The use of genetic algorithm to optimize quantitative learner's motivation model

Paweł Lempa,

Michal Ptaszynski,

Fumito Masui

The paper presents a method of optimizing Quantitative Learner’s Motivation Model with the use of genetic algorithm. It is focused on optimizing the formula for prediction of learning motivation by means of different weights for three values: interest, usefulness in the future and satisfaction. For the purpose of this optimization, we developed a C++ library that implements a genetic algorithm and an application in C# which uses that library with data acquired from questionnaires enquiring about those three elements. The results of the experiment showed improvement in the estimation of student’s learning motivation.
Słowa kluczowe: optimization, genetic algorithm, Quantitative Learner’s Motivation Model

[1] Nobuta Y., Masui F., Ptaszynski M, Modeling Learning Motivation of Students Based on Analysis of Class Evaluation Questionnaire, Technical Transactions, 2-M/2015, 193–201.
[2] Ekbal, A., Saha, S., Simultaneous feature and parameter selection using multiobjective optimization: application to named entity recognition International Journal of Machine Learning and Cybernetics, Volume 7, Issue 4, 2016, 597–611.
[3] Calkin S. Montereo, Araki K., Unsupervised language independent genetic algorithm approach to trivial dialogue phrase generation and evaluation. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, Vol. 4592, 2007, 388–394.
[4] Manurung R., Ritchie G., Thompson H., Using Genetic Algorithms to Create Meaningful Poetic Text, Journal of Experimental & Theoretical Artificial Intelligence, Vol. 24, Issue 1, 2012, 43–64.
[5] Manurung H.M., An evolutionary algorithm approach to poetry generation, Doctoral Thesis, Institute for Communicating and Collaborative Systems, School of Informatics University of Edinburgh, 2003.
[6] Araki K., Kuroda M., Generality of Spoken Dialogue System using SeGA-IL for Different Languages, Systems and Computers in Japan, Vol. 35, No. 12, 2004.
[7] McIntyre N., Lapata M., Plot Induction and Evolutionary Search for Story Generation, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, Stroudsburg, 2010, 1562–1572.
[8] Goldberg DE, Holland JH., Genetic algorithms and machine learning, Machine learning 3(2), 1988, 95–99.
[9] Langdon WB, Poli R. Foundations of genetic programming, Springer, 2002.
[10] Melanie M. An introduction to genetic algorithms, Cambridge, Massachusetts London, England, Fifth printing 1999.
[11] Ladd SR. Genetic algorithms in C++, Hungry Minds, Incorporated, 1995.
[12] Deslauriers W.A., Asexual Versus Sexual Reproduction in Genetic Algorithms, Carleton University.
[13] Wu Ch. H., Tzeng G.H., Goo Y.J., Fang W.C., A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy, Expert Systems with Applications, 2007, Vol. 32, Issue 2, 397–408.
[14] Keller J.M., Kopp T., Application of the ARCS model of motivational design,
M. Reigluth (Ed.), Instructional theories in action: Lessons illustrating selected theories and models, Lawrence Erlbaum Associates, USA 1987.
[15] Keller J.M., Suzuki K., Use of the ARCS motivation model in courseware design (Chapter 16), [in:] D.H. Jonnasen (Ed.), Instructional designs for microcomputer courseware. Lawrence Erlbaum Associates, USA 1988.