This study investigates how embedding a mixed reality Serious Educational Game (MRSEG) in an immersive, place-based socioscientific issues (SSI) course shapes undergraduate’s understandings of and responses to climate change on the Outer Banks, North Carolina, USA. Grounded in Inquiry-Driven Disruptive Pedagogy (IDDP), Multimedia Learning Theory, and research on spatiotemporal reasoning, the MRSEG overlaid historical imagery, scientific data, and future projections onto five coastal locations along North Carolina Highway 12 using mobile mixed reality. Eight students engaged with the MRSEG during a week-long field experience. After completing the MRSEG, students responded to prompts adapted from the Socioscientific and Environmental Engagement Survey (SEEDS). Qualitative analysis revealed that students attributed shifts in four Socioscientific Orientations: Ecological Worldviews, Social and Moral Compassion, Socioscientific Accountability, and Scientific Evidence Views. Students credited these changes to the MRSEG’s ability to visualize present conditions simultaneously with past or future projections, while also interlacing sociocultural and moral considerations. These findings have led to the proposal of a new construct, socioscientific spatiotemporal reasoning (SS-STR), which describes how learners coordinate spatial-temporal evidence with ethical and civic considerations in mixed reality-enhanced SSI contexts.
Out-of-field (OOF) teaching is a persistent challenge in secondary school physics, where effective instruction requires strong content knowledge (CK) and pedagogical content knowledge (PCK). Digital resources are often proposed as support, yet little is known about how teachers’ disciplinary background shapes their use. This study comparatively examined in-field (IF) and OOF physics teachers’ accounts of implementing blended learning with a Massive Open Online Course (bMOOC) in 9th-grade physics classrooms. Eleven secondary school teachers (4 IF, 7 OOF), who integrated a physics bMOOC into their instruction while participating in a professional development program, took part in the study. The data were collected through semi-structured interviews and analyzed inductively employing the Constructivist Grounded Theory approach, informed by two complementary theoretical lenses: professional knowledge and professional identity. Both the IF and OOF teachers described students’ engagement, motivation, and autonomous learning quite similarly. However, their accounts of instructional enactment diverged substantially. The IF teachers appropriated the bMOOC resources flexibly, whereas many OOF teachers reported more teacher-centered use, challenges with pacing and control, and tensions related to disciplinary authority. The discussion conceptualizes bMOOCs as carriers of collective PCK whose enactment depends on teachers’ knowledge and professional identity, and suggests that digital resources can mediate but not determine practice in out-of-field contexts.
Students often encounter difficulties in comprehending abstract chemistry concepts when these concepts are presented without sufficient contextual grounding or opportunities for observable, embodied representation. While integrating the history of science into digital storytelling can provide valuable contextual support, conventional screen-based storytelling focuses primarily on 2D audio-visual manipulation, potentially limiting the depth of enactive cognitive engagement. To address these challenges, this study proposes a robot-based, story-driven contextual creation (RS-CC) approach. RS-CC leverages the physical embodiment and programmability of robots, allowing students to translate abstract scientific narratives into enactive multimodal representations through robot movement, gesture, dialogue, spatial positioning, and visual expression, rather than relying solely on screen-based visual and auditory modes. Adopting an explanatory sequential mixed-methods design, the study combined quantitative analysis, including ANCOVA and independent-samples t-tests, with qualitative inquiry through grounded theory analysis of semi-structured interviews to examine both the magnitude and possible mechanisms of the observed treatment effects. A quasi-experimental study involved 60 eighth-grade students divided into an RS-CC group and a conventional digital storytelling (CDS-CC) group. The study examined differences in chemistry learning achievement, digital story quality, deep motive, and deep learning strategies. Results indicated that the RS-CC group generally outperformed the CDS-CC group across the measured outcomes, although some effects should be interpreted cautiously under strict multiple-comparison correction. The findings suggest that robot-mediated enactment may support contextual chemistry learning by transforming storytelling from screen-based observation into enactive multimodal representation, thereby facilitating deeper integration of scientific knowledge and multimodal expression. This approach offers a promising pedagogical strategy for supporting students’ understanding of abstract chemistry concepts through history-of-science storytelling and embodied robotic representation.
