Circular on Joint Commendation of "Green Schools"

Students graduating in applied sciences from well-known universities and colleges this year are, as ever, getting the green light in their quest for work units, while those graduating in the basic sciences, especially girl students, are out of luck.     

Analyzing Longitudinal Data Using Natural Cubic Smoothing Splines

The analysis of longitudinal data has received widespread interest in the behavioral, educational, medical, and social sciences for many years. Many modeling techniques have been suggested for conducting such analyses, especially when the data exhibit complex nonlinear trajectory patterns. A major problem with many of these modeling techniques, however, is that they often either impose overly restrictive assumptions or can be computationally demanding. The purpose of this paper is to introduce a less known but highly effective modeling procedure that can be used to model complex nonlinear longitudinal data patterns. The procedure is illustrated using empirical data along with an easy to use computerized implementation.     

Nonlinear Growth Mixture Models With Fractional Polynomials: An Illustration With Early Childhood Mathematics Ability

Applications of growth mixture modeling have become widespread in the fields of medicine, public health, and the social sciences for modeling linear and nonlinear patterns of change in longitudinal data with presumed heterogeneity with respect to latent group membership. However, in contrast to linear approaches, there has been relatively less focus on methods for modeling nonlinear change. We introduce a nonlinear mixture modeling approach for estimating change trajectories that rely on the use of fractional polynomials within a growth mixture modeling framework. Fractional polynomials allow for more parsimonious and flexible models in comparison to conventional polynomial models. The procedures are illustrated through the use of math ability scores obtained from 499 children over a period of 3 years, with 4 measurement occasions. Techniques for identifying the best empirically derived growth mixture model solution are also described and illustrated by way of substantive example and a simulation.     

Implementing Restricted Maximum Likelihood Estimation in Structural Equation Models

Structural equation modeling (SEM) is now a generic modeling framework for many multivariate techniques applied in the social and behavioral sciences. Many statistical models can be considered either as special cases of SEM or as part of the latent variable modeling framework. One popular extension is the use of SEM to conduct linear mixed-effects modeling (LMM) such as cross-sectional multilevel modeling and latent growth modeling. It is well known that LMM can be formulated as structural equation models. However, one main difference between the implementations in SEM and LMM is that maximum likelihood (ML) estimation is usually used in SEM, whereas restricted (or residual) maximum likelihood (REML) estimation is the default method in most LMM packages. This article shows how REML estimation can be implemented in SEM. Two empirical examples on latent growth model and meta-analysis are used to illustrate the procedures implemented in OpenMx. Issues related to implementing REML in SEM are discussed.     

The Strategies of Modeling in Biology Education

Modeling, like inquiry more generally, is not a single method, but rather a complex suite of strategies. Philosophers of biology, citing the diverse aims, interests, and disciplinary cultures of biologists, argue that modeling is best understood in the context of its epistemic aims and cognitive payoffs. In the science education literature, modeling has been discussed in a variety of ways, but often without explicit reference to the diversity of roles models play in scientific practice. We aim to expand and bring clarity to the myriad uses of models in science by presenting a framework from philosopher of biology Jay Odenbaugh that describes five pragmatic strategies of model use in the biological sciences. We then present illustrative examples of each of these roles from an empirical study of an undergraduate biological modeling curriculum, which highlight how students used models to help them frame their research question, explore ideas, and refine their conceptual understanding in an educational setting. Our aim is to begin to explicate the definition of modeling in science in a way that will allow educators and curriculum developers to make informed choices about how and for what purpose modeling enters science classrooms.     

The concept of “model” and educational research

Models have played an important role in the development of the physical sciences. Perhaps their role in educational research will be even more important.     

Integration of Stochastic Differential Equations Using Structural Equation Modeling: A Method to Facilitate Model Fitting and Pedagogy

Stochastic differential equation (SDE) models are a promising method for modeling intraindividual change and variability. Applications of SDEs in the social sciences are relatively limited, as these models present conceptual and programming challenges. This article presents a novel method for conceptualizing SDEs. This method uses structural equation modeling (SEM) conventions to simplify SDE specification, the flexibility of SEM to expand the range of SDEs that can be fit, and SEM diagram conventions to facilitate the teaching of SDE concepts. This method is a variation of latent difference scores (McArdle, 2009; McArdle & Hamagami, 2001) and the oversampling approach (Singer, 2012), and approximates the advantages of analytic methods such as the exact discrete model (Oud & Jansen, 2000) while retaining the modeling fiexibility of methods such as latent differential equation modeling (Boker, Neale, & Rausch, 2004). A simulation and empirical example are presented to illustrate that this method can be implemented on current computing hardware, produces good approximations of analytic solutions, and can flexibly accommodate novel models.     

