Connecting, interacting and supporting:Social capital, peer network and cognitive perspectives on small group teaching

No mass lectures anymore but small-group teaching; this is the current trend in higher education and the way universities hope to attract future students. Students get to know each other easily when they collaborate in small groups. The question arises whether dividing a cohort of students in small groups is sufficient for helping all students to build relationships with peers potentially contributing to their achievement? Longitudinal survey- and social network data revealed that when students believed that they could accomplish their studies well, they performed better. Interaction with peers and with teachers enhanced this ‘I-can-do belief’. It was found that within learning communities, as one of the investigated forms of small group teaching, cohesive groups were formed. Further analysis showed that when small groups are formed, the risk emerged of achievement segregation. The better-achieving student created more access to help resources and formed relations in particular with better-achieving peers who potentially contribute to their success. But what about the student who has difficulties with passing the exams? These students become friends, ask help and prefer to collaborate with similar lower-achieving peers. Since the ambition of the Dutch government is that the potential of all students should be reached in higher education (cf. strategic agenda higher education 2015-2025), more research needs to be done about how to organize small group teaching to benefit all students

The Role of Prosocial Attitudes and Academic Achievement in Peer Networks in Higher Education

After the transition to university, students need to build a new peer network, which helps them to adapt to university life. This study investigated to what extent students’ prosocial attitudes and academic achievement facilitate the embeddedness in friendship and help-seeking networks, while taking structural network characteristics into account. Participants were 95 first-year bachelor’s degree students and were part of learning communities consisting of 12 students at a university in the Netherlands. Measures included student-reports of prosocial attitudes, peer nominations of friendship and help-seeking networks, and officially registered grades (GPA). Longitudinal social network analysis, stochastic actor based modeling with the package RSiena, revealed that both students’ own prosocial attitudes and achievement played a role in their friendship formation, whereas only students’ own achievement made the formation of their help-seeking relationships more likely. When students were friends, it was more likely that they approached each other for help and vice versa. Similarity in achievement level contributed to relationship formation in friendship and help seeking networks. Overall, the results underscore the importance of both student’ prosocial attitudes and achievement for their social adjustment (i.e., making friends) and only achievement for their academic adjustment (i.e., seeking help) during the first-year of university within the context of small-scale teaching

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

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/

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/

Data collection for mixed method approaches in social network analysis

The aim of this chapter is to support researchers with their data collection and to provide an overview of how mixed method approaches can be applied in social network analysis. This topic is seldom discussed in the literature, although the selection and combination of formats and instruments for data collection is demanding and complex. This chapter provides an overview how different methods can be combined and integrated to address relational research questions from both qualitative and quantitative perspectives. The pros and cons of combining different methods are illustrated with recent examples of social network research. Finally, a guideline is suggested to support researchers with their data collection in a mixed methods social networks research design.<br/

What makes a nurse today?:A debate on the nursing professional identity and its need for change

In 2020, due to the Nightingale year and COVID-19 crisis, nursing is in the public eye more than ever. Nurses often are being seen as compassionate helpers. The public image of nursing, however, also consists of stereotypes such as nursing being a 'doing' profession and care being a 'female' characteristic. Next to that, nursing is associated with images from the past, such as 'the lady with the lamp'. Therefore, in the public eye at least, the nursing identity seems a simple and straightforward enough construct, but nothing less is true. Looking at what a professional identity consists of, historic and social developments influence a group identity as a construct. In addition, individual, professional and contemporary societal moralities, including stereotypes, play its role. Nurses themselves reinforce stereotypes in order to fit into what is expected, even when they believe professional behaviour encompasses other features. They may do so individually as well as in a group context. But nursing actually seems to be better off when viewed upon as a diverse, autonomous profession. Moral values such as compassion motivate nurses to enter the profession. Research shows that if such values are addressed in daily practice, nursing could perhaps be saved from nurses leaving the profession because of feeling unfulfilled. Another aspect concerns the huge nursing body of knowledge. If seen as the ground on which nursing behaviour is standing, it would contribute to a different image of nursing than simplified stereotypes, which do not acknowledge the complex nature of the profession. This paper challenges the idea that the nursing identity is unchangeable and the notion that 'a nurse will always be a nurse'. By doing so, the paper contributes to a debate on the supposed 'true' nature of the nursing identity and opens a discussion on the need for it to change