Chemistry Education Research and Practice最新文献

Scientific modeling is a core epistemic practice in chemistry, yet supporting learners in visualizing and making sense of abstract molecular structures and functions remains a challenge. While generative artificial intelligence (GenAI) offers new representational capabilities, it remains unclear how text-based prompt engineering intersects with established theories of scientific modeling. This study addresses this gap by reconceptualizing prompt engineering as a form of molecular modeling within AIMS, a chemistry-specific GenAI-supported molecular modeling environment. Through a qualitative case study, 11 secondary science teachers used GenAI embedded in AIMS to design novel sweetener molecules. Applying the CLEAR and REFINE analytic frameworks through the lens of Halloun's Modeling Theory, the study examined how participants translated disciplinary knowledge into iterative prompt-based molecular designs. The analysis concludes with three key findings. Participants' prompts functioned as externalized models that embedded assumptions about molecular structure-function relationships and design constraints within explicit chemical and theoretical requirements. As iterations progressed through model testing, reasoning shifted from broad appeals for scientific accuracy to precise, mechanistic constraints, including spatial arrangements and molecular properties. Revisions operated as controlled experiments where participants tightened or relaxed constraints to test the plausibility of GenAI outputs. These findings advance chemistry education by demonstrating that a chemistry-specific GenAI-supported modeling tool can function as a modeling assistant rather than an answer engine, scaffolding the core epistemic practices of construction, evaluation, and refinement in chemistry education. This reconceptualization positions prompt engineering as a source of written evidence for how learners articulated, tested, and revised chemical constraints, offering educators new ways to support and assess student modeling practices.

Correction for ‘Students’ epistemological resources and framing of stoichiometry assessment items across linguistic backgrounds: insights for equitable design' by Anna Eunji Kim et al., Chem. Educ. Res. Pract., 2026, https://doi.org/10.1039/d5rp00395d.

Developing and using models is recognized as a core scientific practice. This includes, among other things, the reflection on models at the epistemic level, referred to as meta-modeling knowledge (MMK), which encompasses both media-oriented and methodological perspectives regarding models. In organic chemistry, reaction mechanisms function as central models to describe, explain, and predict chemical reactions. Given the discipline- and context-dependent character of MMK, we distinguish between general MMK and specific meta-modeling knowledge (SMMK) related to organic reaction mechanisms. While prior work has examined students’ challenges with models and modeling as well as mechanisms in organic chemistry, little is known about how students’ general MMK and their discipline-specific epistemic perspective on reaction mechanisms interact in shaping students’ reasoning. To address this, we conducted a qualitative interview study with 22 chemistry students. The study explored how students perceive the modeling character of reaction mechanisms and patterns across students’ ideas within the SMMK and MMK dimensions. The findings show that students expressed a diverse range of ideas, from viewing reaction mechanisms as pictorial representations of reaction pathways to understanding them as epistemic tools used in inquiry processes or for practical purposes. Students’ ideas varied not only within the SMMK dimension but also in how these were related to ideas within the MMK dimension, indicating multiple, coexisting epistemic ideas. Different patterns emerged across the identified student groups: some students demonstrated comparable ideas across both dimensions, whereas others showed rather varying ideas within and across the two dimensions.

Chemistry learning places distinctive multimodal demands on students, requiring coordination across symbolic, submicroscopic, and macroscopic representations. While nonverbal communication has been shown to support representational reasoning in chemistry, it is often treated as a stable instructional resource, with limited attention to how its meaning is interpreted across cultural and educational contexts. This study examines how nonverbal communication functions as a discipline-specific semiotic resource in internationalised chemistry classrooms, drawing on the experiences of fifteen Chinese international students engaged in chemistry learning in the UK. Guided by a sociocultural and interpretivist framework, in-depth semi-structured interviews were thematically analysed to explore how students notice, interpret, and respond to instructors’ nonverbal cues. Findings show that visual and auditory nonverbal cues serve distinct roles in supporting translation across representational levels and engagement with spatial, mechanistic, and procedural aspects of chemical knowledge. Students’ interpretations of these cues were strongly shaped by prior educational experiences and culturally grounded expectations, which in some cases led cues to be overlooked or misinterpreted as incidental rather than instructional. These findings highlight the importance of making nonverbal resources explicit to support inclusive engagement with chemical representations in internationalised classrooms.