Creating Self‐Generating Knowledge Sharing Spirals: Improving Motivation in a Knowledge Economy

Many new research advancements have been gained in fields other than the basic social sciences. Some of these advancements come from multidisciplinary studies that merge social sciences with theories from other fields of study. This article takes a multidisciplinary approach, juxtaposing some of the more recent findings from other disciplines to develop a general model for improving performance. The proposed model utilizes social theory from sociology and community studies, knowledge spirals from the knowledge management discipline, motivation theories derived in part from evolutionary biology, and research on engagement from different fields of study within the social sciences. By incorporating a multidisciplinary theory for performance improvement, the authors are adhering to recommendations from researchers calling for alternative paradigms to be utilized, providing a more holistic understanding of the phenomena being studied (Storberg‐Walker, 2007 ; Torraco, 2004 ).     

Assessing Students’ Deep Conceptual Understanding in Physical Sciences: an Example on Sinking and Floating

This paper presents a transformative modeling framework that guides the development of assessment to measure students’ deep understanding in physical sciences. The framework emphasizes 3 types of connections that students need to make when learning physical sciences: (1) linking physical states, processes, and explanatory models, (2) integrating multiple explanatory models, and (3) connecting scientific models to concrete experiences. We carried out a 2-phase exploratory study that helped further develop and refine the framework. In the first phase, we developed 3 items on sinking and floating and pilot tested them with 18 undergraduate students. Analysis of student responses revealed various student misconceptions and the different connections students made among science ideas. Based on the findings, we revised the assessment, modified the instruction, and collected data from another cohort of 26 students. The second cohort of students showed significant improvement of understanding of sinking and floating after instruction. Implications and limitations of how our assessment framework can be used to improve students’ conceptual understanding in science are discussed.     

Knowledge‐ and Evidence‐Based Education: Reasons,Trends, and Contents

Education is in need of reform, and the development of the sciences of mind and brain are candidates to contribute to the renovation of the way people are instructed and of the tools that mediate learning. Knowledge‐ and evidence‐based approaches to education put forward the fact that educational systems are inadequate to provide an answer to the challenges of the 21st century and claim that education should be guided by scientific principles rather than by intuition and professional wisdom only (or, worst, by tradition). This is because sciences that are capable of shedding light into learning process have dramatically advanced during the last half‐century and, still more meaningfully, during the last 20 years. The time has come for a new science of learning to rise, which is structured around cognitive and neuroscience, investigates topics that stem from educational problems, and rests on rigorous forms of in‐laboratory and in‐vivo evaluation.     

Building Bridges Between Neuroscience,Cognition and Education With Predictive Modeling

As the field of Mind, Brain, and Education seeks new ways to credibly bridge the gap between neuroscience, the cognitive sciences, and education, various connections are being developed and tested. This article presents a framework and offers examples of one approach, predictive modeling within a virtual educational system that can include representations from the neural level to the policy level. Researchers could calibrate, test, and question the model, potentially providing quicker, more efficient, and more responsible ways of making advances in the developing educational field. Virtual investigations using models with this sort of capability can supplement the valuable information derived from carrying out policy and instructional experiments in real educational contexts.     

关于建构知识科学的问题

本文以波普的“三个世界”理论为基础，肯定客观知识世界的存在。随着知识世界的发展，我们有了自然、社会、思维（主观精神世界）和知识四个“世界”。目前，我们相应地有自然科学、社会科学和思维科学（包括心理学）分别对前三个世界加以研究，但惟一没有跟知识世界相对应的科学领域。因此，应当建立知识科学。在今天，知识的作用越来越大，成为推动社会发展的首要而最大的动力。这也要求对知识本身进行更加深入而全面的研究。于是，一门专门研究知识发展及其价值问题的科学－－知识科学的产生将是时代的必然要求。知识科学是一个大的门类，它同自然科学、社会科学、思维科学等属于同一序列的科学研究领域。它应当包括知识哲学、具体知识学科以及知识运用等三个基本层次，划分为许多具体学科。     

