The field of education continues to evolve in response to rapid technological developments, prompting many countries to integrate innovative instructional approaches into their science curricula. However, further evidence is needed regarding the effective implementation of these approaches and their potential to improve students’ learning outcomes. Educational robotics and design thinking have emerged as promising instructional approaches for supporting science learning and fostering students’ scientific process skills and STEM (science, technology, engineering, and mathematics) attitudes.Therefore, this study investigated the effects of educational robotics applications and design thinking activities on the scientific process skills and STEM attitudes of seventh-grade secondary school students. A quasi-experimental pre-test–post-test control group design was employed with 134 seventh-grade students from two secondary schools. Students in the first experimental group participated in educational robotics activities, those in the second experimental group engaged in design thinking activities, and the control group received instruction based on the regular science curriculum. Data were collected using the Scientific Process Skills Test and the STEM Attitude Scale and analyzed using paired-samples t-tests, multivariate analysis of variance (MANOVA), and analysis of covariance (ANCOVA). The findings showed that both educational robotics and design thinking activities significantly improved students’ scientific process skills and STEM attitudes compared with the regular curriculum. Although both instructional approaches produced positive outcomes, educational robotics demonstrated a stronger effect on students’ scientific process skills. These findings provide empirical evidence supporting the integration of educational robotics and design thinking into secondary school science instruction to promote students’ scientific process skills and positive attitudes toward STEM.
Multilingual learners are the fastest-growing group in schools, yet science classrooms often treat their linguistic and cultural diversity as a challenge rather than a resource. This qualitative case study examines how three newcomer multilingual learners navigated science learning when project-based learning instruction shifted to the home during COVID-19. We explored how students engaged with science through home materials, family collaboration, and multimodal means of expression. Drawing on figured worlds, we analyzed their interactions and artifacts, across three different online discourse structures. Findings show that students figured themselves in school science in various way, including applying science ideas from school to created materials in the home, building connections to science through family support and with culturally important objects, ultimately blending home and school to create hybrid spaces where they might belong.
Although virtual laboratories (VLs) have been widely promoted as tools for expanding inquiry-based science learning, their potential for supporting dialogic inquiry in limited resource areas remains underexplored. This study investigates how the affordances of VLs are enacted through dialogic inquiry in Brazilian upper-secondary physics classrooms. Drawing on a practitioner-led design-based research project with local teachers, the paper reports an analysis of VL-supported physics lessons in small-town municipal public schools in Rio Grande do Sul, Brazil. Guided by the Activity-Centred Analysis and Design (ACAD) framework, the analysis integrates three dimensions of classroom activity: epistemic task phases, dialogic interaction, and digital tool use. Epistemic Network Analysis and Lag Sequential Analysis were used to examine structural relationships and temporal pathways, complemented by evidence from teacher reflections and student interviews. The findings show that VL use shifted classroom discourse towards experimentation-centred and evidence-oriented inquiry. Manipulation-based actions were primarily associated with procedural coordination, whereas visualisation-focused affordances were more closely linked to observation, questioning, reasoning, and collaborative explanation. Observation-based exploratory talk emerged as a key mediating practice between simulation interaction and conceptual dialogue. The study contributes to understanding how technological affordances, inquiry pedagogy, and classroom dialogue interact in technology-mediated science learning, and how educators can capitalise on this to support knowledge construction.
Professional development for Science, Technology, Engineering, and Mathematics (STEM) teachers presents distinct challenges, as it involves integrating knowledge, skills, and practices across multiple disciplines. The effectiveness of STEM teacher professional development (STEM TPD) can be observed in teachers’ transformative practices in the classroom. This raises the question: How does engagement with STEM TPD transform disciplinary teachers into cross-disciplinary educators? This paper reports on an international professional development programme, resulting from a collaboration between two culturally diverse economies – STEM teacher educators (hereafter referred to as consultants) from Singapore and teacher participants from India, aimed at developing teachers to teach using integrated STEM. We position teachers as boundary crossers within the epistemic infrastructure (EI) of the programme, where differences lie in social, cultural, and structural identities that shape learning opportunities. We examine the complexities and role of an international TPD as a catalyst for teacher learning and transformation into integrated STEM educators. Prescriptive and emergent coding were applied to analyse lesson videos and interviews with the teachers to illuminate the affordances of EI (productive, dynamic, non-neutral, and agentic) in the programme. The findings showed that the STEM TPD, when viewed through the lens of EI, enabled two forms of boundary crossing: teacher identity and teacher collaboration. Learning occurred at the boundaries when teachers re-engaged with their experiences and partial movement across boundaries happened. Implications drawn from the study may enrich the discourse on professional development courses and offer a new perspective for designing and developing STEM teacher professional development.
