Co-evolution models of longitudinally measured interactions

Longitudinal social networks analysis enables us to investigate interactions among group members over time. It allows to model individuals’ positions within groups and personal characteristics or attributes simultaneously and how they change over time. One of the main fundamental mechanisms of relationship formation is homophily, which means that individuals are intended to connect to someone else who is similar to them in terms of background characteristics or behavior. The question arises whether individuals select each other because they are similar or whether they do become more similar over time. Longitudinal network analysis provides the possibility to disentangle selection effects from influence effects, that is, what effects can be explained through individuals selecting specific other group members versus how individuals’ behaviors or characteristics are influenced by the interaction with others within a certain context and at a certain moment. This chapter provides a guideline how to conduct longitudinal social network analysis to analyze group interactions, including the basics of stochastic actor-based modeling.<br/

Social Network Analysis as Mixed Analysis

Social network analysis (SNA) has become an important theoretical and methodological framework to investigate research questions in both the social and natural sciences. In this chapter, the authors discuss the foundations of social network analysis as mixed analysis. Onwuegbuzie and Hitchcock highlighted the potential to integrate qualitative and quantitative strands of network research, and described the method as quantitative-dominant crossover mixed analysis. As noted by Hollstein, qualitative data collection and analysis can facilitate social network analysis because qualitative data can "explicate the problem of agency, linkages between network structure and network actors, as well as questions relating to the constitution and dynamics of social networks". More information about the historical development of social network analysis can be found in Freeman. Social network analysis appears a useful method to investigate the contagion among people.<br/

The dynamics of social networks:Towards a better understanding of selection and influence mechanisms in social capital building

This chapter discusses how longitudinal network analysis can be useful for theory development, especially social capital theory. Established social capital theories refer to the access and use of resources (e.g., information, knowledge) in the network. Various resources enable individuals to achieve their individual goals, such as passing exams and obtaining a job. A longitudinal social network approach provides a better understanding of how networks change over time and how the underlying selection and influence mechanisms contribute to social capital formation and, hence, to performance or attitude changes. Selection and social influence are crucial social network mechanisms, but these mechanisms are not explicitly addressed in social capital theory. The longitudinal social network approach, stochastic actor-oriented modelling (SAOM), enables us to disentangle selection from influence. This is illustrated by students’ social capital building in peer networks in higher education. Higher education students establish social capital when they interact with their peers within the learning context. They select each other when they need academic help (selection) or the academic help relationships may impact students’ grades (social influence). Overall, SAOM can provide a better understanding of social network dynamics and advance social theories, such as the social capital theory.</p

Entornos de aprendizaje no tecnológicos en un mundo tecnológico: la inversión viene a ayudar

We live in a world permeated by digital technologies. Still, however, this digitization is not always reflected in the learning environments of higher education institutions, which raises questions about the adequacy of the instructional outcomes. In this paper, I maintain that the concept of the inverted or flipped classroom may be a fruitful path to including learning “hands-on” with technology even in learning environments absent of any technological resources. The rationale for this proposition is that flipped elements transfer the demand for technology from the teaching environment to the student. I report on a design-based research project to put this claim to a first test. The qualitative and quantitative data collected all support the idea that flipped classroom elements may help overcome differences in terms of availability of technology in different learning environments. The implications for universities and higher education teachers are discussed.Vivimos en un mundo dominado por las tecnologías digitales. Sin embargo, esta digitalización todavía no se refleja siempre en los entornos de aprendizaje de los centros de enseñanza superior, lo cual plantea dudas acerca de la idoneidad de los resultados de la docencia. En este artículo, yo sostengo que el concepto del aula invertida puede representar una vía fructífera para incluir el aprendizaje “práctico” con tecnología incluso en entornos de aprendizaje carentes de cualquier recurso tecnológico. La fundamentación en la que se apoya esta propuesta es que los elementos invertidos trasladan la demanda de tecnología desde el entorno docente hasta el estudiante. Doy cuenta de un proyecto de investigación basado en el diseño con el fin de someter esta afirmación a una primera prueba. Todos los datos cualitativos y cuantitativos recogidos apoyan la idea de que el aula invertida puede ayudar a superar las diferencias por lo que respecta a la disponibilidad de tecnología en distintos entornos de aprendizaje. Se abordan las implicaciones para las universidades y los docentes de enseñanza superior

Newly Qualified Teachers' Well-Being During the COVID-19 Pandemic: Testing a Social Support Intervention Through Design-Based Research.

Around the world, newly qualified teachers are leaving the profession after only a short time working at school. This not only has a negative effect on the capacities of the respective education systems, but also for the teachers themselves, as it often due to factors such as stress and burnout that leads to this decision. The COVID-19 pandemic has exacerbated this situation by adding to the teachers' workload, uncertainty, and stress. Previous research has investigated strategies that may help teachers improve their well-being and, among other factors, found social support to be an important condition. In this mixed methods design-based research study, we developed a design to enhance social support among newly qualified teachers in their first months working at school. Our quantitative and qualitative results show that the design has positive effects on many aspects of teachers' well-being in the intervention group both longitudinally (before and after the intervention) and when compared to a comparison group. The findings are being discussed considering the recent changes in the working conditions of teachers as imposed by the COVID-19 pandemic

The potential of mixed-method social network analysis for studying interaction between agency and structure in education

This article discusses the potential of mixed-method social network analysis (MMSNA) as a methodology for designing and conducting studies that address questions of interplay between human agency and social structures in educational settings. First, we discuss a rationale for using MMSNA referring to the theoretical calls for better understanding the role of agency in network structures. Next, we discuss examples of studies that illustrate how MMSNA has been applied to investigate (a) the role of agency in social network formation and (b) how social networks facilitate actors' agency in educational processes. Finally, we outline a guide for how to use MMSNA and consider its potential for future studies of interactions between agency and structures in educational settings

Mixed Methods Social Network Analysis : Theories and Methodologies in Learning and Education

In this volume, mixed methods social network analysis (MMSNA) is mainly discussed as a powerful framework for generating scientific insight. In this chapter, we aim to add a different facet to the discussion by emphasizing MMSNA’s usefulness to inform organizational decision-making and consultancy. Specifically, we give three examples of consultative approaches using MMSNA that deal with themes of leadership, organizational change, and innovation. We discuss the possibilities and limitations of this method of consulting.</p

Massive Open Online Courses as enablers of service learning

MOOCs offer the possibility of flexible and independent learning processes. Using MOOCs at universities is often seen in the context of blended learning and inverted learning. But the use of MOOCs in other didactic formats, such as service-learning, is less common. Service-learning describes the combination of social engagement with the training of students, i.e., the teaching of technical, methodological and social skills. The aim of this article is to reflect on the use of MOOCs in service-learning and to provide suggestions for further researc

Digital Transformation in Higher Education – New Cohorts, New Requirements?

Digital transformation refers to changes that digital technologies cause and that influence various aspects of human life. Previous researchers mainly focused on the impact of the digital transformation in the context of commercial organisations and business processes. In this study, we aim to examine how digital transformation affects universities and students. We examine differences and changes in the usage of collaboration and communication platforms between different groups of members at the university and within the university lifecycle. To gain new insights, a qualitative case study with semi-structured interviews was conducted. One of the main results shows that Bachelor and Master students prefer the usage of social network sites for collaboration and communication while Ph.D. students and employees do not. Even though an increasing number of modern platforms for direct communication is offered, the results show that the communication between the groups of students and employees still takes place via email