对程序概念的哲学探讨

随着现代科学的发展以及人文社会科学对自然科学成果的吸收，程序已不仅是自然科学的基本概念，也成为人文社会科学领域的重要概念，且在现实生活、理论研究中广泛运用。但在人文社科领域对程序概念的研究很少上升到哲学层面，因此，对程序概念的含义进行探讨，对其涵盖自然科学领域与社会科学领域的“程序一般”进行深入研究，便很有必要。     

Life Tables – A Plea for their Use

This article highlights the important contribution that life tables can make to teaching in the social sciences and in engineering subjects, especially when comparative work is carried out. The article ends by illustrating how to form the life table and generate appropriate graphs using a spreadsheet.     

现代科学中基于个体的新思维

在现代科学中,一种新的基于个体的思维方式受到越来越多的科学家的关注。与自上而下的分析范式以及强调中心因素主导作用的中心控制范式不同,基于个体的思维范式主要采取的是自下而上的综合策略:即从个体出发,重点模拟简单的单位,而不是复杂的单位;主要运用局部控制,而不是全局控制;让行为从底层突现出来,而不是自上而下地做出规定。基于个体的思维范式已经成为现代科学,特别是复杂性科学探索的一种新思维。     

Developing Students’ Reflections on the Function and Status of Mathematical Modeling in Different Scientific Practices: History as a Provider of Cases

Mathematical models and mathematical modeling play different roles in the different areas and problems in which they are used. The function and status of mathematical modeling and models in the different areas depend on the scientific practice as well as the underlying philosophical and theoretical position held by the modeler(s) and the practitioners in the extra-mathematical domain. For students to experience the significance of different scientific practices and cultures for the function and status of mathematical modeling in other sciences, students need to be placed in didactical situations where such differences are exposed and made into explicit objects of their reflections. It can be difficult to create such situations in the teaching of contemporary science in which modeling is part of the culture. In this paper we show how history can serve as a means for students to be engaged in situations in which they can experience and be challenged to reflect upon and criticize, the use of modeling and the significance of the context for the function and status of modeling and models in scientific practices. We present Nicolas Rashevsky’s model of cell division from the 1930s together with a discussion of disagreement between him and some biologists as one such episode from the past. We illustrate how a group of science students at Roskilde University, through their work with this historical case, experienced that different scientific cultures have different opinions of the value of a model as an instrument for gaining scientific knowledge; that the explanatory power of a model is linked not only to the context of its use, but also to the underlying philosophical and theoretical position held by the modeler(s) and the scientists discussing the model and its use. The episode’s potential to challenge students to reflect upon and criticize the modeling process and the function of models in an extra mathematical domain is discussed with respect to the notions of internal and external reflections.     

Do psychology researchers tell it like it is? A microgenetic analysis of research strategies and self-report accuracy along a continuum of expertise

Acquiring research skills is considered to be a highly challenging aspect of developing expertise in the social sciences. Because instruction and mentoring in these skills are typically grounded in the self-report of researchers, difficulties in learning the material may be due to the content and accuracy of these explanations. Using a mixed-method, microgenetic design, this study examines the explanations of problem-solving processes by researchers along a continuum of expertise during simulated experiment design and subsequent data analysis. Findings indicate that participants’ self-explanations are largely inaccurate. Further, frequency of inaccurate statements is positively associated with the frequency of abstract cognitive processes, such as mental modeling and situation assessment. Implications of these findings for instruction and future research directions are discussed.     

论沃勒斯坦的社会科学观

沃勒斯坦社会科学观的发展经历了形成、确立和完善三个阶段,在重现社会科学的演变历程及知识体系现状后,沃勒斯坦表达了重塑社会科学,建立整体社会科学知识观的愿望,在世界体系分析和历史社会科学的方法论支撑下,将“否思”批判和道德选择作为社会科学的价值追求。沃勒斯坦的社会科学观引起了社会科学内部的争论、反思,启发社会科学研究者要正视社会科学知识的动态建构,更新社会科学的方法论范式,重塑高等教育学术体系,秉持研究者的责任自觉,辩证对待全球化与民族文化。