Adaptive and Personalizing Learning Sequence Using Modified Roulette Wheel Selection Algorithm
Prior literature in the field of adaptive and
personalized learning sequence in e-learning have proposed and
implemented various mechanisms to improve the learning process
such as individualization and personalization, but complex to
implement due to expensive algorithmic programming and need of
extensive and prior data. The main objective of personalizing
learning sequence is to maximize learning by dynamically selecting
the closest teaching operation in order to achieve the learning
competency of learner. In this paper, a revolutionary technique has
been proposed and tested to perform individualization and
personalization using modified reversed roulette wheel selection
algorithm that runs at O(n). The technique is simpler to implement
and is algorithmically less expensive compared to other revolutionary
algorithms since it collects the dynamic real time performance matrix
such as examinations, reviews, and study to form the RWSA single
numerical fitness value. Results show that the implemented system is
capable of recommending new learning sequences that lessens time
of study based on student's prior knowledge and real performance
matrix.
[1] P. Brusilovsky, J. Vassileva, “Course Sequencing techniques for large
scale web-base education.” International Journal of Continuing
Engineering Education and Lifelong Learning, 13(1-2), 75-94.2003.
[2] United Nations (UNESCO). Personalized Learning: A New ICTEnabled
Education Approach. Retrieved April 13,
2013.http://iite.unesco.org/pics/publications/en/files/3214716.pdf.
[3] US Department of Education. Competency-based learning or
Personalized Learning. Retrieved April 10, 2013 at http://www.ed.gov/
oii-news/competency-based-learning-or-personalized-learning
[4] S. K. Robinson, “Personalizing Learning: What does this mean? How
can we work towards it? And why should we?”Retrieved April 9, 2013
from http://personalizinglearning.com/tag/defining-personalizedlearning/.
[5] Bray, K. McClaskey, “Personalization vs. Differentiation vs.
Individualization. Rethinking Learner”. Retrieved April 10, 2013.
[6] Chen, C. Liu, M. Chang, “Personalized curriculum sequencing utilizing
modified item response theory for web-based instruction”. Expert
Systems with Applications 30, 378-396. 2006.
[7] Quality Improvement Agency – UK. Effective practice in teaching and
learning: Improving own learning and performance. Retrieved January 1,
2014 from http://dera.ioe.ac.uk/7642/1/Improving%20Own%20
Learning%20and%20Performance.pdf.
[8] T. R. Guskey, “Closing Achievement Gaps: Revisiting Benjamin S.
Bloom’s Learning for Mastery”. Journal of Advanced Academics.19, 8-
31. 2007.
[9] F. Wang, “On extracting recommendation knowledge for personalized
web-based learning based on ant colony optimization with segmentedgoal
and meta-control strategies", Expert Systems with Applications, 39,
6446–6453.2012.
[10] Dwi, A. Basuki, “Personalized Learning Path of a Web-based Learning
System”. International Journal of Computer Applications 53(7):17-22.
Published by Foundation of Computer Science, New York, USA. 2012.
[11] M. Ballera, A. Musa, “Personalize eLearning System using Three
Parameters and Genetic Algorithms. In M. Koehler & P. Mishra (Eds.),
Proceedings of Society for Information Technology & Teacher
Education International Conference 2011 (pp. 569-574). Chesapeake,
VA: 2011.
[12] C. Chen, C. Liu, M. Chang, “Personalized curriculum sequencing
utilizing modified item response theory for web-based instruction”.
Expert Systems with Applications 30, 378-396. 2006.
[13] S. Hovakimyan, S. G. Sargsyan, “The Genetic Algorithms (GA) in Webbased
Learning Systems”. Proceedings of IASTED International
Conference on ACIT-Software Engineering (ACIT-SE 2005),
Novosibirsk, Russia, 2005.
[14] K. A. Papanikolou, M. Grigoriadou, “Towards new forms of knowledge
communication: the adaptive communication of a web-based learning
environment”. Computers and Education, 39, 333-360. 2002.
[15] P. C. Chang, C. Y. Lai, “A hybrid system combining self organizing
maps with case-based reasoning in wholsaler’s new release book
forecsating” . Expert Systems with Applications 29, 183-192. 2005.
[16] C. Chen, L. Lee-Hahn, Y. Chen, “Personalized e-learning system using
item response theory”. Expert Systems with Applications 44, 237-255.
2005
[17] E. Goldberg, Genetic Algorithms in Search, Optimization and Machine
Learning. Addison Wesley Longman, Inc. ISBN 0-201-15767-5. 1989.
[18] J. E. Baker, “Adaptive Selection Methods for Genetic Algorithms”.
Proceedings of International Conference on Genetic Algorithms and
their Applications.pp.101-111. 1985.
[19] R. Kumar, Jyotishree, “Blending Roulette Wheel Selection and Rank
Selection in Genetic Algorithms”. International Journal of Machine
Learning and Computing.Vol.2, No. 4. 2012.
[1] P. Brusilovsky, J. Vassileva, “Course Sequencing techniques for large
scale web-base education.” International Journal of Continuing
Engineering Education and Lifelong Learning, 13(1-2), 75-94.2003.
