Gamification has emerged as an effective strategy to enhance student learning through intelligent tutoring systems (ITSs). This explanatory mixed-methods study examined the impact of a gamified ITS—Toc-toc MET—on second-grade students’ mathematical self-efficacy (MSE) and learning achievement in a South Korean elementary school. Ninety students were divided into an experimental group (n = 45), which received instruction integrated with Toc-toc MET, and a comparison group (n = 45), which received conventional teacher-led instruction. Quantitative findings revealed significant improvements in MSE but no significant differences in learning achievement. Qualitative interviews with one instructor and four students provided insights into these mixed results, highlighting students’ experiences and perceptions of Toc-toc MET. Findings suggest that a gamified ITS can effectively enhance early elementary students’ MSE and offer guidance for optimizing its implementation in mathematics education.
This systematic literature review examines current research on the integration of artificial intelligence (AI) in primary mathematics education, focusing on bibliometric patterns, pedagogical applications and outcomes, as well as implementation challenges and teacher-related considerations over the past five years. By analysing 30 peer-reviewed empirical studies published between 2020 and 2024, the review examines bibliometric patterns, research aims, methods, educational settings, perceived benefits, implementation challenges, and professional development needs. Findings indicate a growing interest in AI-enhanced learning, particularly through intelligent tutoring systems, adaptive platforms, and AI-based diagnostics. Key benefits include improved conceptual understanding, personalised feedback, and increased learner engagement, while major challenges involve technical limitations, pedagogical misalignment, and infrastructural disparities. The review highlights a predominance of experimental studies conducted in face-to-face classroom settings alongside the emerging use of blended and online models. Notably, teacher readiness and policy flexibility are pivotal for effective implementation. This review contributes a field-specific synthesis that informs researchers, educators, and policymakers of the evolving landscape of AI in primary mathematics education, offering insights for evidence-based design and sustainable integration.
This study investigated the effect of expertise-based training (XBT) on prospective mathematics teachers’ (PMTs) skills to assess and improve the authenticity of mathematics problems. Participants included 30 senior PMTs (8 males and 22 females) from the department of elementary mathematics education at a state university in northern Turkey. Data were collected through the authenticity of the mathematical problem instrument (AMPI) and a worksheet to reveal PMTs’ authentic transformation skills. This study revealed that the XBT approach effectively improved the authenticity of mathematical problems. Regarding PMTs’ skills at transforming math problems, the desired good-fit level was achieved by over a quarter of the participants. This study also showed that two components regarding assessing and improving the quality of authentic math problems need special attention: language use and specificity.
This study investigates how high school students simultaneously employ two modes of conceptualising gravity, force/attraction and curvature/geometry, when reasoning about gravitational phenomena. Drawing on the conceptual profile perspective, we examined how these modes coexist and distribute across different cognitive contexts and expressive modalities. Through multimodal analysis combining depictive gesture analysis, speech, drawings, and written responses, we investigated eight Brazilian Year-12 students who participated in a Special and General Relativity course. Data were collected from classroom discussions, written exercises, and individual interviews, enabling triangulation across spontaneous collaborative reasoning, deliberate written formulation, and retrospective individual explanation. Results reveal that (1) the two modes coexist actively within the same students rather than replacing one another; (2) their activation depends systematically on the type of cognitive demand, the geometric mode predominates in descriptive contexts while the force mode emerges in causal explanations; (3) pictorial modalities preferentially access the geometric mode even when speech accesses the force mode, producing multimodal discordances. These findings suggest that the conceptual transition from Newtonian to Einsteinian gravity involves a prolonged coexistence of conceptual zones rather than a substitution, and that multimodal analysis reveals aspects of students’ conceptual repertoire that remain hidden in purely verbal analysis. Connections with the gesture-speech mismatch literature are discussed.