Pre-service science teachers often struggle to conduct critical, practice-oriented reflection due to limited experience and a lack of objective evidence. This study employed a design-based research (DBR) approach involving three iterative cycles of design, implementation, and evaluation to develop and refine a collective reflection model. The model integrated classroom video recordings with intelligent multimodal analysis reports (IMAR) generated by an AI-powered platform. Across successive iterations, the design was enhanced through structured facilitation protocols, comparative analysis of reports, and the involvement of in-service teachers and experts. These refinements progressively transformed the nature of collective reflection: participants’ discourse shifted from subjective impressions and superficial comments to evidence-based, critical dialogues. Analyses of teaching videos and self-efficacy surveys further indicated measurable improvements in instructional practices. The study concludes by presenting a set of design principles for leveraging AI analytics to foster critical, organized, and practice-oriented collective reflection. These principles offer a scalable and practical framework for integrating intelligent teaching analytics into teacher education programs.
This study presents a systematic content analysis aimed at examining the effects of augmented reality (AR), virtual reality (VR), and mixed reality (MR) technologies on student achievement, motivation, engagement levels, and cognitive gains within the context of STEM (Science, Technology, Engineering, and Mathematics) education. A total of 59 peer-reviewed academic articles published between January 2019 and June 2024 were systematically reviewed using an article analysis form developed by the researchers. Throughout the review process, multiple variables were examined, including the journals in which the studies were published, year of publication, types of technology used (AR/VR/MR), country of implementation, research methods and design types, participant age/grade levels, sample sizes, discipline areas (STEM, Mathematics, Science, etc.), sub-domains, problem focus, and the significance of research findings. In addition, the keywords used in each study, the number of citations, and the Q-index classifications of the journals were also analysed in detail. The findings indicate that AR and VR-based applications significantly enhance students’ academic performance, attitudes toward courses, and levels of participation compared to traditional instructional methods. Furthermore, certain implementations have been shown to positively impact learners’ scientific process skills, problem-solving strategies, and self-regulation abilities. These results reveal the strong pedagogical potential of integrating digital technologies into learning environments and offer meaningful implications for instructional designers, educational technology practitioners, and policymakers.
This study examined the effectiveness of integrating mobile technologies into the 5E learning cycle model in improving pre-service science teachers’ learning outcomes in physics laboratory contexts. A quasi-experimental design was employed with 64 participants, assigned to an intervention group (n = 32) and a control group (n = 32). Data were collected using the Academic Achievement Test (AAT), the Attitude Scale toward Physics Laboratory (ASPL), and the Scientific Process Skills Test (SPST). The findings indicated that the mobile-supported 5E model led to significantly higher academic achievement, more positive attitudes toward laboratory work, and improved scientific process skills compared to the traditional implementation. Effect size analyses further supported these findings, indicating large to very large improvements in academic achievement and scientific process skills, and moderate to large improvements in attitudes toward the laboratory. Sub-factor analyses of the ASPL revealed particularly strong improvements in dimensions related to technology use and data analysis, suggesting that mobile technologies can support the connection between conceptual understanding and experimental practice. Overall, the findings highlight the potential of integrating mobile technologies into structured inquiry-based models to enhance laboratory learning in teacher education.
The power of embodiment in VR enables learners to physically experience concepts instead of merely visualizing them abstractly. Early evidence suggests that engaging in VR environments positively impacts visual-spatial cognition. Yet, it is challenging to determine specific effects from the VR experiences for complex STEM topics, beyond general explanations of immersion and presence. This study explores how VR simulations designed to address complex scientific concepts shape learners’ mental representations as revealed through gesture analysis of their explanations. Undergraduate biology students who participated in a facilitated VR simulation experience on gene regulation using the lac operon model (n = 30) were compared with another group who only engaged with course materials (n = 12). Gestures accompanying their explanations were analyzed for types and semantic meaning using the Structure, Behavior, and Function framework. While canonical gestures for basic structures and processes, as well as binary function (e.g., on/off), were common across groups, VR participants produced significantly more gestures representing peripheral structural components, dynamic behavioral processes, and nuanced functional complexities (e.g., rates). Case studies of students who created ‘air diagrams’ revealed three-dimensional spatial relations that mirrored depictions in the VR environment, indicating persistent spatialized memory traces. Analysis of student drawings was also conducted and showed VR participants included significantly more accurate structural and behavioral elements in their representations. The study contributes insights into how VR environments shape the character of mental representations for complex scientific phenomena, with implications for designing effective VR environments for science education.