[2] United Nations (UNESCO). Personalized Learning: A New ICTEnabled
Education Approach. Retrieved April 13,
2013.http://iite.unesco.org/pics/publications/en/files/3214716.pdf.
[3] US Department of Education. Competency-based learning or
Personalized Learning. Retrieved April 10, 2013 at http://www.ed.gov/
oii-news/competency-based-learning-or-personalized-learning
[4] S. K. Robinson, “Personalizing Learning: What does this mean? How
can we work towards it? And why should we?”Retrieved April 9, 2013
from http://personalizinglearning.com/tag/defining-personalizedlearning/.
[5] Bray, K. McClaskey, “Personalization vs. Differentiation vs.
Individualization. Rethinking Learner”. Retrieved April 10, 2013.
[6] Chen, C. Liu, M. Chang, “Personalized curriculum sequencing utilizing
modified item response theory for web-based instruction”. Expert
Systems with Applications 30, 378-396. 2006.
[7] Quality Improvement Agency – UK. Effective practice in teaching and
learning: Improving own learning and performance. Retrieved January 1,
2014 from http://dera.ioe.ac.uk/7642/1/Improving%20Own%20
Learning%20and%20Performance.pdf.
[8] T. R. Guskey, “Closing Achievement Gaps: Revisiting Benjamin S.
Bloom’s Learning for Mastery”. Journal of Advanced Academics.19, 8-
31. 2007.
[9] F. Wang, “On extracting recommendation knowledge for personalized
web-based learning based on ant colony optimization with segmentedgoal
and meta-control strategies", Expert Systems with Applications, 39,
6446–6453.2012.
[10] Dwi, A. Basuki, “Personalized Learning Path of a Web-based Learning
System”. International Journal of Computer Applications 53(7):17-22.
Published by Foundation of Computer Science, New York, USA. 2012.
[11] M. Ballera, A. Musa, “Personalize eLearning System using Three
Parameters and Genetic Algorithms. In M. Koehler & P. Mishra (Eds.),
Proceedings of Society for Information Technology & Teacher
Education International Conference 2011 (pp. 569-574). Chesapeake,
VA: 2011.
[12] C. Chen, C. Liu, M. Chang, “Personalized curriculum sequencing
utilizing modified item response theory for web-based instruction”.
Expert Systems with Applications 30, 378-396. 2006.
[13] S. Hovakimyan, S. G. Sargsyan, “The Genetic Algorithms (GA) in Webbased
Learning Systems”. Proceedings of IASTED International
Conference on ACIT-Software Engineering (ACIT-SE 2005),
Novosibirsk, Russia, 2005.
[14] K. A. Papanikolou, M. Grigoriadou, “Towards new forms of knowledge
communication: the adaptive communication of a web-based learning
environment”. Computers and Education, 39, 333-360. 2002.
[15] P. C. Chang, C. Y. Lai, “A hybrid system combining self organizing
maps with case-based reasoning in wholsaler’s new release book
forecsating” . Expert Systems with Applications 29, 183-192. 2005.
[16] C. Chen, L. Lee-Hahn, Y. Chen, “Personalized e-learning system using
item response theory”. Expert Systems with Applications 44, 237-255.
2005
[17] E. Goldberg, Genetic Algorithms in Search, Optimization and Machine
Learning. Addison Wesley Longman, Inc. ISBN 0-201-15767-5. 1989.
[18] J. E. Baker, “Adaptive Selection Methods for Genetic Algorithms”.
Proceedings of International Conference on Genetic Algorithms and
their Applications.pp.101-111. 1985.
[19] R. Kumar, Jyotishree, “Blending Roulette Wheel Selection and Rank
Selection in Genetic Algorithms”. International Journal of Machine
Learning and Computing.Vol.2, No. 4. 2012.
@article{"International Journal of Information, Control and Computer Sciences:71640", author = "Melvin A. Ballera", title = "Adaptive and Personalizing Learning Sequence Using Modified Roulette Wheel Selection Algorithm", abstract = "Prior literature in the field of adaptive and
personalized learning sequence in e-learning have proposed and
implemented various mechanisms to improve the learning process
such as individualization and personalization, but complex to
implement due to expensive algorithmic programming and need of
extensive and prior data. The main objective of personalizing
learning sequence is to maximize learning by dynamically selecting
the closest teaching operation in order to achieve the learning
competency of learner. In this paper, a revolutionary technique has
been proposed and tested to perform individualization and
personalization using modified reversed roulette wheel selection
algorithm that runs at O(n). The technique is simpler to implement
and is algorithmically less expensive compared to other revolutionary
algorithms since it collects the dynamic real time performance matrix
such as examinations, reviews, and study to form the RWSA single
numerical fitness value. Results show that the implemented system is
capable of recommending new learning sequences that lessens time
of study based on student's prior knowledge and real performance
matrix.", keywords = "E-learning, fitness value, personalized learning
sequence, reversed roulette wheel selection algorithms.", volume = "9", number = "12", pages = "2483-5", }