Augmented reality (AR) has shown strong potential in STEM education by enabling students to visualize abstract concepts, interact with 3-D representations, and connect digital simulations with real-world experiences to enhance understanding and engagement. Despite its demonstrated benefits, little attention has been paid to how AR supports specific phases of STEM practices across the before, during, and after stages of AR activities through multimodal interactions with peers, teachers, and AR. Thus, this study was aimed at identifying how AR can be integrated into STEM design practices to scaffold students’ inquiry and reasoning. Adopting a case study approach, this research was conducted in a gifted Grade 5 classroom in Singapore, where 14 students participated in STEM lessons focused on designing and building wind turbines. Data collected from classroom videos, field notes and student artifacts were analyzed using multimodal discourse analysis. The findings revealed that AR provided procedural scaffolding by enhancing students’ spatial reasoning through 3-D observation and conceptual scaffolding by identifying key scientific concepts about wind turbines through AR-based inquiry. Moreover, before and after the AR inquiry, it was critical to provide epistemic scaffolding that promoted evidence-based reasoning through teacher questioning, peer feedback, and the claim-evidence-reasoning (CER) framework. Together, these scaffoldings formed a synergistic system that supported students’ evidence-based reasoning through AR-based inquiry. These findings highlight that AR affordances are impactful when pedagogical design and technological affordances operate synergistically to foster deep, inquiry-driven STEM learning.
Graduate teaching assistants (GTAs) play an integral role in undergraduate STEM education. Investment in training initiatives equips GTAs with evidence-based teaching strategies. This study investigates chemistry GTAs’ responses to a questioning strategy, known as Stretch-It, during a training program enhanced with rehearsal in a mixed reality teaching simulator. The GTAs’ use of Stretch-It questioning in the simulator was recorded over three training sessions (before, during, and after the strategy was introduced). Additionally, the GTAs’ use of Stretch-It questioning while teaching in an undergraduate general chemistry laboratory environment was investigated through in-class observations. The results support previous literature that training in a simulator environment can increase GTA proficiency in target skills while closely modeling a real-life teaching environment. Additionally, when using Stretch-It questioning in the classroom, GTAs used the strategy as an instructional and interactive technique during whole-group and individual interactions. Thus, tasking GTAs to practice Stretch-It questioning may be helpful for professional developers when promoting interactive teaching strategies.
Science education research has expanded and diversified over the past three decades, yet comprehensive, data-driven syntheses remain scarce. This study maps 35 years of scholarship (1990–2025) across five leading journals by applying Structural Topic Modeling (STM) to 10,164 research articles. A content-prevalence STM was conducted with publication year as a covariate and selected the number of topics via coherence, exclusivity, residuals, and semantic inspection, yielding a 12-topic solution: 1- History, Philosophy, and the Nature of Science; 2- Informal and Out-of-School Learning; 3- Problem Solving and Conceptual Challenges in Physics Chemistry; 4- Curriculum Reform and Education Policy; 5- Experimental Design and Intervention Research; 6- STEM, Gender, and Motivation; 7- Conceptual Development and Misconceptions; 8- Discourse, Identity, and Classroom Interactions; 9- Modeling, Reasoning, and Scientific Explanations; 10- Teacher Education and Professional Development; 11- Assessment, Measurement, and Psychometrics; and 12- Socio-scientific Issues and Argumentation The topic labelled as History, Philosophy and Nature of Science accounts for the largest share of the corpus. In sum, the present study establishes a collective baseline, providing a foundation for the promotion of equitable, epistemically rich, and technology-enabled innovation across the domains of science education research, policy, and practice.