Chemistry learning and understanding depends on the ability to think circularly at three levels: the macroscopic, the sub-microscopic, and the symbolic level. This ability is mostly given for granted in higher education, although many students struggle in representing the sub-microscopic level, which is outside their range of experience. In addition, handling these three levels at a time may easily overload the Working Memory (WM), thus hindering meaningful learning. It has been demonstrated that success in chemistry learning correlates with good visuospatial abilities as well as with WM capacity. Nevertheless, the knowledge about specific cognitive processes involved in chemistry learning is still limited, especially concerning undergraduates. The present work contributes to this goal by investigating the differential involvement of verbal and visuospatial WM components in chemistry learning. We evaluated a sample of 315 undergraduates with a chemistry task (CT), and a subset (N = 81) with three different cognitive tasks (the Listening Span Test, the Imaginative Puzzle, and the Cattell’s Fluid Intelligence Test), aimed at assessing the verbal WM, the visuospatial WM and the estimated IQ, respectively. We sought correlations between WM components and chemistry performance: our results highlight a positive correlation between accuracy in performing the chemistry task and performance in the visuospatial WM task. Supplemental analysis compared the role of WM components in relation to the types of chemistry task questions (textual or visual). Our results showed that good visuospatial WM performance promoted the accuracy of students’ performance when they were asked to manipulate both textual and visual information; this advantage was enhanced when visual information was involved.
Scientific reasoning, as a core component of scientific thinking, is widely regarded as important for supporting students’ science learning and innovation-related competencies. However, fewer studies have translated theoretical models of scientific reasoning into classroom-based instructional designs supported by digital tools. This study integrated the Data-Mechanism Coordination and Reasoning (DMCR) dual-pathway framework with Scratch graphical programming and PhET virtual simulations. The course design, which emphasizes control of variables (COV), data analysis (DA), and causal decision-making (CDM), was implemented in a 12-week classroom intervention with 104 fifth-grade students. In this sample, we observed statistically significant pre–post increases in students’ task-based measures of scientific reasoning, particularly in COV and DA; these increases were evident across students with higher and lower baseline scores. Students’ performance also differed across the three dimensions: performance was highest in COV, followed by DA, while CDM remained comparatively weaker. The study provides classroom-based evidence on how a DMCR-informed digital learning design may support scientific reasoning practices among fifth-grade students and offers implications for curriculum design and digital pedagogy in science education.
The growing integration of Artificial Intelligence (AI) in education has brought increased interest in using AI-supported platforms for lesson planning. However, concerns remain regarding the pedagogical validity and theoretical alignment of AI-generated plans. This study examines the instructional quality of lesson plans created by two AI platforms—one general-purpose and one education-focused—through expert evaluation. Utilizing a case study design, lesson plans based on the Engineering Design-Based Learning (EDBL) approach were generated for a sixth-grade science unit, incorporating four skill dimensions from the revised Turkish science curriculum. Eleven science education experts assessed each plan using a standardized rating scale and open-ended feedback. Findings reveal that while both platforms support key competencies such as conceptual and social-emotional skills, they fall short in fully capturing the iterative and reflective processes of EDBL. The education-focused platform demonstrated higher alignment with curriculum goals and instructional sequencing. Experts emphasized that AI-generated plans can serve as useful drafts but require pedagogical adaptation and teacher judgment for effective classroom use. The study highlights the promise and limitations of AI tools in science instruction, offering insights for educators, AI developers, and curriculum designers aiming to integrate AI meaningfully into teaching practice.
Instructors use various approaches to teach introductory chemistry concepts such as nomenclature, atomic structure, and reaction balancing. Such approaches have different impacts depending on the topic being taught. This study investigates student learning and perspectives for two such approaches within an introductory chemistry course: study guides and immersive virtual reality. Guided by cognitivism, embodied cognition theory and affordances theory, this mixed-method study collected data using pre/post tests, a student questionnaire, an instructor focus group, and classroom observations (n = 298 students). Our quantitative findings illustrate no learning differences by treatment for nomenclature and reaction balancing, but show a significant difference for immersive virtual reality with atomic structure. Our qualitative findings show mixed student and instructor perspectives on using study guides versus immersive virtual reality. We discuss general and practical implications for using study guides and immersive virtual reality in large undergraduate chemistry courses.
Science, technology, engineering, and mathematics (STEM)-focused programs such as FIRST Robotics have demonstrated success in promoting technical skills. Yet prior research indicates that women often experience and respond differently to competitive environments, contributing to enduring gender gaps in professional persistence. Despite this emphasis on program values, a significant gap remains in understanding how women internalize these values within a framework that captures both psychological and sociocultural barriers. This study investigated how women FIRST alumni perceived the influence of the program's Core Values: discovery, innovation, impact, inclusion, teamwork, and fun, on their STEM career development. Guided by Social Cognitive Career Theory (SCCT), the analysis situated these perceptions within structural environmental constraints, operationalized through the Six-Factor Under-Representation Framework. Adopting a qualitatively driven mixed-methods design, we analyzed semi-structured interviews with 15 women alumni to identify variation in perceived supports and barriers across participant characteristics, such as leadership roles, scholarship attainment, and professional maturity. The findings indicated that environmental factors played a stronger role than personal or behavioral factors in shaping participants’ trajectories. Inclusion was most frequently associated with negative experiences, and challenges in translating inclusive program ideals into lived experiences for women participants persist. Moreover, variations in value internalization were systematically associated with scholarship achievement and leadership roles, which acted as critical buffers against structural disincentives. Overall, the study demonstrated the value of integrating psychological career frameworks with structural sociocultural analysis to better support women’s sustained participation in STEM.
