Epistemic emotions (surprise, curiosity, enjoyment, confusion, anxiety, frustration and boredom) have an object focus on knowledge or knowledge construction and are thus hypothesized to affect learning outcomes. In the context of upper secondary school science, the present study clarifies this relation by examining the students’ pre- and posttest performance (n = 148 students) and their experiences of situational epistemic emotions (n = 1801 experience sampling method observations). As expected, epistemic emotions correlated with both pre- and posttest performance: curiosity and enjoyment correlated positively, and frustration and boredom correlated negatively with the performance. However, based on structural equation modeling, after controlling for the pretest performance, only boredom was found to have a significant negative effect on posttest performance. The findings underline the complexity of the interplay between emotions and learning. Thus, the state versus trait nature of epistemic emotions, and the implications for research and practice are being discussed.

This paper presents two novel network methods developed for education research. These methods were used to investigate online discussions and the structure of students’ background knowledge in a blended university course for pre-service teachers (n = 11). Consequently, these measures were used for correlation analysis. The social network analysis of the online discussions was based on network roles defined using triadic motifs instead of more commonly used centrality measures. The network analysis of the background knowledge is based on the Katz centrality measure and Jaccard similarity. The results reveal that both measures have characteristic features that are typical for each student. These features, however, are not correlated when student participation is controlled for. The results show that the structure and extension of a student’s background knowledge does not explain their activity and role in online discussions. The limitations and implications of the developed methods and results are discussed.

Lately, new materialism has been proposed as a theoretical framework to better understand material-dialogic relationships in learning, and concurrently network analysis has emerged as a method in science education research. This paper explores  how to include materiality in network analysis  and reports the development of a method to construct network data from video. The approaches, 1) information flow, 2) material semantic and 3) material engagement, were identified based on the literature on network analysis and new materialism in science education. The method was applied and further improved with a video segment from an upper secondary school physics lesson.  The example networks from the video segment show that network analysis is a potential research method within the materialist framework and that the method allows studies into the material and dialogic relationships that emerge when students are engaged in investigations in school.