Technology and mathematics education

Konold, Cliff; Lehrer, Richard

doi:10.4324/9780203930236.ch4

Cited by 54 publications

(4 citation statements)

References 48 publications

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“…106)." They start with constructing measures for simple tasks, inventing ways to display data and measure data distribution, and engaging in collective critiques over these inventions (Konold & Lehrer, 2008;Lehrer, 2017;Lehrer et al, 2011).…”

Section: Learning About Measurement and Uncertainty: The Contexts Mattermentioning

confidence: 99%

“…These vary in their modeling focus and levels of integration with scientific sensemaking. Some put emphasis on the experience of “visualizing, measuring, and modeling variability (Lehrer et al, 2020, p. 106).” They start with constructing measures for simple tasks, inventing ways to display data and measure data distribution, and engaging in collective critiques over these inventions (Konold & Lehrer, 2008; Lehrer, 2017; Lehrer et al, 2011). Through these activities, students learn to understand and model measurement as composed of signal and noise, and learn to distinguish signal and noise by drawing on their knowledge of the distribution of the data.…”

Section: Introductionmentioning

confidence: 99%

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Emergent learning about measurement and uncertainty in an inquiry context: A case from an elementary classroom

Tang,

Shu,

Wei

et al. 2023

Science Education

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Students often learn about measurement uncertainty as an isolated topic, with a focus on generalizable strategies to manage uncertainty in scientific investigation. In this study, we report and analyze a case of emergent learning about measurement and uncertainty, in which students in a Chinese elementary school science class explored and reconciled discrepancies in hypotheses by constructing and using measures and making inferences. Adopting a model‐based view of measurement, we show that when allowed to take on emergent measurement uncertainty while inquiring into causes for phenomena, late elementary students with no prior experience can engage in sophisticated reasoning characterized by a variety of theoretical modeling practices. Developing and aligning models of the phenomenon, the measure and the measurement data supported students in constructing an intuitive solution to their discrepancies. Our analysis also identified (1) a pattern of thinking and some common assumptions students adopted in their modeling practice that were productive, and (2) contextual elements affording and constraining emergent learning on measurement and uncertainty. In our discussion, we reflect on the educational potential of adopting a model‐based account of measurement and of treating measurement and uncertainty as integrated into investigative practice. We also discuss the necessary contexts for realizing the potential of the model‐based account.

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Section: Learning About Measurement and Uncertainty: The Contexts Mattermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Emergent learning about measurement and uncertainty in an inquiry context: A case from an elementary classroom

Tang,

Shu,

Wei

et al. 2023

Science Education

View full text Add to dashboard Cite

show abstract

“…There is ample research evidence (e.g. Gil & Ben-Zvi, 2011;Hall, 2011;Ireland & Watson, 2009;Konold & Lehrer, 2008;Meletiou-Mavrotheris, 2003;Paparistodemou & Meletiou-Mavrotheris, 2008;Meletiou-Mavrotheris & Paparistodemou, 2015) that use of dynamic statistics software promotes the development of important data literacy and analytical skills, and of more coherent mental models of core mathematical concepts (e.g. theoretical probability, experimental probability, relative frequency, percent, fraction, measures of center and spread, algebraic relationships, line of best fit, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Early Statistical Reasoning

Kleanthous

Meletiou-Mavrotheris

2016

International Journal of Information Communication Technologies and Human Development

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This paper explores the potential of dynamic statistics software for supporting the early teaching and learning of statistical and probabilistic concepts integrated within the mathematics curriculum. It shares the experiences from a case study that implemented a data-driven approach to mathematics instruction using the dynamic data-visualization software InspireData©, an educational package specifically designed to meet the learning needs of students in the middle and high school grades (Grades 4-12). The authors report on how a group of fourteen (n=14) Grade 4 (about 9-year-old) students used the affordances provided by the dynamic learning environment to gather, analyze, and interpret data, and to draw data-based conclusions and inferences. Findings from the study support the view that mathematics instruction can promote the development of learners' statistical reasoning at an early age, through an informal, data-based approach. They also suggest that the use of dynamic statistics software has the potential to enhance statistics instruction by scaffolding and extending young students' stochastical and mathematical reasoning.

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“…This suggests a need for collective as well as individual forums for developing measure. The larger orientation toward data in which students participated was one we term data modeling (Lehrer & Romberg, 1996;Lehrer & Schauble, 2004;Konold & Lehrer, 2008), illustrated in Fig. 2.…”

Section: Introductionmentioning

confidence: 99%

Developing conceptions of statistics by designing measures of distribution

Lehrer

Kim

Jones

2011

ZDM Mathematics Education

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Students often learn procedures for measuring, but rarely do they grapple with the foundational conceptual problem of generating and validating coordination between a measure and the phenomenon being measured. Coordinating measures with phenomenon involves developing an appreciation of the objects and relations in each as well as establishing their mutual correspondence. We supported students' developing conceptions of statistics by positioning them to design measures of center and of variability for distributions that they had generated through repeated measure of a length. After students invented and explored the viability of their measures individually, they participated in a public (whole-class conversation) forum featuring justification and reflection about the viability of their designed measures. We illustrate how individual invention enticed students to attend to, and to make explicit, characteristics of distribution not initially noticed or known only tacitly. Conceptions of statistics and of relevant characteristics of distribution were further expanded as students justified and argued about the utility and prospective generalization of particular inventions. Teachers supported student learning by highlighting prospective relations between characteristics of measures and characteristics of distribution as they emerged during the course of activity in each setting.

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Technology and mathematics education

Cited by 54 publications

References 48 publications

Emergent learning about measurement and uncertainty in an inquiry context: A case from an elementary classroom

Emergent learning about measurement and uncertainty in an inquiry context: A case from an elementary classroom

Early Statistical Reasoning

Developing conceptions of statistics by designing measures of distribution

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