This study examined how a design-based learning environment supported first-year undergraduates’ (N = 31) epistemic engagement with generative AI in a science argumentation course. A two-year design-based research (DBR) study was conducted across two cycles (2024: n = 11; 2025: n = 20). Within a Toulmin-structured argumentation framework, learners interacted with ChatGPT, and their prompts were analysed as epistemic indicators across four stages of the Four-Stage Dialogic Partnership Model (4S-DPM): Epistemic Retrieval, Epistemic Scaffolding, Epistemic Critique, and Epistemic Agency. Comparative analysis revealed a significant shift toward higher-order epistemic engagement (Fisher’s exact test, p < .001): Stage 3–4 prompts increased from 12.5
While prior studies have explored scientists’ nature of science (NOS) views, the experiences of scientists with disabilities (ScWDs) remain largely unexamined. This qualitative case study investigated ScWDs’ portrayals of NOS in their scientific practices. Seven acclaimed scientists across oceanography, genetics, ecology, microbiology, and neuroscience—each self-identifying as having a disability—participated in three separate in-depth interviews, unraveling the layers of their experiences. Through a collaborative qualitative analysis, we examined how ScWDs portray NOS. Four pivotal themes emerged, illuminating ScWDs’ NOS portrayal that is uniquely associated with their disabilities: (1) Scientists with disabilities portray creative NOS and use of diverse methods in scientific investigations by leveraging their disabilities; (2) Scientists with disabilities inherently face societal stigma portraying social dimensions of NOS; (3) Scientist with disabilities are highly collaborative portraying sociocultural NOS; and (4) Scientists with disabilities gather evidence beyond their senses portraying inferential and empirical NOS. These findings disrupt and broaden the ubiquitous, stereotypical portrayals of science and scientists frequently perpetuated in science classrooms. Understanding NOS through the ScWDs’ narratives can inform strength-based design instructions that position all learners, including those with disabilities, as capable of doing science.
GenAI can be a potential tool to facilitate students’ scientific practices through automated and real-time feedback. Yet, it is unknown how to design effective GenAI feedback to facilitate students’ progress in scientific practices, particularly at a primary age. In our study, we conceptualise a design framework for providing feedback for primary students to improve their drawings of scientific observations and models. Based on the design framework, we created a Science GenAi Feedback for Drawing (Sci-GiFD) application and conducted a mixed-method case study on 65 Year 3 primary students. The application provided feedback to students’ observational drawings of evaporation of salt water across six days, as well as drawing a side-view model of evaporation. We collected various forms of data, including students’ responses to pre- and post-drawing of scientific model, process data of 404 drawings and accuracy of GenAI in scoring drawings. Quantitatively, students’ improvement of drawing of topic-specific scientific model were associated with the application based on the design framework. Qualitatively, as supported by drawing artefacts and the associated feedback from two students, some students were able to act on GenAI feedback to revise their drawings of observation and scientific models. Practical implications related to pedagogical and technical design of GenAI-powered applications will be discussed.
Digital visualisation tools hold strong potential for supporting physics teaching, yet little is known about how early‑stage preservice teachers begin to integrate such tools in authentic planning tasks. This qualitative single‑case study examines how one pair of first‑semester preservice physics teachers worked with GeoGebra while planning a lesson on motion on an inclined plane. The participants were enrolled in the introductory physics course with a didactic focus on the upper-secondary teacher programme during the autumn semesters of 2023 and 2024. Video recordings from a workshop were analysed using a theory‑informed qualitative content analysis grounded in the technology‑related TPACK domains (TCK, TPK, TPACK). The case showed how the participants demonstrated emerging Technological Content Knowledge when they interpret and modify vector representations, Technological Pedagogical Knowledge when they reason about static and dynamic visualisations for instruction, and Technological Pedagogical Content Knowledge when they structure simulations to support conceptual understanding in mechanics. The analysis also revealed moments of uncertainty, including ambiguous interpretation of unlabelled or overlapping vectors. While based on a limited empirical scope, the case provides nuanced insight into how preservice physics teachers begin to develop integrated, technology‑supported reasoning during early physics teacher education.
Preparing students to take climate actions is a new educational objective that requires understanding students’ motivations to act. In this study we assessed drivers of climate behavioral intention among high school students nearing the end of their K-12 education (n=414). We worked with [State] educators, where there has been multiyear funding to support teacher professional learning in climate education and state level integration of climate science into learning standards. Structural equation modeling indicates that climate knowledge and belief have little direct effect on climate actions–their effects are almost completely moderated by emotion and perceived responsibility. While other studies identify emotion as an important component of climate action, this study reveals the magnitude of the impact, with standardized total effects of emotion of .704 for personal future climate actions, .668 for personal current climate actions, and .589 for civic engagement. The findings provide a clear and potentially uncomfortable message for science and climate educators: the emotional landscape is more influential than traditional content mastery, yet likely less comfortable to many science educators. Future research should clarify how emotional engagement can be harnessed in climate education to promote enduring climate action and civic participation. science educators.