Artificial intelligence (AI) is transforming education, yet its impact on learning remains minimally theorized. Existing pedagogical models rarely address AI’s role, leading to misapplications - such as comparing student performance to AI outputs - without understanding how AI influences learning. This paper proposes a biological theory of learning grounded in autopoiesis and adaptation, reframing learning as a process of biological change driven by energy modulation and the regulation of biological perturbation. We revisit foundational assumptions about human nature and analyze three scenarios: (1) AI versus traditional tools, (2) human-human versus human-AI interaction, and (3) the learner’s relationship with AI. This framework moves beyond debates like constructivism versus behaviorism, offering a deeper understanding of learning as embodied transformation. AI is not merely the context for this framework but the phenomenon that renders it necessary: by producing learning-like outputs without biological learning, AI forces a precision in the definition of learning that no prior technology demanded. Ultimately, we challenge the trend of modeling students after AI systems, advocating instead for educational environments that honor complexity, emotion, and human adaptability. We ground these arguments in science education as the domain where AI’s entry into learning environments is most acute and where the biological dimensions of learning — hands-on experimentation, sensory perturbation, and embodied inquiry — are most clearly at stake.
The purpose of this study was to examine the relationship between computational thinking and science process skills in preschool students. The participants were 153 children aged 5 to 6 from three different preschools in Türkiye. Data were collected using a demographic information form, the TECHCHECK-K scale to assess computational thinking, and the Science Process Skills Achievement Test. The data were analyzed using RStudio. The results indicated that preschool students demonstrated moderate levels of computational thinking and relatively high levels of science process skills. Correlation analyses revealed a moderate and statistically significant relationship between computational thinking and science process skills. Furthermore, structural equation modeling demonstrated that computational thinking was positively and significantly associated with science process skills, explaining 18
This study investigates how discourse and rating signals from self- and peer-assessment can inform the design of technology-supported scaffolds to enhance feedback literacy in project-based STEM education. The dataset comprises 9,180 assessment events written in Indonesian by 95 undergraduate engineering students, making it one of the largest non-English peer feedback corpora analyzed in this domain. Two empirical research questions guided the analysis: (1) What patterns characterize students’ written comments and rating behaviors? (2) How do these signals relate to course outcomes? A third propositional question regarding design implications was explored in the discussion. Findings show that short, descriptive comments were common in both self- and peer-assessments, limiting elaboration. Peer ratings were more strongly associated with final course outcomes than self-ratings, confirming peers as more reliable indicators of achievement. Calibration mismatches between self- and peer-assessments were systematic across rubric dimensions, revealing persistent over- and underestimation patterns. Building on these empirical patterns, we develop three lightweight and explainable design hypotheses: elaboration prompts triggered by unusually short comments, rubric-aware prompts that encourage reference to task criteria, and calibration prompts that activate when self-ratings diverge substantially from peer norms. These mechanisms are grounded in transparent, real-time indicators and are intended to be tested in future classroom implementations. By grounding learning analytics in non-English STEM classrooms, this study demonstrates how simple, explainable signals can inform the development of feedback literacy scaffolds, support metacognitive monitoring, and improve collaborative learning outcomes in science and engineering education.
This study explored how eco-oriented gamification initiatives activate key psychological mechanisms that shape Vietnamese university students’ intentions to sort waste at the source. By integrating sustainability-driven digital innovation into higher education, the research seeks to advance education for sustainable development and contribute to the achievement of the Sustainable Development Goals (SDGs). Data were collected via an online survey of 369 university students from multiple Vietnamese institutions. Structural equation modeling (SEM) was employed to test the proposed relationships and mediation effects. The results revealed that green gamification significantly enhances Environmental, Social, Governance (ESG) compliance, stimulates epistemic curiosity, and fosters immersive experiences. Each of these psychological factors positively influenced students’ waste-sorting intentions. Furthermore, ESG compliance, curiosity, and immersion serve as critical mediators that translate gamified sustainability engagement into stronger pro-environmental behavioral intentions. These findings provide actionable insights for universities and policymakers seeking to embed sustainability into educational practices through innovative digital strategies. By aligning gamified learning systems with governance standards and experiential engagement, institutions can cultivate long-term environmental responsibility in young adults. This study advances sustainable education literature by integrating governance-based psychology with cognitive and experiential mechanisms within a gamification framework. By offering empirical evidence from an emerging, yet underexplored, context, this study contributes a novel theoretical and practical perspective on how green gamification can drive sustainable behavioral transformation in higher education.