This paper presents a theoretical perspective of generative artificial intelligence (AI) as dialogic partner by extending Jonassen’s notion of technology as partner through Bakhtin’s dialogism and insights from science classroom discourse. This reconceptualization shifts AI from a primarily cognitive or informational role towards a dialogic and post-humanist account of human–AI meaning-making, with pedagogical and methodological implications. Pedagogically, the perspective advances more dialogic forms of science learning, as illustrated through the design of a customized chatbot. Methodologically, it opens up a way of analyzing learning with generative AI as an interactional accomplishment, as illustrated through a systematic coding of student–AI chatlogs.
This scoping literature review characterizes research on mathematical modelling in pre-service teacher education. Specifically, we draw on 87 peer-reviewed journal articles to report on the general characteristics (e.g., geographic, study size, etc.) of the research on mathematical modelling in pre-service teacher education, which modelling perspectives (goals and task characteristics) and competencies for teaching mathematical modelling are represented in the research, and provide a conceptual organization of the literature. Our results show the research on modelling in preservice teacher education to be theoretically pluralistic, pedagogically oriented, and contextually situated. We suggest that future research would benefit from embracing this multidimensionality, developing frameworks that better account for how preservice teachers’ modelling competencies, beliefs, and practices co-evolve within the intertwined demands of teaching and learning mathematical modelling.
Several methods in Natural Language Processing (NLP) have been developed to automate Mathematics Word Problem (MWP) generation. However, there are concerns regarding the practicality of the MWP for collaborative application and the suitability of the evaluation method for specific MWP generation tasks. Addressing this issue, we carried out a systematic literature review (SLR) of 315 peer-reviewed articles published in the past 10 years to identify the current state of research on MWP generation and the current evaluation method, then propose a comprehensive framework for evaluation with the inclusion of the socialization aspect. The findings revealed the current state of MWP generation research including the generation purpose, generation method with input and output information, publication trends, etc. This study also summarized current evaluation methods for MWP research and introduced a framework applicable to both individual and collaborative settings. As Artificial Intelligence (AI) in education research is continuously progressing, the findings of this SLR can provide a valuable reference for researchers, allowing them to generate more advanced and good quality MWP with different input information and conduct quality evaluation using our proposed framework. Furthermore, recommendations for future studies also provided valuable insights to uncover potential research opportunities enabled by AI and the Large Language Model (LLM).
Teacher explanations are often viewed as monologic instructional methods associated with authoritative classroom discourse. This study challenges this assumption by examining how Life Sciences teachers’ mobilisation of Topic-Specific Pedagogical Content Knowledge (TSPCK) components within explanations shapes subsequent classroom interaction. Drawing on a qualitative single embedded case study of three experienced Grade 11 Life Sciences teachers in South Africa teaching population ecology, I analysed nine video-recorded lessons to explore how explanations function as pedagogical and interactional resources. Teacher explanations were analysed through four TSPCK components, which include learner prior knowledge, representations, what is difficult to teach, and curricular saliency. The fifth component, conceptual teaching strategies, was treated as emergent from their integration and was not coded as a separate category. Classroom interaction following the prompt embedded in explanations was examined for dialogic interaction patterns and categorised as knowledge-sharing or argumentative. The findings reveal that dialogic interaction emerged irrespective of the nature of the prompt (open-ended questions, closed-ended questions, or instructions) when explanations foregrounded and integrated multiple TSPCK components. In such cases, learners appropriated, re-voiced, and transformed the pedagogical and conceptual resources embedded in explanations, particularly representations and key conceptual distinctions, to justify claims, challenge peers, and extend lines of reasoning. I argue that teacher explanations function as framing devices that establish shared semiotic resources and discursive affordances, enabling heightened dialogic interaction beyond the effects of prompt type alone. These findings extend TSPCK research by empirically demonstrating how explanations mediate classroom discourse. I highlight implications for science teacher professional development that integrate explanatory practices with dialogic teaching.