Although STEM education is widely acknowledged as being vital for our ongoing collective prosperity and well-being, there are still tensions regarding how it is defined and enacted in education systems. A central tension is the division between integration and separation of STEM disciplines. This paper aims to explore this tension by investigating the STEM integration perspectives of educators, parents and guardians and self-identified STEM professionals from non-metropolitan Australia. This addresses both a dearth of broader stakeholder data in non-metropolitan STEM education research and aligns with the focus on community and culture prominent in this field. An online survey with responses from 110 educators, 206 parents/ guardians and 83 STEM professionals included rating preference for separation against integration on a 0-to-10-point scale and open justifications. Mann Whitney U Tests were used to assess differences between the groups and thematic analysis was conducted on the qualitative data. Results showed that the STEM integration views of parents/ guardians did not differ significantly from either educators (p=.148) or STEM professionals (p=.167). There was a statistically significant (p<.01) difference between educators and STEM professionals, with educators holding a slight preference for integration. Although there were some differences between group perspectives and framing, qualitative data showed broad appreciation for different STEM integration approaches. More research and engagement are needed to develop shared community STEM education visions.
Artificial intelligence (AI) influences learning processes, student activities, and the organization of teaching, both in general and specifically in science education. Research indicates that the way AI will affect science learning primarily depends on how it is implemented in teaching. This study examines the ability of preservice biology teachers (PSBTs) to independently develop pedagogical AI agents (TDP-AI agents), most often in the form of chatbots, tailored to teaching goals, content, and students’ needs. This empirical study, based on a mixed-methods research approach and applying the ICAP theoretical framework, analyzed 54 lesson plans. In addition, the study explored PSBTs’ perceptions regarding the development and contribution of TDP-AI to biology teaching. The results reveal three patterns of AI agent application in lesson plans: (a) intensive use of TDP-AI agents across all lesson phases, promoting students’ cognitive engagement, (b) moderate and selective use focused on core activities, and (c) limited use mainly in introductory segments. Five thematic areas reflect PSBTs’ perspectives: (1) personalization and flexibility of learning, (2) enhancement of student motivation and engagement, (3) development of teachers’ digital and pedagogical competencies, (4) technical and resource-related challenges in agent development, and (5) pedagogical-methodological barriers in designing student–AI interactions. The findings emphasize the need for systematic support for preservice teachers in developing digital tools and building pedagogical approaches to ensure AI is used ethically and effectively in science education.
The importance of scientific argumentation is reflected not only in the depth of knowledge construction but, more importantly, in its role in fostering students’ scientific thinking. Existing domain-specific research on learning progressions in scientific argumentation has largely focused on selected components (e.g., claim and explanation) and has primarily targeted secondary school students. Furthermore, inconsistent definitions of the constituent elements of scientific argumentation across studies have led to fragmented models of learning progression. This study employed the Rule-Space Model to investigate the developmental pathways of elementary school students’ scientific argumentation in the ecosystem domain. First, the study developed an attribute hierarchy model of ecosystem scientific argumentation consisting of five attributes: Expressing a Claim, Providing Evidence, Simple Reasoning, Complex Reasoning, and Rebuttal. Based on this model, eight ideal attribute mastery patterns and three potential learning progression pathways were generated. Second, an Assessment Tool for Measuring Students’ Learning Progression in Scientific Argumentation in the Context of Ecosystems (ATMSLPSA) was developed. Following pilot testing with 101 students and subsequent refinement, the final instrument was administered to 320 sixth-grade students in China, yielding 299 valid responses. The results supported the effectiveness of the three proposed learning progression pathways. Based on these pathways, this study classified elementary school students’ learning progression in ecosystem scientific argumentation into five levels. Overall, students’ performance in scientific argumentation was concentrated primarily at Level 2, indicating that most students had developed the ability to make preliminary evidence-based inferences but still exhibited weaknesses in complex reasoning and rebuttal. Third, individual diagnostic reports on ecosystem scientific argumentation were generated from the response data, thereby facilitating inferences about potential subsequent developmental pathways and providing a basis for targeted instructional intervention and remediation.
