A GCSE maths tutoring game using neural networks

William Lawrence; Jenny Carter; Samad Ahmadi

doi:10.1109/ICEGIC.2010.5716877

A GCSE maths tutoring game using neural networks

William Lawrence, Jenny Carter, Samad Ahmadi

De Montfort University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper investigates the use of neural networks to provide a challenging environment to motivate students of mathematics in further investigation of mathematical concepts. The research focuses on areas of shape, but similar methods could be used for a variety of mathematical topics. The paper presents a game in which a back-propagation neural network is trained by the player to compare areas of mathematical shapes. The original prototype in MATLAB is presented. A demonstration of the idea of a neural network as a opponent using the Python Programming Language further expands on this original work. The results show that a neural network can be used in a variety of ways to support students of differing levels of ability.

Original language	English
Title of host publication	2010 2nd International IEEE Consumer Electronic Society's Games Innovation Conference, ICE-GIC
Publisher	IEEE
Number of pages	7
ISBN (Print)	9781424471782
DOIs	https://doi.org/10.1109/ICEGIC.2010.5716877
Publication status	Published - 22 Feb 2011
Externally published	Yes
Event	2nd International IEEE Consumer Electronic Society Games Innovation Conference - Hong Kong, China Duration: 21 Dec 2010 → 23 Dec 2010 Conference number: 2

Conference

Conference	2nd International IEEE Consumer Electronic Society Games Innovation Conference
Abbreviated title	ICE-GIC 2010
Country/Territory	China
City	Hong Kong
Period	21/12/10 → 23/12/10

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10.1109/ICEGIC.2010.5716877Licence: Unspecified

Cite this

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title = "A GCSE maths tutoring game using neural networks",

abstract = "This paper investigates the use of neural networks to provide a challenging environment to motivate students of mathematics in further investigation of mathematical concepts. The research focuses on areas of shape, but similar methods could be used for a variety of mathematical topics. The paper presents a game in which a back-propagation neural network is trained by the player to compare areas of mathematical shapes. The original prototype in MATLAB is presented. A demonstration of the idea of a neural network as a opponent using the Python Programming Language further expands on this original work. The results show that a neural network can be used in a variety of ways to support students of differing levels of ability.",

author = "William Lawrence and Jenny Carter and Samad Ahmadi",

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day = "22",

doi = "10.1109/ICEGIC.2010.5716877",

language = "English",

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booktitle = "2010 2nd International IEEE Consumer Electronic Society's Games Innovation Conference, ICE-GIC",

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address = "United States",

note = "2nd International IEEE Consumer Electronic Society Games Innovation Conference , ICE-GIC 2010 ; Conference date: 21-12-2010 Through 23-12-2010",

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AU - Carter, Jenny

AU - Ahmadi, Samad

N1 - Conference code: 2

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N2 - This paper investigates the use of neural networks to provide a challenging environment to motivate students of mathematics in further investigation of mathematical concepts. The research focuses on areas of shape, but similar methods could be used for a variety of mathematical topics. The paper presents a game in which a back-propagation neural network is trained by the player to compare areas of mathematical shapes. The original prototype in MATLAB is presented. A demonstration of the idea of a neural network as a opponent using the Python Programming Language further expands on this original work. The results show that a neural network can be used in a variety of ways to support students of differing levels of ability.

AB - This paper investigates the use of neural networks to provide a challenging environment to motivate students of mathematics in further investigation of mathematical concepts. The research focuses on areas of shape, but similar methods could be used for a variety of mathematical topics. The paper presents a game in which a back-propagation neural network is trained by the player to compare areas of mathematical shapes. The original prototype in MATLAB is presented. A demonstration of the idea of a neural network as a opponent using the Python Programming Language further expands on this original work. The results show that a neural network can be used in a variety of ways to support students of differing levels of ability.

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DO - 10.1109/ICEGIC.2010.5716877

M3 - Conference contribution

AN - SCOPUS:79952825197

SN - 9781424471782

BT - 2010 2nd International IEEE Consumer Electronic Society's Games Innovation Conference, ICE-GIC

PB - IEEE

T2 - 2nd International IEEE Consumer Electronic Society Games Innovation Conference

Y2 - 21 December 2010 through 23 December 2010

ER -

A GCSE maths tutoring game using neural networks

Abstract

Conference

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