Fostering Students\u27 Identification with Mathematics and Science

Book Summary: Interest in Mathematics and Science Learning is the first volume to assemble findings on the role of interest in mathematics and science learning. As the contributors illuminate across the volume’s 22 chapters, interest provides a critical bridge between cognition and affect in learning and development. This volume will be useful to educators, researchers, and policy makers, especially those whose focus is mathematics, science, and technology education. Chapter Summary: The primary purpose of this chapter is to explore the process whereby students transition from a short-term, situational interest in mathematics or science to a more enduring individual interest in which they incorporate performance in mathematics or science into their self-definitions (e.g. I am a scientist ). We do so by examining the research related to domain identification, which is the extent to which students define themselves through a role or performance in a domain, such as mathematics or science. Understanding the process of domain identification is important because it can contribute to an understanding of how individual interest develops over time. The means through which students become highly domain identified involves many factors that are internal (e.g. goals and beliefs) and external (e.g. family environment and educational experiences) to them. Students who are more identified with an academic domain tend to demonstrate increased motivation, effort, perseverance (when faced with failure), and achievement. Importantly, students with lower domain identification tend to demonstrate less motivation, lower effort, and fewer desirable outcomes. Student outcomes in a domain can reciprocally influence domain identification by reinforcing or altering it. This feedback loop can help explain incremental changes in motivation, self-concept, individual interest, and, ultimately, important outcomes such as achievement, choice of college major, and career path. This dynamic model presents possible mechanisms for influencing student outcomes. Furthermore, assessing students\u27 domain identification can allow practitioners to intervene to prevent undesirable outcomes. Finally, we present research on how mathematics and science instructors could use the principles of the MUSIC Model of Academic Motivation to enhance students\u27 domain identification, by (a) empowering students, (b) demonstrating the usefulness of the domain, (c) supporting students\u27 success, (d) triggering students\u27 interests, and (e) fostering a sense of caring and belonging. We conclude that by using the MUSIC model, instructors can intentionally design educational experiences to help students progress from a situational interest to one that is more enduring and integrated into their identities

Ancient Cities and Landscapes in the Kurdistan Region of Iraq: The Erbil Plain Archaeological Survey 2012 Season

In 2012, the Erbil Plain Archaeological Survey (EPAS) conducted its first season of fieldwork. The project’s goal is the complete mapping of the archaeological landscape of Erbil, with an emphasis on the Neo-Assyrian and Hellenistic periods. It will test the hypothesis that the Neo-Assyrian landscape was closely planned. This first report emphasizes the project’s field methodology, especially the use of a variety of satellite remote sensing imagery. Our preliminary results suggest that the plain was part of the urbanized world of Mesopotamia, with new cities of the Bronze Age, Iron Age, and Sasanian era identified.Anthropolog

Improving your data transformations: Applying the Box-Cox transformation

Many of us in the social sciences deal with data that do not conform to assumptions of normality and/or homoscedasticity/homogeneity of variance. Some research has shown that parametric tests (e.g., multiple regression, ANOVA) can be robust to modest violations of these assumptions. Yet the reality is that almost all analyses (even nonparametric tests) benefit from improved the normality of variables, particularly where substantial non-normality is present. While many are familiar with select traditional transformations (e.g., square root, log, inverse) for improving normality, the Box-Cox transformation (Box & Cox, 1964) represents a family of power transformations that incorporates and extends the traditional options to help researchers easily find the optimal normalizing transformation for each variable. As such, Box-Cox represents a potential best practice where normalizing data or equalizing variance is desired. This paper briefly presents an overview of traditional normalizing transformations and how Box-Cox incorporates, extends, and improves on these traditional approaches to normalizing data. Examples of applications are presented, and details of how to automate and use this technique in SPSS and SAS are included. Accessed 57,471 times on https://pareonline.net from October 06, 2010 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right

Notes on the use of data transformations

Data transformations are commonly used tools that can serve many functions in quantitative analysis of data. The goal of this paper is to focus on the use of three data transformations most commonly discussed in statistics texts (square root, log, and inverse) for improving the normality of variables. While these are important options for analysts, they do fundamentally transform the nature of the variable, making the interpretation of the results somewhat more complex. Further, few (if any) statistical texts discuss the tremendous influence a distribution\u27s minimum value has on the efficacy of a transformation. The goal of this paper is to promote thoughtful and informed use of data transformations. Accessed 244,249 times on https://pareonline.net from May 30, 2002 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right

Justice for All: moira, tyche and nemesis in the Marvel Cinematic Universe

This article explores the ways in which the ancient concepts of moira, tyche, and nemesis permeate the films and series of the Marvel Cinematic Universe

Best Practices in Using Large, Complex Samples: The Importance of Using Appropriate Weights and Design Effect Compensation

Large surveys often use probability sampling in order to obtain representative samples, and these data sets are valuable tools for researchers in all areas of science. Yet many researchers are not formally prepared to appropriately utilize these resources. Indeed, users of one popular dataset were generally found not to have modeled the analyses to take account of the complex sample (Johnson & Elliott, 1998) even when publishing in highly-regarded journals. It is well known that failure to appropriately model the complex sample can substantially bias the results of the analysis. Examples presented in this paper highlight the risk of error of inference and mis-estimation of parameters from failure to analyze these data sets appropriately. Accessed 5,573 times on https://pareonline.net from September 19, 2011 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right

A Lie Algebra of Integrals for Keplerian Motion Restricted to the Plane

In this paper we consider the slightly simpler problem of Keplerian motion restricted to the plane rather than Keplerian motion in three dimensions as done in [3, page 11]. We parallel the three-dimensional problem in that we use the same actions to find invariants (integrals) but rather than working in a six-dimensional phase space to find six independent integrals we restrict ourselves to a four-dimensional phase space. In doing this, we find that we have three independent integrals and thus we have a three-dimensional Lie algebra

What is Rotating in Exploratory Factor Analysis?

Exploratory factor analysis (EFA) is one of the most commonly-reported quantitative methodology in the social sciences, yet much of the detail regarding what happens during an EFA remains unclear. The goal of this brief technical note is to explore what rotation is, what exactly is rotating, and why we use rotation when performing EFAs. Some commentary about the relative utility and desirability of different rotation methods concludes the narrative. Accessed 43,804 times on https://pareonline.net from January 07, 2015 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right

Bringing balance and technical accuracy to reporting odds ratios and the results of logistic regression analyses

Logistic regression and odds ratios (ORs) are powerful tools recently becoming more common in the social sciences. Yet few understand the technical challenges of correctly interpreting an odds ratio, and often it is done incorrectly in a variety of different ways. The goal of this brief note is to review the correct interpretation of the odds ratio, how to transform it into the more easily understood and intuitive relative risk (RRs) estimate, and a suggestion for dealing with odds ratios or relative risk estimates that are below 1.0 so that perceptually their magnitude is equivalent of Ors or RRs greater than 1.0. Accessed 37,451 times on https://pareonline.net from October 18, 2006 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right

Advantages of Hierarchical Linear Modeling

Accessed 124,217 times on https://pareonline.net from January 10, 2000 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right