Henderien Steenbeek

An empirical validation of a dynamic systems model of interaction: do children of different sociometric statuses differ in their dyadic play?

Studying short-term dynamic processes and change mechanisms in interaction yields important knowledge that contributes to understanding long-term social development of children. In order to get a grip on this short-term dynamics of interaction processes, the authors made a dynamic systems model of dyadic interaction of children during one play session. The control parameters of the model relate to childrens goal-directedness, concerns, emotional appraisals, social power, and social competence. Three groups of dyads of different sociometric statuses are represented by specific control parameter values. The models order parameters consist of childrens emotional expressions and other- versus self-directed actions. This article describes the empirical validation of the model and the methods needed for such validation. It focuses on the models predictions of averages and distributions of the major variables, of the occurrence of attractors and power law distributions, and on the models sensitivity. Overall, the model fits the empirical data well. In the discussion, we reflect on the developmental and methodological implications for explaining social interaction on the short-term as well as on the long-term time scale. In addition, implications for intervention and assessment are presented, in particular relating to the problem of rejection.

A dynamic systems model of dyadic interaction during play of two children

This article describes the construction and validation of a dynamic systems model of dyadic interaction, applied to a dyadic play situation between children of different sociometric statuses. The introduction addresses research on social interaction, principles of dynamic systems, and of simulation. We proceed with a discussion of our interaction model, which is inspired by the theory of Frijda (1986). The model describes the continuous interaction between concerns, drives, emotional appraisals, emotional expressions and behaviour of children during each moment of the childrens play session. The empirical validation of the model focuses on group differences (averages and distributions of three types of dyads), which are based on a time-sampling of videotaped play interactions. The predictions of the model, which differ from those made on the basis of the current literature, are corroborated by our empirical data. The conclusion discusses the use of models in the explanation of social behaviour and development.

Using the dynamics of a person context system to describe children’s understanding of air pressure

© 2012 Van der Steen et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Using the Dynamics of a Person-Context System to Describe Children’s Understanding of Air Pressure

A Dynamic Network Model to Explain the Development of Excellent Human Performance

Across different domains, from sports to science, some individuals accomplish excellent levels of performance. For over 150 years, researchers have debated the roles of specific nature and nurture components to develop excellence. In this article, we argue that the key to excellence does not reside in specific underlying components, but rather in the ongoing interactions among the components. We propose that excellence emerges out of dynamic networks consisting of idiosyncratic mixtures of interacting components such as genetic endowment, motivation, practice, and coaching. Using computer simulations we demonstrate that the dynamic network model accurately predicts typical properties of excellence reported in the literature, such as the idiosyncratic developmental trajectories leading to excellence and the highly skewed distributions of productivity present in virtually any achievement domain. Based on this novel theoretical perspective on excellent human performance, this article concludes by suggesting policy implications and directions for future research.

‘Do you still like to play with him?' Variability and the dynamic nature of children’s sociometric ratings.

There is a wealth of research on the relation of sociometric status and other psychological constructs and behavioural properties. Surprisingly, few studies focus on the stability of sociometric status. Existing studies focus on long-term stability, on short-term variability as a form of measurement error, and are limited to ratings that children receive from others. The present article argues that stability and variability are inextricably bound together. We investigate repeated short-term stability and variability in 6- to 8-year-old children’s given and received ratings. In addition to stability, children show a characteristic pattern of fluctuation and variability. In their patterns of giving ratings to others, children have a certain relatively constant ‘style’. The focus of the present article lies on the presentation of new methodological approaches to the study of variability and stability of sociometric ratings in children. (Netherlands Journal of Psychology 63, 86-101.)

‘Looking at’ educational interventions: surplus value of a complex dynamic systems approach to study the effectiveness of a science and technology educational intervention

To fully understand the effect of science education interventions on students’ performance, insight is needed in the properties of teaching-learning processes in individual teacher–student pairs. The assessment of students’ performance during group-based interventions in inquiry learning has been given much attention in the recent years, while the influences on individual student’s learning gains over time have been relatively neglected.

Networks as complex dynamic systems: Applications to clinical and developmental psychology and psychopathology

Cramer et al.s article is an example of the fruitful application of complex dynamic systems theory. We extend their approach with examples from our own work on development and developmental psychopathology and address three issues: (1) the level of aggregation of the network, (2) the required research methodology, and (3) the clinical and educational application of dynamic network thinking.

Attractor States in Teaching and Learning Processes: A Study of Out-of-School Science Education

In order for out-of-school science activities that take place during school hours but outside the school context to be successful, instructors must have sufficient pedagogical content knowledge (PCK) to guarantee high-quality teaching and learning. We argue that PCK is a quality of the instructor-pupil system that is constructed in real-time interaction. When PCK is evident in real-time interaction, we define it as Expressed Pedagogical Content Knowledge (EPCK). The aim of this study is to empirically explore whether EPCK shows a systematic pattern of variation, and if so whether the pattern occurs in recurrent and temporary stable attractor states as predicted in the complex dynamic systems theory. This study concerned nine out-of-school activities in which pupils of upper primary school classes participated. A multivariate coding scheme was used to capture EPCK in real time. A principal component analysis of the time series of all the variables reduced the number of components. A cluster revealed general descriptions of the components across all cases. Cluster analyses of individual cases divided the time series into sequences, revealing High-, Low-, and Non-EPCK states. High-EPCK attractor states emerged at particular moments during activities, rather than being present all the time. Such High-EPCK attractor states were only found in a few cases, namely those where the pupils were prepared for the visit and the instructors were trained.

Assessing young children´s learning and behavior in the classroom : A complexity approach

This chapter provides a complex dynamic systems view on curriculum-based learning in early education. We first discuss the general notions of curriculum, assessment, skills, and learning in early education and give a short overview of curriculum-based assessment in early childhood education (ECE) in the Netherlands. We conclude that for assessment to be usable in an educational context, an understanding of the process of learning is crucial. We then present the theory of complex dynamic systems as a general framework for defining learning, skills, and assessment. A complex dynamic system is defined as any network of interacting components (e.g., cognitive, emotional, and behavioral components in an agent, such as a learning child, and components of the teaching process in a teacher). As a consequence of their interactions in a network of dynamic connections, complex dynamic systems undergo typical change in the form of self-organization, variability, temporal stability, and so forth. These patterns of change characterize the processes of learning and associated teaching in young children and educators. In spite of the abstract nature of this theoretical framework, we show that its basic concepts are intuitively appealing in educational contexts and provide concrete and usable ways for assessment in early childhood education. To this end, we present a taxonomy of assessment forms based on complexity thinking and provide two practical, complexity-based examples of the assessment of learning processes in early childhood education: a web-based program for assessing students’ learning and behavioral goals and a video feedback coaching program for teachers giving science lessons for young children. We conclude with practical recommendations for early childhood curricula and for early childhood assessment.

A true love story. Young people's romantic and sexual development in the context of everyday life

Wieke Dalenberg. A true love story. Young people’s romantic and sexual development in the context of everyday life. Groningen: University of Groningen, 2016, 147 p., ISBN 978 94 6332 100 6