Science, technology, engineering, and mathematics (STEM) participation is a central concern in mathematics and science education because it reflects how global higher education systems distribute access to advanced knowledge, research infrastructure, and scientific recognition. Yet participation in international STEM mobility remains unevenly structured across national, regional, and economic contexts. This study examines global patterns of STEM participation within the Fulbright Foreign Student Program as a window into how scientific opportunity is organized internationally. Drawing on administrative data for 16,627 Fulbright STEM scholars from 161 countries awarded between 2015 and 2024, the study employs a structural descriptive quantitative design using inequality and concentration measures. Guided by a dual theoretical framework integrating core–periphery theory and scientific capital, the analysis examines how STEM participation is distributed across world regions and income classifications, the extent to which participation is concentrated among a small number of countries, and how these patterns evolve over time. The findings reveal pronounced concentration, with a limited set of countries accounting for a disproportionate share of STEM scholars, alongside modest evidence of diffusion across a broader range of national contexts. These patterns are interpreted as structural associations, reflecting distributional tendencies within this program during the study period. Differences across income groups further indicate distinct configurations of scientific capital, with high-income systems exhibiting more diversified participation and lower-middle-income systems relying on concentrated national hubs. The findings suggest that STEM participation in Fulbright reflects persistent global structures alongside expansion across national contexts. These findings contribute to STEM education research by advancing a macro-structural perspective on participation and by extending scientific capital beyond individual trajectories to the level of global higher education systems.
The Effect of the SCAMPER Strategy on the Acquisition of Scientific Concepts among Fourth-Grade Primary Students in Science. This study examined the effect of the SCAMPER strategy on fourth-grade primary students' acquisition of scientific concepts in science. Its significance lies in the need to modernize science teaching methods in line with twenty-first-century requirements and to foster learners' creative thinking (Royce, 2016, p. 22). A quasi-experimental design with two equivalent groups was used. The sample comprised 60 fourth-grade students from a school in Salah al-Din Governorate, Ashur Education Department (2025–2026), divided into an experimental group (n = 30, taught using SCAMPER) and a control group (n = 30, taught using the conventional method). The groups were equated for age, intelligence, prior knowledge, and parental educational level, consistent with prior research (Laibi & Ibrahim, 2022, p. 295). Moreover, a 30-item multiple-choice achievement test on scientific concepts was developed. Validity was established through expert review, and reliability was calculated at 0.87 using the Kuder–Richardson (KR-20) formula. Results showed a statistically significant difference (p ≤ 0.05) favoring the experimental group, confirming SCAMPER's effectiveness in enhancing scientific concept acquisition — consistent with recent findings (Yüzüak & Recepkethüda, 2025, p. 12; Polat & Yılayaz, 2026, p. 160). The study recommends training teachers in SCAMPER, integrating it into teacher-preparation curricula, and extending research to other educational levels.
This study explores high school mathematics teachers’ perceptions of visual proofs, or Proofs Without Words (PWWs). Using a mixed-methods design combining semi-structured interviews (n = 8) and questionnaires (n = 122), we examined (1) teachers’ acceptance of visual proofs as valid, (2) their perceptions of colleagues’ likely evaluations, and (3) their preferences regarding classroom use. Interviews first revealed two distinct levels of acceptance: Unconditional Acceptance, in which the student-produced visual proof was judged sufficient as presented, and Explanation-required Acceptance, in which teachers accepted the proof but preferred it to be accompanied by a verbal explanation. In questionnaire responses, 40
This study investigates the state-like versus trait-like nature of self-efficacy among 1,767 Chinese eighth-graders in the context of mathematical problem-posing and problem-solving. The investigation utilized problem-posing and problem-solving tasks developed by Cai et al., alongside task-specific self-efficacy instruments based on Bandura’s social cognitive theory. Using both Linear Regression and Generalized Additive Models, the analysis revealed that the correlation between self-efficacy and performance was consistently stronger for problem-solving than for problem-posing. A key finding was that task-specific self-efficacy was not always the strongest predictor. Specifically, self-efficacy for posing easy problems emerged as the most robust predictor for all levels of problem-posing performance. Conversely, self-efficacy for solving easy problems was the strongest predictor only for easy and moderate problem-solving tasks. These results suggest that self-efficacy exhibits a “State-Trait Duality,” simultaneously functioning as a temporary state shaped by situational factors and a stable trait consistent across tasks. This dualistic framework provides a more nuanced understanding of how self-efficacy influences students’ mathematical engagement and achievement.