Informal science settings, such as zoos, aquariums, and science centers, naturally spark emotions ranging from curiosity and excitement to frustration. Yet, studying emotional experiences in informal science learning (ISL) environments has been difficult, largely due to the challenges of measuring emotions in real time. Traditional methods, including self-reports, physiological measures, and observational approaches, offer useful insights but also come with their own limitations. To address these, the aim of this study was to develop an observational tool to systematically document observable emotional expressions during ISL visit - the EMOTool (Emotions Observational Tool), specifically designed for researchers conducting observational studies in ISL environments. The EMOTool utilizes observational data from eight studies in ISL settings to develop a comprehensive taxonomy of emotions observed in these contexts, incorporating refined definitions that include observable behaviors and discursive markers, along with an adapted version of the Affect Grid for accurate emotion coding. The EMOTool provides ISL researchers with a practical tool for emotion research and analysis, improving clarity and precision in identifying and distinguishing emotions within observational data. We expect that the EMOTool will enhance research in ISL contexts, providing the means to identify emotions in real-time and serving as a crucial step towards deeper understanding of the role of the emotional experiences.
Epistemic stances of learners impact how they use GenAI in learning science. Yet, even though these epistemic stances are so important, little research has examined how learners’ epistemic stances were linked to their use of GenAI, and how these stances changed after using GenAI in learning science. Incorporating multiple case studies, we purposefully engaged two pairs of ninth graders as they used different modes (visual, written, verbal, and gestural modes) to represent their epistemic stances in the context of socio-scientific argumentation. We also examined how their use of GenAI in socio-scientific argumentation might shape their multimodal representations of epistemic stances. One pair of students distanced themselves from GenAI bots. They changed from positioning that GenAI offers different perspectives to believing that the primary value of GenAI lay in communicating correct knowledge. In socio-scientific argumentation, this pair of students asked the GenAI chatbot to emulate a professor delivering long scientific texts. Another pair of students, as they developed competence in evaluating socio-scientific texts provided by GenAI, changed their epistemic stance from believing that GenAI’s benefit lies in offering multiple perspectives to a stance of cooperating with GenAI and evaluating its output. These findings reveal a need to use a multimodal approach to capture how students collaboratively communicate their epistemic stances when they use GenAI in learning science. By understanding their epistemic stances, teachers can provide in-the-moment responses that shape a shared epistemic agency between students and GenAI.
The ability to construct scientific explanations is a cornerstone of scientific literacy, and its development is a priority in high school science education. This study introduces and evaluates a novel pedagogical approach, termed Algorithmic Explanations (AE), which guides students to structure their reasoning in a step-by-step, logical manner akin to an algorithm. Implemented in a high school chemistry context, the AE approach was used to teach three topics: (1) Electrochemistry, (2) Chemical Experiments, and (3) Redox Reactions. A mixed-methods approach was employed. A quasi-experimental design confirmed the effectiveness of the AE approach, with results showing that the experimental group significantly outperformed the control group in constructing scientific explanations across all topics following the intervention. Furthermore, a time-series design affirmed the transferability of the AE approach’s positive effects, demonstrating its utility in enhancing explanation skills across different chemistry topics. Interview results revealed that students held positive attitudes toward the AE approach. The AE approach facilitated students learning by clarifying the structure of scientific argumentation and deepening their conceptual understanding of chemistry.
Mechanistic explanations require learners to coordinate entities, activities, and interactions to account for how a scientific process unfolds over time. Student-generated sequential particle diagrams and written explanations may support this coordination by externalising otherwise invisible motion and interaction patterns. This study investigates how Year 7 students used sequential diagrams and written explanations to account for sugar dissolving across contrasting conditions (cold versus hot water, and with stirring). Using qualitative analysis of 19 student worksheets, we identified recurring conceptual elements and patterns in causal organisation. Students’ explanations clustered into three accounts of dissolution: sugar disappearing, sugar filling space among water particles, and sugar becoming attached to water particles. Two illustrative cases show how students used temporal sequencing to build mechanistic account to represent changing particle motion and interactions, alongside mixed inferences that shaped their explanations. We argue that sequential diagrams with written explanations function as a window into developing mechanistic reasoning by revealing transitional forms of causal organisation that may remain hidden in written text alone.
