Qiaoping Zhang

Teachers’ instructional decisions reflect underlying pedagogical values, yet limited research examines how teachers recognise and interpret value differences in classroom practice. This study investigates how nine experienced Malaysian secondary mathematics teachers interpreted mathematics educational values (MEV) tensions through critical incident narratives. Data from online semi-structured interviews were analysed in two stages: first, extracting critical incidents from narratives, and second, coding underlying MEV tensions. Analysis identified six key MEV differences: application versus computation, enjoyment versus hardship, ability versus effort, exposition versus exploration, compliance versus contribution, and information communication technology (ICT) versus paper-and-pen. Findings indicate that making MEV tensions explicit through critical incident narratives can enhance teachers’ reflective practice by surfacing previously implicit value conflicts. This study advances the mathematics education literature by operationalising pedagogical values through narrative analysis and demonstrating the potential of the critical incident methodology to support values-informed professional practice. Implications for teacher preparation and professional development are discussed.

Technology offers powerful opportunities to support mathematics teachers in developing the critical ability to notice and respond to student thinking. As research at this intersection expands, a systematic mapping of the field is needed to clarify its intellectual structure and developmental trajectory. This study presents a mixed-methods systematic review of 90 articles published between 2004 and 2024 in the Web of Science and Scopus databases, combining bibliometric analyses with qualitative thematic analyses. Findings indicate that the field has undergone a two-phase evolution: an initial period dominated by research using standard video, followed by a recent and rapid diversification into emerging technologies such as 360° video, extended reality (XR), and artificial intelligence (AI). While the feasibility and promise of these technologies have been well demonstrated, the next stage of research requires longitudinal investigations of sustained impact, comparative studies to identify the mechanisms by which different tools influence teacher noticing, and critical analyses of equity and access.

The rapid integration of artificial intelligence (AI) has led to the increasing use of AI-based intelligent technology in various disciplines and educational contexts. Yet, little is known about how K–12 students accept and use such technologies in mathematics learning. This study examined secondary students’ adoption of AI-based intelligent technology through an extended Technology Acceptance Model (TAM) that incorporated subjective norms, perceived usefulness, perceived ease of use, behavioral intention, and actual use. A mixed-methods design was employed, combining survey data from 483 Chinese secondary students with followed semi-structured interviews with 12 participants. Confirmatory factor analysis and structural equation modelling were employed to analyze the quantitative data. Results revealed that subjective norms were the strongest predictor of behavioral intention, followed by perceived usefulness, while perceived ease of use had a weaker yet significant effect. Behavioral intention significantly influenced actual use, and qualitative findings further validated the extended TAM. This study provides new insights into students’ acceptance of AI-based intelligent technology in mathematics education, highlighting the pivotal role of subjective norms and perceived usefulness in shaping students’ adoption. Implications are discussed in the end, aiming to advance the effective integration of AI-based intelligent technology in K–12 schools.

This study investigates how novice secondary mathematics teachers notice students’ functional thinking and examines the relationship between their mathematical task performance (MTP) with their noticing skills. A total of 86 novice secondary mathematics teachers in China participated in the study by completing a questionnaire that assessed their MTP and professional noticing skills in analyzing written artifacts of students’ solutions about linear functions. Thematic analysis was used to code and interpret the noticing responses. The findings reveal that novice mathematics teachers demonstrate relatively strong attending and responding skills, but face challenges in interpreting students’ functional thinking. Furthermore, teachers with medium-level MTP exhibited stronger attending and responding skills compared to their high-level peers, though the difference was not statistically significant, and performed significantly better than their low-level peers. Teachers with low-level MTP showed the weakest results across all noticing dimensions. The findings provide evidence-based insights into teachers’ professional noticing of students’ functional thinking and highlight how teachers’ MTP may influence their noticing practices.

Purpose: This study explores the values that Hong Kong students associate with success in mathematics, aiming to improve educational practices that align with their needs and aspirations. By identifying key attributes students prioritize, the research seeks to bridge gaps between teaching methods and learner perspectives. It highlights the need to go beyond performance metrics by incorporating student-identified values into curriculum design and teaching strategies. As part of the WIFI Study Project, the findings aim to help educators and policymakers create inclusive, motivation-driven learning environments that reflect students' self-defined pathways to success in mathematics. Design/methodology/approach: The study involved 1,375 Hong Kong secondary students, who provided open-ended responses about the attributes they considered important for success in mathematics. The qualitative data were analysed using the Values Automatic Sorting Algorithm (VASA), a tool that groups semantically similar responses into distinct categories. This method allowed large-scale qualitative analysis without predefined categories, offering a contrast to traditional survey or Likert-scale approaches. The responses were processed iteratively to ensure reliability, resulting in 57 unique student-identified values. By prioritizing student voices, the approach allowed emergent themes to surface naturally, capturing perspectives often missed by structured or quantitative methods. Findings: The analysis identified 57 student-identified values, with the ten most common being smartness, effort, nutrition, content knowledge, algorithm, ability, stimulation, understanding, recall and thinking. Compared to previous studies using fixed-response formats, this approach uncovered new values (e.g. nutrition) and emphasized cognitive-metacognitive traits (e.g. recall, thinking). The findings highlighted gaps between student priorities and traditional educational focuses, such as procedural mastery. Effort and smartness were the most emphasized, reflecting cultural values and beliefs about self-efficacy. VASA proved effective in processing unstructured data, revealing a richer and more diverse set of student values than conventional quantitative methods. Research limitations/implications: This study highlights the importance of qualitative, student-centred methods in exploring values, challenging the reliance on predetermined frameworks in mathematics education. VASA's success in large-scale qualitative analysis offers a replicable model for studying learner perspectives in other disciplines. Practically, the findings encourage educators to consider values like nutrition and stimulation, which relate to holistic well-being, and to balance procedural rigor with metacognitive development. The results also call for culturally responsive teaching, particularly in contexts that emphasize innate ability. Future research could examine how aligning teaching practices with student-identified values impacts engagement and achievement over time. Originality/value: This study introduces VASA, an innovative tool for analysing unstructured qualitative data on a large scale, departing from traditional quantitative value assessments. It uniquely identified 57 student-generated values, including overlooked factors like nutrition, expanding the conversation about mathematics learning beyond cognitive skills. By using open-ended methods, the study revealed nuanced and context-specific priorities that fixed-response surveys often miss. Centring on student voices, it challenges instructor- or theory-driven frameworks and provides evidence for rethinking what “success” means in mathematics education. This approach advances methodological diversity in values research and emphasizes the need for learner-informed strategies in teaching and curriculum design.