Professional noticing, encompassing attending to, interpreting, and responding to students’ understanding, is a vital skill for effective mathematics teaching. This study investigated how novice and expert primary school teachers developed instrumental relationships with fractions learning trajectory (LT) knowledge during professional noticing processes. Data were collected from 64 participants (30 novice teachers and 34 expert teachers) through tasks requiring them to analyse students’ fractional reasoning strategies and propose developmentally appropriate instructional activities. Results revealed that expert teachers exhibited significantly higher skills in interpreting and responding to students’ understanding compared to novice teachers, while showing minimal differences in attending capabilities. The study identified four levels of instrumental genesis—artifact role, using specific LT components, partial instrumentation, and full instrumentation—revealing that expert teachers consistently achieved higher levels of LT transformation from theoretical artifacts to practical instruments. Expert teachers demonstrated instrumental resilience, maintaining stable performance across varying cognitive demands, while novice teachers showed systematic regression as task complexity increased. These findings demonstrated that expertise enables robust instrumental relationships with theoretical frameworks, suggesting that teacher education programs must support systematic instrumental genesis rather than simple framework exposure to develop effective professional noticing capabilities.
Activities designed to prepare pre-service teachers (PSTs) to lead high-quality mathematical discussions are a crucial element of teacher education. However, the research literature presents teacher education activities that are designed in a wide variety of ways to support PSTs effectively. This study synthesizes research to map and characterize teacher education activities focused on preparing PSTs to lead high-quality mathematical discussions and the discussion-leading practices these activities aim to develop. Studies were identified through searches in Scopus, Web of Science, and Education Research Complete, and selected based on inclusion criteria focused on mathematics teacher education and PSTs’ preparation to lead discussions. Data were analyzed using an iterative thematic synthesis approach informed by our conceptual framework. The findings, based on 23 studies, indicate that research has emphasized teacher education activities supporting PSTs in analyzing or planning for ambitious discussion-leading practices, although descriptions of what PSTs are expected to do during these classroom practices vary in specificity. A notable gap in the literature concerns limited attention given to how teacher education activities provide opportunities for PSTs to enact discussion-leading practices. While many studies report on PSTs’ opportunities to analyze or plan for discussions, fewer examine how they gain experience with the interactive, in-the-moment aspects of leading mathematical discussions. Together, these findings clarify how the literature has conceptualized and described teacher education activities and the discussion-leading practices they aim to support, as well as relations between them, offering insights for designing learning opportunities that better support PSTs in enacting high-quality mathematical discussions.
Socioscientific issues (SSIs) are recognized as a vital approach for developing scientific literacy. Negotiating different opinions can enhance students’ ability to address SSIs by considering the complexity of both the scientific dimensions and social factors. However, few studies have explored how students handle differing opinions to work toward a consensus that integrates multiple perspectives into the SSI discussion process. To bridge this gap, this qualitative study investigates the negotiation processes among 162 eleventh-grade students from three classes, complemented by follow-up interviews with a sample of five students. Data sources included worksheets (indicating student thinking), audio recordings of student group discourse, and semi-structured student interviews. The study identifies four key negotiation strategies employed by students:1) integrative negotiation (resolving dual-value conflicts through threshold compromises), 2) conditional negotiation (refining positions via pragmatic evidence-based conditions), 3) implicit negotiation (building tacit consensus through collaborative concept-defining), and 4) reverse negotiation (consolidating shared opposition before addressing counterarguments). These strategies are operationalized through specific behavioral moves (e.g., clarifying concepts, adding information, qualifying boundaries) and reveal that consensus building relies on negotiating evidence, strengthening logical reasoning, and integrating interdisciplinary perspectives. This study contributes practical insights for guiding student-centered SSI discussions, emphasizing the need to scaffold negotiation moves (e.g., teaching students to clarify conflicts or propose conditional solutions) to help them integrate diverse standpoints, make informed decisions, and ultimately foster scientific literacy.