In response to global demands for digitally competent citizens, it is crucial to examine how science teachers integrate technology into their teaching practices through technological pedagogical and content knowledge (TPACK). This study employs the TPACK-Practical (TPACK-P) framework, which emphasizes assessing teachers’ proficiency in authentic classroom settings, to investigate two complementary aspects: (1) evaluate the proficiency of in-service science teachers with respect to assessment, planning, design, and enactment dimensions, and (2) explore the interactions among the five TPACK-P components. A multiple case study of four in-service science teachers teaching force and energy topics was conducted utilizing classroom observations and semi-structured interviews. Findings revealed varying proficiency levels across the dimensions, from lack of use to infusive application. Furthermore, five key assertions emerged: (i) central role of assessment in shaping practices, (ii) the uniqueness of each teacher’s TPACK-P map, (iii) predominance of assessment-focused integration, (iv) underuse of ICT for subject content delivery, and (v) dynamic nature of TPACK-P. This study highlights both the complexity and need for context-sensitive support of ICT integration in science education.
Resource constraints in analytical chemistry education often limit students’ opportunities to practise with advanced instrumentation. This exploratory study investigated third-year undergraduate chemistry students’ perceptions of, and engagement with, a newly developed virtual reality (VR) application—Immersive ChemLab—featuring a digital twin of an Atomic Absorption Spectroscopy (AAS) instrument implemented as an interactive pre-laboratory preparation activity. The application comprised three mini-modules covering instrument setup, optimisation, and quantitative analysis. Using an explanatory mixed-methods design, we collected student perceptions through two surveys (N = 25, N = 18) administered after VR training and after completion of the physical laboratory session, supplemented by focus group data (N = 10). Quantitative data were analysed using non-parametric statistics, and qualitative data underwent thematic analysis, with integration yielding three overarching themes. Students reported that Immersive ChemLab enhanced their preparedness for the physical laboratory, with perceived benefits including improved understanding of AAS theoretical principles and instrument operation, increased familiarity and confidence, and self-reported efficiency compared to other instruments supported by traditional pre-laboratory materials. Despite technical challenges and regardless of prior VR experience, students maintained positive attitudes towards VR as a learning tool. Strong correlations emerged between module experiences and perceived learning benefits. However, significant methodological limitations constrain interpretation: reliance on self-reported perceptions without objective performance measures, absence of a control group, and small sample size prevent causal claims about VR effectiveness. The findings suggest potential value for VR-based pre-laboratory preparation where access to complex instrumentation is limited, whilst highlighting implementation considerations for chemistry educators. Rigorous controlled studies with objective learning outcome measures are essential to substantiate these exploratory findings.
The success of responsive science teaching to elicit, foreground, and pursue the substance of students’ ideas depends on how teachers can recognize and respond to these ideas and connect them to disciplinary perspectives. One approach to supporting responsive teaching is to design instruction that facilitates students’ scientific sensemaking and invites teachers to consider possible student ideas. Generating students’ ideas, however, is labor-intensive as it requires understanding of learning content and instructional contexts. In this work, we explore the capacity of several large language models (LLMs), including OpenAI’s GPT-5-mini, GPT-4o, Claude’s Sonnet 4, Gemini 2.5 Flash, Mistral 7B, and Llama-4-17B, to simulate 8820 students’ ideas in science learning activities across domains (life sciences, physics, and chemistry) and grade levels (elementary, middle, and high school) to support instruction planning. Findings indicate that the LLM-simulated responses are realistic and are mostly at the target grade levels for knowledge scope and readability. We observe variations in performance across LLMs and grade levels, with LLMs producing more within-scope ideas for high school lessons and overly complex ideas for lower grades. Interviews with six teachers using the LLM-generated ideas with their own lesson plans reveal that the simulated student responses are overall realistic and align with the lessons’ objectives. Responses are most useful when they spark teachers’ sensemaking about what students know and suggest relevant instructional scaffolds. We discuss how to improve LLMs’ simulations of student thinking and promote responsive science teaching with AI-generated insights.
