Evolution of an adaptive mathematics learning game for lower primary students

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@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/evolution-of-an-adaptive-mathematics-learning-game-for-lower-primary-students",
  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, neural-network 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