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.
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.
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.
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.
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.
