opportunities to build expertise and addressing teacher beliefs about whether computational-model construction belongs in the science classroom. Results from students' classroom experiences and learning over the 2 years are offered to further illustrate the impact of these instructional supports.

StarLogo The Next Generation (TNG) enables secondary school students and teachers to model decentralized systems through agent-based programming. TNG's inclusion of a three-dimensional graphical environment provides the capacity to create games and simulation models with a first-person perspective. The authors theorize that student learning of complex systems and simulations can be motivated and improved by transforming simulation models of complex systems phenomena (specifically this study examines systems including epidemics and Newtonian motion) into games. Through this transformation students interact with the model in new ways and increase their learning of both specific content knowledge and general processes such as inquiry, problem solving and creative thinking. During this study several methods for connecting the simulations to game dynamics were tried, ranging from student-created games, to altering existing games, to students playing pre-made games. This article presents the results of research data from two years of curriculum development and piloting in northern Massachusetts science classrooms to demonstrate the successes and challenges of integrating simulations and games. This article also explores the results of these interventions in terms of ease of implementation, student motivation and student learning.