National Science Education Standards

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The Uses Of Argument

This article reviews the (quasi)experimental research of the past decade on the learning effects of computer simulations in science education The focus is on two questions: how use of computer simulations can enhance traditional education, and how computer simulations are best used in order to improve learning processes and outcomes We report on studies that investigated computer simulations as a replacement of or enhancement to traditional instruction In particular, we consider the effects of variations in how information is visualized, how instructional support is provided, and how computer simulations are embedded within the lesson scenario The reviewed literature provides robust evidence that computer simulations can enhance traditional instruction, especially as far as laboratory activities are concerned However, in most of this research the use of computer simulations has been approached without consideration of the possible impact of teacher support, the lesson scenario, and the computer simulation's place within the curriculum

The learning effects of computer simulations in science education

Science and Engineering Practices that connect to garden-based education (all 8): • Asking questions (for science) and defining problems (for engineering) • Developing and using models • Planning and carrying out investigations • Analyzing and interpreting data • Using mathematics and computational thinking • Constructing explanations (for science) and designing solutions (for engineering) • Engaging in argument from evidence • Obtaining, evaluating, and communicating information

Next Generation Science Standards

This project consisted of a meta-analysis of U.S. research published from 1980 to 2004 on the effect of specific science teaching strategies on student achievement. The six phases of the project included study acquisition, study coding, determination of intercoder objectivity, establishing criteria for inclusion of studies, computation of effect sizes for statistical analysis, and conducting the analyses. Studies were required to have been carried out in the United States, been experimental or quasi-experimental, and must have included effect size or the statistics necessary to calculate effect size. Sixty-one studies met the criteria for inclusion in the meta-analysis. The following eight categories of teaching strategies were revealed during analysis of the studies (effect sizes in parentheses): Questioning Strategies (0.74); Manipulation Strategies (0.57); Enhanced Material Strategies (0.29); Assessment Strategies (0.51); Inquiry Strategies (0.65); Enhanced Context Strategies (1.48); Instructional Technology (IT) Strategies (0.48); and Collaborative Learning Strategies (0.95). All these effect sizes were judged to be significant. Regression analysis revealed that internal validity was influenced by Publication Type, Type of Study, and Test Type. External validity was not influenced by Publication Year, Grade Level, Test Content, or Treatment Categories. The major implication of this research is that we have generated empirical evidence supporting the effectiveness of alternative teaching strategies in science. 2007 Wiley Periodicals, Inc. J Res Sci Teach 44: 1436-1460, 2007

A Meta-Analysis of National Research: Effects of Teaching Strategies on Student Achievement in Science in the United States

A curriculum unit for middle school Earth Science called “What's on Your Plate?” was designed. The unit was implemented in several middle and high school classrooms in California and Massachusetts. In the first implementation, the total number of students who participated was 1100. The unit was designed with two main pedagogical principles: make thinking visible, and help students learn from one another; both were derived from an inquiry-based framework. With these two main pedagogical principles as a larger guiding framework, we designed the curriculum to provide students with rich, iterative model-based activities for students to both learn with and provide criteria for them to critique their peers' work from the opposite coast. The goal here was to influence students' understanding of the domain as well as their understanding of the nature of models in science by engaging them in an authentic context in which they constructed and reasoned with models, as well as critiqued the models of their peers. Data from 15 classrooms is described both in terms of the gains students made of their understanding of the nature of models as measured by pencil and paper survey administered both before and after the unit. In addition, a small subset of students' data is shown to illustrate advances in students' understanding of models. Lastly, we show how students with more sophisticated epistemologies of models are better able to further their content understanding as compared to students with less sophisticated epistemologies of models.

Fostering Students' Epistemologies of Models via Authentic Model-Based Tasks.