This study presents the design and implementation of an AI-supported modeling-based teacher education program integrating Model-Based Learning (MBL) with the 5E learning cycle. The program was implemented with 14 in-service chemistry teachers over 24 lessons and consisted of pre-interviews, three modeling cycles, and post-interviews. During the modeling process, teachers developed dynamic models through dialogic interactions with generative AI (Claude). Metamodeling knowledge levels were explored using seven questions across six domains: nature of models, purpose of models, modeling processes, variability, diversity, and evaluation criteria. Responses were analyzed from objectivist and subjectivist understandings. Teachers identified limitations of the electron sea model in explaining luster (n = 14), thermal conductivity (n = 7), malleability and ductility (n = 6), and electrical conductivity (n = 3). To address these, teachers developed educational models incorporating the wave nature of electrons, lattice vibrations (phonons), and redistribution of electron clouds. Analysis revealed a shift from predominantly objectivist understandings of models in the pre-assessment toward more subjectivist understandings of models in the post-assessment. Notable improvements were observed in modeling processes, variability, and diversity of models. These changes were observed in the context of modeling activities that involved recognizing model limitations and developing educational models, within which generative AI served as an interactive element supporting dialogic interaction and iterative revision as part of the broader instructional program. Teachers came to understand models not as fixed knowledge but as tentative and revisable inquiry tools. These findings provide direction for teacher education focused on strengthening modeling knowledge in science education.

How students read graphical information in chemistry depends not only on what a graph displays but on which features they treat as meaningful and what knowledge those features activate. This study uses the cognitive resources perspective as framing to examine how undergraduate general chemistry students reason about titration curves in a preservative selection task involving juice stability and microbial growth. Twelve second-semester general chemistry students participated in think-aloud interviews while selecting a preservative based on graphical evidence. Analysis identified two distinct resource activation patterns. For six students, reasoning was organized around discrete pH values, drawing on part-for-whole and comparing resources productive in stoichiometric contexts. For five students, reasoning centered on slope and rate of change, with resistance-to-change resources linked functionally to preservation goals. One student activated both patterns. Although all approaches drew on productive existing knowledge and could yield correct selections, they reflected qualitatively different activation patterns. These findings indicate that variation in titration curve interpretation reflects differential activation of existing resources shaped by perceptual salience and task framing, with implications for how instruction might support students in activating slope- and resistance-based reasoning in graphical contexts.

In laboratory chemistry education, experimentation is still often employed as a means of obtaining a “correct” result, while the grounds for trusting data and the conditions under which it is interpreted remain implicit for learners. Even in project-oriented formats, students often lack a methodological language that would allow them to comprehend experimental results as justified and context-dependent knowledge. To address this gap, the present study explores how key features of analytical method validation, specificity, linearity, precision, and trueness, can be pedagogically reinterpreted to support students’ reasoning about experimental data and design. This approach was implemented in an educational workshop with pre-service chemistry teachers, during which participants developed and justified a spectrophotometric method for measuring the total flavonoid content in a plant matrix. A qualitative analysis of students’ written reflections was conducted to examine how engagement with validation characteristics influenced their reasoning about experimental results and the justification of methodological choices. The findings suggest that introducing analytical validation as a conceptual framework for analysing experimental data encourages students to interpret results through the lens of measurement quality, relate method features to the reliability of analytical outcomes, and apply validation criteria when designing experimental procedures. These results imply that analytical validation can serve not only as a professional analytical process but also as a methodological foundation for fostering experimental reasoning and making trust in experimental data an explicit focus in chemistry education.

