Social network analysis (SNA) has been used in many educational studies in the past decade, but what these studies have in common is that the populations in question in most cases are defined and known to the researchers studying the networks. Snowball sampling is an SNA methodology most often used to study hidden populations, for example, groups of homosexual people, drug users or people with sexually transmitted diseases. By use of a snowball sampling approach, this study mapped municipal social networks of educational actors, who were otherwise hidden to the researchers. Subsequently, the resulting networks were validated through interviews with central respondents and by using prior investigations of the municipalities. Our results confirmed that the majority of the designated respondents recognized the resulting sociograms and their own position in these. It was also found that not all predictions based on existing knowledge of the municipalities aligned with SNA data. However, these discrepancies could be explained by development in the municipalities in the time following previous investigations. This study shows that snowball sampling is an applicable method to use for mapping hidden populations in educational settings, but also that qualitative studies are needed in order to interpret the networks in real-life contexts.

Studies of the time development of empirical networks usually investigate late stages where lasting connections have already stabilized. Empirical data on early network history are rare but needed for a better understanding of how social network topology develops in real life. Studying students who are beginning their studies at a university with no or few prior connections to each other offers a unique opportunity to investigate the formation and early development of link patterns and community structure in social networks. During a nine week introductory physics course, first year physics students were asked to identify those with whom they communicated about problem solving in physics during the preceding week. We use these students' self reports to produce time dependent student interaction networks. We investigate these networks to elucidate possible effects of different student attributes in early network formation. Changes in the weekly number of links show that while roughly half of all links change from week to week, students also reestablish a growing number of links as they progress through their first weeks of study. Using the Infomap community detection algorithm, we show that the networks exhibit community structure, and we use non-network student attributes, such as gender and end-of-course grade to characterize communities during their formation. Specifically, we develop a segregation measure and show that students structure themselves according to gender and pre-organized sections (in which students engage in problem solving and laboratory work), but not according to end-of-coure grade. Alluvial diagrams of consecutive weeks' communities show that while student movement between groups are erratic in the beginnning of their studies, they stabilize somewhat towards the end of the course. Taken together, the analyses imply that student interaction networks stabilize quickly and that students establish collaborations based on who is immediately available to them and on observable personal characteristics.

A new methodology is proposed for qualitative discourse analysis (QDA) aimed at gaining enhanced insights into learning possibilities and indicators that arise during classroom group discussions. The constitution of this new methodology has two principle components: a discourse analysis approach that aims to identify the relationships between content and group dynamics; and a network analysis (NA) approach that uses the same data to identify meaning-related structural dynamics found in the data. The proposed methodology pairs these two components to create a supplementary iterative interchange that facilitates the attainment of greater analytic insights than are achievable by either of the two components individually. The critical aspects of the methodology are illustrated and discussed using real classroom data in ways that provide a procedural exemplar. The strengths and limitations of the proposed methodology are also discussed.

Reviews of literature often summarize research within a given field to provide succinct information about state of the art as well as gaps for future research to address. In educational research, such information includes different positions within a field; for example, theoretical positions underpinning empirical studies. Such theoretical positions may influence interpretations of research results and remain a challenge to map and present. Here, we show how to use thematic analysis and network analysis to construct maps, analyse and synthesize theoretical positions within educational research. We use early childhood inquiry-based science education (ECIBSE) literature as a case because of its historical roots in Dewey’s educational philosophy. This allows us to discuss findings in light of a well-known theoretical framework. Using our methodology, we analyse four theoretical positions for teaching and learning: (1) science should be learned through inquiry, (2) teaching should model scientific practices, (3) children should develop science-related competencies (4) child’s experience should take precedence. After discussing these positions, we turn to methodological possibilities and challenges of using thematic network analysis for literature reviews. R-code, data and instructions for reproducing our results are available at: https://github.com/jbruun/thematicDiscourseNetworkAnalysis/.

One of the major difficulties in learning physics is for students to develop a conceptual understanding of the core concepts of physics. Many authors argue that students’ conceptions of basic physical phenomena are rooted in basic schemas, originating in fundamental kinaesthetic experiences of being. We argue that this idea should be utilized in physics instruction, that kinaesthetic activities will provide useful entry point for students’ acquisition of the basic conceptions of physics, and that they can overcome the phenomenological gap between experiential and conceptual understanding. We discuss the nature of image schemas and focus particularly on one: effort-resistance-flow. This schema is fundamental not only in our everyday experience, but also in most of school physics. We show how enactment of a particular kinaesthetic model can support student understanding and intuition with respect to central physics concepts, and describe and explain the design of lesson with based on the model.