The studies reported in this paper are an initial effort to explore the applicability of computational models in introductory science learning. Two instructional interventions are described that use a molecular dynamics model embedded in a set of online learning activities with middle and high school students in 10 classrooms. The studies indicate that middle and high schools students can acquire robust mental models of the states of matter through guided explorations of computational models of matter based on molecular dynamics. Using this approach, students accurately recall arrangements of the different states of matter, and can reason about atomic interactions. These results are independent of gender and they hold for a number of different classroom contexts. Follow-up interviews indicate that students are able to transfer their understanding of phases of matter to new contexts.

Reasoning with Atomic-Scale Molecular Dynamic Models

Though addressing sources of uncertainty is an important part of doing science, it has largely been neglected in assessing students' scientific argumentation. In this study, we initially defined a scientific argumentation construct in four structural elements consisting of claim, justification, uncertainty qualifier, and uncertainty rationale. We consulted literature to characterize and score different levels of student performances on each of these four argumentation elements. We designed a test comprised of nine scientific argumentation tasks addressing climate change, the search for life in space, and fresh water availability and administered it to 473 students from 9 high schools in the United States. After testing the local dependence and unidimensionality assumptions, we found that the uncertainty qualifier element was not aligned with the other three. After removing items related to uncertainty qualifier, we applied a Rasch analysis based on a Partial Credit Model. Results indicate that (1) claim, justification, and uncertainty rationale items form a unidimensional scale, (2) justification and uncertainty rationale items contribute the most on the unidimensional scientific argumentation scale as they cover much wider ranges of the scale than claim items, (3) average item difficulties increase in the order of claim, justification, and uncertainty rationale, (4) students' elaboration of uncertainty exhibits dual characteristics: self-assessment of their own knowledge and ability versus scientific assessment of conceptual and empirical errors embedded in investigations, and (5) students who can make warrants between theory and evidence are more likely to think about uncertainty from scientific sources than those who cannot. We identified limitations of this study in terms of science topic coverage and sample selection and made suggestions on how these limitations might have affected results and interpretations. © 2014 Wiley Periodicals, Inc. J Res Sci Teach 51: 581–605, 2014

Assessment of uncertainty-infused scientific argumentation

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sce.21504

Automated Text Scoring and Real-Time Adjustable Feedback: Supporting Revision of Scientific Arguments Involving Uncertainty.

Modeling and argumentation are two important scientific practices students need to develop throughout school years. In this paper, we investigated how middle and high school students (N = 512) construct a scientific argument based on evidence from computational models with which they simulated climate change. We designed scientific argumentation tasks with three increasingly complex dynamic climate models. Each scientific argumentation task consisted of four parts: multiple-choice claim, openended explanation, five-point Likert scale uncertainty rating, and open-ended uncertainty rationale. We coded 1,294 scientific arguments in terms of a claim’s consistency with current scientific consensus, whether explanations were model based or knowledge based and categorized the sources of uncertainty (personal vs. scientific). We used chi-square and ANOVA tests to identify significant patterns. Results indicate that (1) a majority of students incorporated models as evidence to support their claims, (2) most students used model output results shown on graphs to confirm their claim rather than to explain simulated molecular processes, (3) students’ dependence on model results and their uncertainty rating diminished as the dynamic climate models became more and more complex, (4) some students’ misconceptions interfered with observing and interpreting model results or simulated processes, and (5) students’ uncertainty sources reflected more frequently on their assessment of personal knowledge or abilities related to the tasks than on their critical examination of scientific evidence resulting from models. These findings have implications for teaching and research related to the integration of scientific argumentation and modeling practices to address complex Earth systems.

Amy Pallant

Papers

Fostering Students' Epistemologies of Models via Authentic Model-Based Tasks.

Reasoning with Atomic-Scale Molecular Dynamic Models

Assessment of uncertainty-infused scientific argumentation

Automated Text Scoring and Real-Time Adjustable Feedback: Supporting Revision of Scientific Arguments Involving Uncertainty.

Constructing Scientific Arguments Using Evidence from Dynamic Computational Climate Models