Evolution of an adaptive mathematics learning game for lower primary students
Created by W.Langdon from
gpbibliography.bib Revision:1.7615
 @Misc{Ismail:2015,

author = "Siti Afiqah Ismail and Jason Teo Tze Wi",

title = "Evolution of an adaptive mathematics learning game for
lower primary students",

year = "2015",

keywords = "genetic algorithms, genetic programming",

URL = "http://www.ums.edu.my/fki/index.php/en/evolutionofanadaptivemathematicslearninggameforlowerprimarystudents",

URL = "http://www.ums.edu.my/fki/files/EVOLUTION_OF_AN_ADAPTIVE_MATHEMATICS_LEARNING_GAME_FOR_LOWER_PRIMARY_STUDENTS_new.pdf",

size = "6 pages",

abstract = "The newly coined term courseware was actually derived
from the words course and software. The courseware that
is available nowadays has been added with the
adaptiveness values. These adaptive elements have been
implemented by researchers in various ways. Some are
using fuzzy, neuralnetwork or even metaheuristics to
implement the adaptive elements in to their courseware
systems. By using these approaches, they apply the
adaptiveness by optimizing the learning path. In this
research, the learning path will be optimized based on
the learners' understanding level of the concept being
learnt. This approach is commonly known as
personalization. In this project, the Evolutionary
Algorithm approach is selected as the optimization
method. The EA used in this project is Genetic
Programming. Instead of evolving the separate
representations to the solution, Genetic Programming
evolves the solution itself. Genetic Programming
usually evolves computer programs instead of evolving
the solution representations found in Genetic
Algorithms. Nonetheless, the process of Genetic
Programming is still similar to Genetic Algorithms.
Apart from implementing GP into the learning system,
this research uses the basic user interface design for
designing an interface of the mathematics learning
game. Since the main audience of the game is young
children, some interface design elements especially
suited for young children have to be taken into
account. In this research, 4 experiments had been
conducted to test the algorithms implemented. In
comparison, experiment 2 yielded better results
compared to other experiments. In experiment 2, the
level was set to be fixed, while in the other
experiments, the level changing parameter is set to be
random. In experiments 1, 3 and 4, the findings show
that the random changing level is unpredictable. Some
level jumps are too high and some level jumps are too
low. In general, the overall outcomes of this research
demonstrate that EAs can be a viable approach in terms
of implementing adaptive courseware at least in the
realms of teaching mathematics to young children.",

notes = "
...new.pdf gives rough outline of table of contents
See also: THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENT FOR THE DEGREE OF BACHELOR OF COMPUTER
SCIENCE (SOFTWARE ENGINEERING) WITH HONOURS",
 }
Genetic Programming entries for
Siti Afiqah Ismail
Jason Teo Tze Wi
Citations