In recent years in New Zealand, there has been a policy shift towards schools ‘localising’ the national curriculum to align with the context, aspirations, and knowledge of the local community and student population. In relation to mathematics education, this requires educators to understand and value the mathematical connections between diverse students’ funds of knowledge and use these to develop mathematical tasks. This article draws on interview responses from a case study of eight teachers from one low socio-economic, culturally diverse school to investigate their initial perceptions and actions to develop an appropriate localised mathematics curriculum drawing on diverse students’ funds of knowledge. The findings indicate that teachers viewed it as important to use real and relevant contexts in mathematics teaching. Interview responses indicated that both students and their families were seen as important sources of information. However, there were challenges for teachers to recognise students’ funds of knowledge related to mathematics beyond schooling or generic experiences.
The COVID-19 pandemic brought with it a new way of being in a changed and uncertain world. Aotearoa/New Zealand took a well-being approach and in turn, we share the positive outcomes which resulted for some low socio-economic schools and communities in relation to teacher learning and relationships with families. In this article, we report on how teachers and schools connected with diverse students and their families during the period of remote learning. We draw on the responses from 20 teachers and school leaders who participated in interviews. Following the wider government focus, schools took a well-being first approach which led to increased connections and positive home/school relationships. The results highlight how a disruptive event such as COVID-19 can also be a time to focus on strengths of diverse communities and gain insights. We demonstrate that while focusing on mathematics, teachers and school leaders gained insights related to their students’ funds of knowledge and saw opportunities for learning for students, parents, and the teachers themselves.
The COVID-19 pandemic has caused new ways of doing and being, both in education systems and beyond across the world. In the context of Aotearoa/New Zealand, the widely supported government approach focused on the well-being of the nation with a position that saving lives was more important than maintaining an open economy. As researchers and educators, we supported teachers as they worked with their students in their home settings. This provided us with an opportunity to explore a vision of a reinvented system of mathematics education beyond institutional and formal structures of schools. In this chapter, we present the analysis of the responses from 24 educators mainly from low socioeconomic urban settings as they reflected on how they enacted mathematics teaching and learning during the lockdown while connecting with students and their families as well as their subsequent learning from this experience. Results highlighted that the mathematical learning of students went beyond what was accessed by digital means and included parents drawing on rich everyday opportunities. A key finding was that by supporting and privileging the well-being of students and communities, the connections and relationships between educators and families were enhanced.
This study examines the use of counterexamples for supporting the development of students’ algorithmic thinking. Working from the premise that some counterexamples are more effective than others for the development of generalized algorithms, the study proposes distinctions between counterexamples in relation to the iterative refinement of student-invented algorithms. Furthermore, the study identifies some factors that may influence differences among counterexamples. Using task-based interviews, data were collected from 23 undergraduate students working in pairs ( n = 8) and individually ( n = 7) on three algorithmatizing tasks. From a thematic analysis of the data, two illustrative cases are presented to show how and why different counterexamples might bring about particular revisions in students’ algorithms. The two illustrative cases highlight two types of counterexamples— set-of-instructions-changing ( SoI-changing) and domain-of-validity-narrowing ( DoV-narrowing)—and their influencing factors. Implications of the findings are discussed with respect to existing literature, further research, and teaching.
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