This study investigates how undergraduate chemistry students engage in sensemaking during an inquiry-based Boyle's law laboratory activity, focusing on how different types of knowledge gaps and associated epistemic emotions guide their sensemaking trajectories. A holistic multiple-case study design was employed, with sensemaking episodes serving as the unit of analysis. Two student groups were purposefully selected to represent contrasting sensemaking trajectories. Group 1 exhibited a sustained sensemaking trajectory and completed the activity, whereas Group 2 exhibited a disengaging sensemaking trajectory and ultimately withdrew from the task without arriving at a coherent resolution. Data were collected through audio and video recordings, activity sheets, and epistemic emotion diaries, and were analyzed thematically. The results revealed clear differences between the groups. Group 1 primarily encountered procedural knowledge gaps. These gaps involved relatively low element interactivity and were resolved through collaborative reasoning. By contrast, Group 2 encountered conceptual and epistemic gaps that required the coordination of multiple abstract constructs, imposed high cognitive demands, and remained unresolved. Epistemic emotions also diverged markedly across the two groups. Both began the activity with curiosity and surprise, yet Group 1 sustained positive emotions that helped them manage confusion productively. Group 2 experienced increasing frustration and boredom, reflecting a trajectory of hopeless confusion that hindered sustained sensemaking. The interaction of unresolved gaps and negative epistemic emotions seemed to be associated with Group 2's disengagement. The findings suggest the instructional relevance of attending to both cognitive demands and epistemic emotions, particularly in moments when students encounter persistent knowledge gaps during laboratory sensemaking.

Grouping approaches are commonly employed in chemistry education research to better understand variation. Traditionally used as a tool for data dimensionality reduction, these approaches are used as a tool to help researchers interpret complex data sets that can inform instructional strategies or target interventions. Among these techniques, cluster analysis, and in particular k-means clustering, has gained popularity for its simplicity and applicability to continuous variables. However, k-means cluster analysis is limited by its algorithmic nature, including assumptions of equal variance between clusters. Latent profile analysis, a model-based alternative within the mixture modeling framework, offers greater flexibility by allowing probabilistic group membership and the modeling of individual variances and covariances across latent profiles. This methods-focused study compares k-means clustering and latent profile analysis using data from undergraduate organic chemistry students enrolled in courses with either traditional or specifications grading. By examining students’ affective traits, this study highlights the strengths and limitations of each grouping approach. Findings support the broader adoption of mixture modeling in chemistry education research to explore heterogeneity.

This study examines teacher candidates’ reasoning about solution concentration units (mass percent, molarity, and molality), with a focus on difficulties seen in definitional tasks and item-based applications. A 12-item diagnostic test informed by Cognitive Load Theory (CLT), Dual Process Theory (DPT), Conceptual Change Theory (CCT), and Representational Competence Theory (RCT) was administered to 152 teacher candidates. Additionally, semi-structured interviews were conducted with purposefully selected teacher candidates to clarify the reasoning routes underlying their response choices. Quantitative findings showed an overall accuracy of 70.29%, but performance was notably low on some items (e.g., Item 5: 32.30%), particularly when tasks required coordinating unit meaning with the correct referent and managing conversions and proportional reasoning. Error coding revealed recurring response patterns across five categories: definitional confusion (DC), unit mistake (UM), ratio-reasoning mistake (RR), superficial decision (SD), and computational mistake (CM). Interview evidence suggested that, within the formats of this instrument, some teacher candidates relied on cue-based responding and limited checking rather than explicitly grounding choices in the intended referent meaning (e.g., solution volume vs. solvent mass). Interpreted through CLT, DPT, CCT, and RCT, the findings suggest that effective instruction may require more than definitional recall and routine calculation. It may also benefit from supports that explicitly connect formula, unit, and problem context and encourage checking during problem solving. The study aims to contribute to chemistry education research by offering a theory-informed interpretation of recurring error patterns in concentration tasks based on integrated test and interview evidence.