Automatic Detection and Dynamics of Working Memory using Q-Learning and Exponentially Weighted Moving Average
Abstract
Intelligent Tutoring Systems work to customize Virtual Learning Environments according to the learner’s cognitive profile. In order to customize these environments, it needs to apply Artificial Intelligence techniques to detect Affective Traits, Working Memory Capacity, and Learning Styles. This work proposes the application of Q-Learning and Exponentially Weighted Moving Average techniques for Working Memory Capacity detection through the use of the aprendice’s navigation traces. Experimental results show the potential of both methods to detect the Working Memory Capacity and point out the superiority of the Exponentially Weighted Moving Average technique considering the scenarios evaluated.
Keywords:
Working Memory, Detection, Q-Learning, Exponentially Weighted Moving Average, Virtual Learning Environments
References
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Turner, M. L. and Engle, R. W. (1989). Is working memory capacity task dependent? Journal of Memory and Language, 28(2):127–154.
Unsworth, N., Redick, T. S., Spillers, G. J., and Brewer, G. A. (2012). Variation in working memory capacity and cognitive control: Goal maintenance and microadjustments of control. Quarterly Journal of Experimental Psychology, 65(2):326–355.
Woehrle, J. L. and Magliano, J. P. (2012). Time flies faster if a person has a high working-memory capacity. Acta Psychologica, 139(2):314–319.
Box, G. E. P., Hunter, J. S., and Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation and Discovery. John Wiley & Sons, Inc., Hoboken, New Jersey, 2nd ed. edition.
Broadway, J. M. and Engle, R. W. (2011). Lapsed attention to elapsed time? Individual differences in working memory capacity and temporal reproduction. Acta Psychologica, 137(1):115–126.
Chang, T.-W., El-Bishouty, M. M., Graf, S., and Kinshuk (2013a). An Approach for Detecting Students’ Working Memory Capacity from Their Behavior in Learning Systems. 2013 IEEE 13th International Conference on Advanced Learning Technologies, pages 82–86.
Chang, T. W., El-Bishouty, M. M., Graf, S., and Kinshuk (2013b). Recommendation mechanism based on students’ working memory capacity in learning systems. Proceedings - 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013, pages 333–335.
Chang, T.-w., Kurcz, J., and El-bishouty, M. M. (2015). Ubiquitous Learning Environments and Technologies.
Chang, T.-W., Kurcz, J., El-Bishouty, M. M., Graf, S., and Kinshuk (2014). Adaptive Recommendations to Students Based on Working Memory Capacity. In 2014 IEEE 14th International Conference on Advanced Learning Technologies, pages 57–61. IEEE.
Dorça, F. A. (2012). Fabiano Azevedo Dorça. PhD thesis, Universidade Federal de Uberlândia.
El-Bishouty, M. M., Chang, T.-w., and Lima, R. (2015). Analyzing Learner Characteristics and Courses Based on Cognitive Abilities, Learning Styles, and Context. In Chang, M. and Li, Y., editors, Smart Learning Environments, Lecture Notes in Educational Technology, pages 3–25. Springer Berlin Heidelberg, Berlin, Heidelberg.
Engle, R. W. (2010). Role of Working-Memory Capacity in Cognitive Control. Current Anthropology, 51(S1):S17–S26.
Ford, N. and Chen, S. Y. (2000). Individual Differences, Hypermedia Navigation, and Learning: An Empirical Study. J. Educ. Multimedia Hypermedia, 9(4):281–3311.
Graf, S. and Kinshuk (2008). Learner modelling through analyzing cognitive skills and learning styles. Handbook on Information Technologies for Education and Training, pages 179–194.
Graf, S., Lin, T., and Kinshuk (2008). Improving student modeling: the relationship between learning styles and cognitive traits. Computers in Human Behavior, pages 122–137.
Graf, S., Liu, T. C., Kinshuk, Chen, N. S., and Yang, S. J. H. (2009). Learning styles and cognitive traits - Their relationship and its benefits in web-based educational systems. Computers in Human Behavior, 25(6):1280–1289.
Huai, H. (2000). Cognitive style and memory capacity: effects of concept mapping as a learning method. Tese de doutorado, Twente University, The Netherlands.
Iglesias, A., Martinez, P., Aler, R., and Fernandez, F. (2009). Learning teaching strategies in an adaptive and intelligent educational system through reinforcement learning. Applied Intelligence, 31(1):89–106.
Kirschner, P. (2002). Cognitive load theory: implications of cognitive load theory on the design of learning. Learning and Instruction, 12(1):1–10.
Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106:166–171.
Miller, G. (1994). The magic number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review, 101(2):343–352.
Mitchell, T. M. (1997). Machine learning. MIT Press, pages 1–5.
Ribeiro, P., Assis, L., Vivas, A., and Pitangui, C. (2017). Detecção de Estilos de Aprendizagem utilizando Média Móvel Exponencialmente Ponderada. In Simpósio Brasileiro de Informática na Educação, page 1247, Recife.
Salazar, L., Assis, L., Vivas, A., Pitangui, C., and Falci, S. (2017). Detecção de Estilos de Aprendizagem em Ambientes Virtuais de Aprendizagem Utilizando Redes Bayesianas. In Simpósio Brasileiro de Informática na Educação, page 1317, Recife.
Silva, J. C., Pitangui, C., Assis, L., and Vivas, A. (2017). Detecção Automática e Dinâmica de Estilos de Aprendizagem em Sistemas Adaptativos e Inteligentes utilizando Dynamic Scripting. In Simpósio Brasileiro de Informática na Educação, number S, page 1327, Recife.
Sweller, J. (2005). Implications of Cognitive Load Theory for Multimedia Learning. The Cambridge Handbook of Multimedia Learning, pages 19–48.
Teigen, K. H. (1994). Yerkes-Dodson: A Law for all Seasons. Theory & Psychology, 4(4):525–547.
Tortorella, R. A. W., Hobbs, D., Kurcz, J., Bernard, J., Baldiris, S., Chang, T.-W., and Graf, S. (2015). Improving Learning Based on the Identification of Working Memory Capacity, Adaptive Context Systems, Collaborative Learning and Learning Analytics. Proceedings of Science and Technology Innovations, pages 39–55.
Turner, M. L. and Engle, R. W. (1989). Is working memory capacity task dependent? Journal of Memory and Language, 28(2):127–154.
Unsworth, N., Redick, T. S., Spillers, G. J., and Brewer, G. A. (2012). Variation in working memory capacity and cognitive control: Goal maintenance and microadjustments of control. Quarterly Journal of Experimental Psychology, 65(2):326–355.
Woehrle, J. L. and Magliano, J. P. (2012). Time flies faster if a person has a high working-memory capacity. Acta Psychologica, 139(2):314–319.
Published
2018-10-29
How to Cite
VIVAS, Alessandro; ASSIS, Luciana; PITANGUI, Cristiano.
Automatic Detection and Dynamics of Working Memory using Q-Learning and Exponentially Weighted Moving Average. In: BRAZILIAN SYMPOSIUM ON COMPUTERS IN EDUCATION (SBIE), 29. , 2018, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 1293-1302.
DOI: https://doi.org/10.5753/cbie.sbie.2018.1293.
