What does it mean to design for and teach towards the rightful presence of every student across science learning environments? This talk focuses on the Rightful Presence science teaching and learning framework with the three interconnected tenets: 1) The right to reauthor rights; 2) Making in/justices visible; and 3) Collective disruption of guest/host relationalities through allied political struggles.

Using case studies in middle school science classrooms serving students from low-income backgrounds and also in an informal science with recently resettled refugee youth, this talk discusses how teachers, youth, and parents take up particular roles in curricular design and enactment, as they collectively work towards a more rightful presence of youths’ and families’ lives, within the discipline of middle school science and engineering education. One case features an engineering for sustainable communities curriculum, the second in a classroom where students were learning about the physiological effects of stress in the body in connection with social activity in the environment, and the last case illustrates how recently resettled refugee youth created a large piece of electric art to beautify their refugee center.

In tandem, the talk will also discuss the considerations relevant to research-practice-partnerships for science education researchers engaging in this work, the new insights gleaned, the tensions inherent in such work and implications for further research.

Science classrooms are increasingly expected to welcome all students, including the full range of perspectives, backgrounds, and ways of thinking they bring. Yet many learning environments still present science as a collection of settled facts and linear explanations, leaving little room for the kind of open-ended engagement that supports inclusion of all students.

In my talk I will present a line of research in science education that explores how inquiry learning, collaborative activities, and carefully designed scaffolds can support students in engaging with science, but also with complex real-world problems. I will present work from secondary education showing that students benefit from tools and structures that make their thinking visible and help them learn from each other's reasoning. And share some more recent studies in primary education, where students explore socio-scientific issues that involve multiple perspectives and genuine uncertainty.

Across these studies, a consistent finding is that diversity in student perspectives can be a productive resource rather than a complication. When learning environments are designed to support students in working with differences rather than around them, both understanding and engagement improve. The talk reflects on what this means for designing inclusive science classrooms and invites participants to consider how inquiry learning, working with authentic problems and scaffolding can give every student opportunities to engage with and learn science content.

Hannie Gijlers is Associate Professor in Instructional Technology at the University of Twente and programme director of the Master of Educational Science and Technology. She studied Educational Science at the University of Groningen and completed her PhD at the University of Twente, where she investigated how computer-supported learning environments can effectively support collaborative inquiry learning in science education. Her research focuses on the design of learning environments that help students engage in inquiry, with particular attention to the role of scaffolds, collaborative tools, and shared representations. Over the years her work has broadened in scope, from secondary to primary education and from STEM topics to real-world issues that involve multiple perspectives and societal questions. She has published in the field of technology enhanced learning and the learning sciences and contributed to the design of several ICT based learning environments.

Quantitative science education research routinely inverts its own priorities: statistical procedures come first, and scientific reasoning is retrofitted to whatever the p-values allow. The proper order should be restored, or so I argue herein. Drawing on McElreath's (2020) dictum of "science before statistics," I present a seven-step Bayesian data analytic workflow for education research, with particular emphasis on its first (and most frequently skipped) step: specifying the hypothesized causal structure of a phenomenon, via directed acyclic graphs, before any model is fit. Said step surfaces the assumptions every regression already makes, identifies the covariates required for unbiased estimation, and reveals which questions the data cannot answer at all.

I then demonstrate, using data from published studies, how blind adherence to statistical significance can mislead: 1) effects that vanish under principled confounding adjustment, 2) findings driven by unmodeled clustering, and 3) inflated estimates produced by threshold-based filtering. The aim is not to replace one mechanical ritual with another, however. Rather, I invite researchers to slow down, reflect, and reason critically about their findings. If science education is truly to serve all learners, the inferences guiding policy and practice must rest on scientific reasoning, vice statistical ceremony.