Background: Improving scientific reasoning and argumentation are central aims of science education. Because of their complex nature, self-regulation is important for successful scientific reasoning. This study provides a first attempt to investigate how scientific reasoning and self-regulation processes conjointly impact argumentation quality. Methods: In a study with university students (N = 30), we used fine-grained process data of scientific reasoning and self-regulation during inquiry learning to investigate how the co-occurrences between scientific reasoning and self-regulation processes are associated with argumentation quality. Findings: When modeling the co-occurrence of scientific reasoning and self-regulation processes using epistemic network analysis, differences between students showing either high or low argumentation quality become apparent. Students who showed high argumentation quality engaged in different scientific reasoning processes together more often than students with low argumentation quality, and they made more connections between self-regulation and scientific reasoning processes. Contribution: These findings offer educational implications for teaching scientific reasoning. Integrating self-regulation and scientific reasoning during instruction could be beneficial for improving scientific reasoning and argumentation.

Guided inquiry learning is an effective method for learning about scientific concepts. The present study investigated the effects of combining video modeling (VM) examples and metacognitive prompts on university students’ (N = 127) scientific reasoning and self-regulation during inquiry learning. We compared the effects of watching VM examples combined with prompts (VMP) to watching VM examples only, and to unguided inquiry (control) in a training and a transfer task. Dependent variables were scientific reasoning ability, hypothesis and argumentation quality, and scientific reasoning and self-regulation processes. Participants in the VMP and VM conditions had higher hypothesis and argumentation quality in the training task and higher hypothesis quality in the transfer task compared to the control group. There was no added benefit of the prompts. Screen captures and think aloud protocols during the two tasks served to obtain insights into students’ scientific reasoning and self-regulation processes. Epistemic network analysis (ENA) and process mining were used to model the co-occurrence and sequences of these processes. The ENA identified stronger co-occurrences between scientific reasoning and self-regulation processes in the two VM conditions compared to the control condition. Process mining revealed that in the VM conditions these processes occurred in unique sequences and that self-regulation processes had many self-loops. Our findings show that video modeling examples are a promising instructional method for supporting inquiry learning on both the process and the learning outcomes level.

Teachers turn to many sources for support and professional learning, including social media-based communities that have shown promise to help teachers access resources and facilitate productive exchanges. Although such online communities show promise, questions about their quality for providing a suitable learning environment remain insufficiently answered. This study examines how teachers’ engagement on Twitter may adhere to characteristics of high-quality professional development activities. In that, we employ advanced conversational analysis techniques that extend the primarily descriptive methods used in prior research. Specifically, we collected data from three Twitter communities related to Advanced Placement Biology (N = 2,040 tweets, N = 93 teachers). Qualitive two-cycle content analyses derived both tweet content and sentiment. Using epistemic network analyses, we examined the collaborative structures to examine how participation patterns can identify characteristics of high-quality teacher professional development. Results indicate that some teachers use Twitter with a content focus and coherent to their individual contexts and prior knowledge. Notably, differences in collaboration and participation patterns by teachers’ overall activity level hint at the existence of an online community of practice. More active teachers communicated more about how their individual contexts relate to instruction, whereas less active teachers exhibited more targeted engagement, for instance, related to sharing teaching resources and organizing learning opportunities. Overall, this study illustrates how Twitter may provide a meaningful learning