Discriminating "Signal" and "Noise" in Computer-Generated Data

Theodosia Prodromou; David Pratt

Discriminating "Signal" and "Noise" in Computer-Generated Data

Theodosia Prodromou
, David Pratt

STEM Education

Research output: Contribution to journal › Conference article › peer-review

Abstract

This paper presents a case study of a group of students (age 14-15) as they use a computer-based domain of stochastic abstraction to begin to view spread or noise as dispersion from the signal. The results show that carefully designed computer tools, in which probability distribution is used as a generator of data, can facilitate the discrimination of signal and noise. This computational affordance of distribution is seen as related to classical statistical methods that aim to separate main effect from random error. In this study, we have seen how signal and noise can be recognised by students as an aspect of distribution. Students' discussion of computer-generated data and their sketches of the distribution express the idea that more variation is centred close to the signal, and less variation is located further away from it.

Original language	English
Pages (from-to)	57-64
Journal	Proceedings of the Thirty Fourth International Conference for the Psychology of Mathematics Education
Volume	4
Publication status	Published - 2010
Event	PME 34: 34th Annual Conference of the International Group for the Psychology of Mathematics Education - Belo Horizonte, Brazil Duration: 18 Jul 2010 → 23 Jul 2010

Keywords

Stochastic Analysis and Modelling
Statistical Theory
Probability Theory

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title = "Discriminating {"}Signal{"} and {"}Noise{"} in Computer-Generated Data",

abstract = "This paper presents a case study of a group of students (age 14-15) as they use a computer-based domain of stochastic abstraction to begin to view spread or noise as dispersion from the signal. The results show that carefully designed computer tools, in which probability distribution is used as a generator of data, can facilitate the discrimination of signal and noise. This computational affordance of distribution is seen as related to classical statistical methods that aim to separate main effect from random error. In this study, we have seen how signal and noise can be recognised by students as an aspect of distribution. Students' discussion of computer-generated data and their sketches of the distribution express the idea that more variation is centred close to the signal, and less variation is located further away from it.",

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author = "Theodosia Prodromou and David Pratt",

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journal = "Proceedings of the Thirty Fourth International Conference for the Psychology of Mathematics Education",

publisher = "International Group for the Psychology of Mathematics Education (IGPME)",

note = "PME 34: 34th Annual Conference of the International Group for the Psychology of Mathematics Education ; Conference date: 18-07-2010 Through 23-07-2010",

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TY - JOUR

T1 - Discriminating "Signal" and "Noise" in Computer-Generated Data

AU - Prodromou, Theodosia

AU - Pratt, David

PY - 2010

Y1 - 2010

N2 - This paper presents a case study of a group of students (age 14-15) as they use a computer-based domain of stochastic abstraction to begin to view spread or noise as dispersion from the signal. The results show that carefully designed computer tools, in which probability distribution is used as a generator of data, can facilitate the discrimination of signal and noise. This computational affordance of distribution is seen as related to classical statistical methods that aim to separate main effect from random error. In this study, we have seen how signal and noise can be recognised by students as an aspect of distribution. Students' discussion of computer-generated data and their sketches of the distribution express the idea that more variation is centred close to the signal, and less variation is located further away from it.

AB - This paper presents a case study of a group of students (age 14-15) as they use a computer-based domain of stochastic abstraction to begin to view spread or noise as dispersion from the signal. The results show that carefully designed computer tools, in which probability distribution is used as a generator of data, can facilitate the discrimination of signal and noise. This computational affordance of distribution is seen as related to classical statistical methods that aim to separate main effect from random error. In this study, we have seen how signal and noise can be recognised by students as an aspect of distribution. Students' discussion of computer-generated data and their sketches of the distribution express the idea that more variation is centred close to the signal, and less variation is located further away from it.

KW - Stochastic Analysis and Modelling

KW - Statistical Theory

KW - Probability Theory

UR - http://pme34.lcc.ufmg.br

UR - http://trove.nla.gov.au/work/38986776

M3 - Conference article

VL - 4

SP - 57

EP - 64

JO - Proceedings of the Thirty Fourth International Conference for the Psychology of Mathematics Education

JF - Proceedings of the Thirty Fourth International Conference for the Psychology of Mathematics Education

T2 - PME 34: 34th Annual Conference of the International Group for the Psychology of Mathematics Education

Y2 - 18 July 2010 through 23 July 2010

ER -

Discriminating "Signal" and "Noise" in Computer-Generated Data

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Keywords

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