Verbal Probability Expressions (VPEs), such as “likely” or “rarely,” are often used to communicate scientific information that is uncertain and are often preferred over numerical expressions despite a higher risk of misinterpretation (mode preference paradox). Prior research has focused on adult interpretations of VPEs, with only few studies focusing on how young people understand them. This study investigates how secondary school students in England interpret 29 common VPEs using a slider-based scale (0–100
Integrated STEM education (iSTEMe) is widely promoted as a key innovation for enhancing scientific and technological literacy. However, a critical examination of the literature reveals that these claims remain largely unsubstantiated. Our analysis identifies three structural weaknesses: (1) lack of consensus on its conceptualization and operationalization; (2) limited empirical evidence supporting its effectiveness compared to non-integrated, disciplinary approaches; and (3) a persistent disconnect from classroom realities. Despite these limitations, continued iSTEMe promotion appears driven more by political and policy agendas than by robust educational evidence. Consequently, we argue that, rather than a transformative innovation, iSTEMe risks becoming a pedagogical chimera—an idealized approach fraught with conceptual and methodological complexities that inherently exceed the practical conditions required for successful implementation. We therefore call for greater caution among policymakers and researchers, emphasizing the need for robust theoretical frameworks, stronger empirical evidence, well-grounded teacher education, and teachers’ active involvement in educational reforms.
This study examines how computational thinking (CT) and mathematical problem solving (PS) appear together in primary education through unplugged activities. Using Turing Tumble, a gravity-driven mechanical computer, thirty 9 to 10-year-old students organised into 10 triads engaged in a classroom teaching experiment aimed at fostering CT skills and PS strategies. We conducted a mixed-methods analysis of video recordings and student workbooks, supported by detailed case study excerpts and still frames of the problem-resolution process with the machine, capturing students’ observable actions and strategic decisions. Findings reveal that students relied heavily on iterative testing and debugging rather than explicit planning, suggesting that tangible tasks encourage exploratory, action-oriented reasoning. A taxonomy of testing behaviours is proposed, distinguishing mechanical, kinaesthetic, algorithmic partial, and algorithmic complete testing. Co-occurrence analysis shows strong links between Polya´s looking back phase and testing/debugging, evidencing how metacognitive reflection manifests through hands-on verification. This study provides empirical evidence that unplugged tasks can connect CT and mathematical PS by linking strategic phases of problem solving with observable CT actions. Implications for instructional design and assessment in early CT integration are discussed.
This study utilized Item Response Theory (IRT) and Computerized Adaptive Testing (CAT) techniques to develop and preliminarily evaluate a multidimensional Mathematics Competence Assessment and Diagnosis (MCAD). The research consisted of three studies: First, we followed a five-step process to construct the item bank, which included Item Construction, Booklet Design, Participants, Procedure, and Data Analysis. Second, we conducted a simulation study to assess measurement precision and testing efficiency. The results indicated that MCAD’s measurement precision exceeded that of unidimensional CAT and random administration of items, requiring less than 33
This study investigates the impact of GeoGebra-assisted collaborative learning on students' understanding of function graphs. Function graphs are fundamental in mathematics education, yet many students struggle to grasp the relationships between variables, primarily due to traditional teaching methods that focus on procedural skills rather than conceptual understanding. To address this challenge, the study incorporates GeoGebra, a dynamic mathematics software, alongside collaborative learning strategies. The research utilizes a quasi-experimental design involving high school students who had previously struggled with function graphs. The results demonstrate that the experimental group, which engaged in GeoGebra-assisted collaborative learning, showed a significant improvement of 27% in their post-test scores, compared to just a 6% improvement in the control group using traditional methods. The study highlights the effectiveness of GeoGebra in fostering a deeper conceptual understanding of mathematical functions by enabling students to visualize and manipulate graphs interactively. Additionally, collaborative learning encouraged peer interaction, reinforcing the learning process and promoting better problem-solving skills. The findings suggest that combining interactive tools like GeoGebra with collaborative learning techniques can enhance students’ mathematical comprehension, leading to improved engagement and performance in mathematics education.
