A Fuzzy Skill Predictor for Early Childhood Educators

  • Abstract
  • Keywords
  • References
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  • Abstract

    This study presents a model of a two-input single output (TISO) Fuzzy Skill Predictor based on Howard Gardner’s theory of multiple intelligences to assist early childhood educators in discovering latent skills in children of early school age as to tailor them towards professional skill development in their future lives. The skill prediction system was developed in two phases beginning with the generation of weighted fuzzy rules and then followed by the development of a fuzzy rule-based decision support system. The Mamdani Fuzzy inference model in MATLAB was used in implementing the system using weighted attributes of intelligence and ability to determine skills. The system was tested with hypothetical data based on Howard Gardner’s theory of multiple intelligence and was found useful for predicting skills based on the parameters used. The system was validated using early school academic records of 7 randomly sampled undergraduates studying various courses in the university. Though limited entries were used to test the system, the model is robust and can be easily modified to accommodate more entries and rules to predict as many skills as possible.


  • Keywords

    Fuzzy, Skill, Intelligence, Ability, Inference, Prediction

  • References

      [1] Anooj, P.K. (2012). Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy

      [2] rules. Journal of King Saud University - Computer and Information Sciences, 24(1), 27-40.

      [3] https://doi.org/10.1016/j.jksuci.2011.09.002 . Accesed on 20-05-2018.

      [4] Brown, A. L. (1978). Knowing when, where, and how to remember: a problem of metacognition. In R. Glaser (Ed.),

      [5] Advances in instructional psychology, vol. 1 (pp. 77–165). Hillsdale, NJ: Erlbaum.

      [6] Brown, A. L. and DeLoache, J. S. (1978). Skills, plans, and self-regulation. In R. S. Siegel (Ed.), Children’s

      [7] thinking: What develops? (pp. 3–35). Hillsdale, NJ: Erlbaum.

      [8] Davison, M.L. (1992). Multidimensional Scaling. FL, USA: Krieger.

      [9] Embretson, S.E., & Reise, S.P. (2000). Item Response Theory for Psychologists. Mahwah, NJ: Erlbaum.

      [10] Epuran, M. and Stanescu, M. (2010). Motric Learning - Applications in Body Activities. Romania: Discobolul

      [11] Publishing House, p.74.

      [12] Evans, M. (2000). Statistical distribution, 3rd edition. USA: Si Colab.

      [13] Flavell, J. H. (1992). Perspectives on perspective taking. In H. Beilin, & P. Pufall (Eds.), Piaget’s theory: prospects

      [14] and possibilities (pp. 107–141). Hillsdale, NJ: Erlbaum.

      [15] Gardner, H., and Hatch, T.H. (1989). "Multiple intelligences go to school: Educational implications of the theory of

      [16] multiple intelligences" . Educational Researcher. 18 (8): 4 doi:10.3102/0013189X018008004.

      [17] Hambleton, R.K., & Swaminathan, H. (1985). Item Response Theory: Principles and Applications. Boston: Kluwer-Nijhoff.

      [18] Kaplan, D. (2008). Structural Equation Modeling: Foundations and Extensions, 2nd ed. Los Angeles: Sage.

      [19] Kaplan, R.M. and Saccuzzo, D.P. (2010). Psychological Testing: Principles, Applications, and Issues (8th ed.).

      [20] Belmont, CA: Wadsworth, Cengage Learning.

      [21] Kluwe, R. H. (1987). Executive decisions and regulation of problem solving behavior. In F. E. Weinert, & R. H.

      [22] Kluwe (Eds.), Metacognition, motivation, and understanding (pp. 31– 64). Hillsdale, NJ: Erlbaum.Kumar, D., Singh, J., Singh, O. P. and Seema, D. (2013). A fuzzy logic based decision support system for evaluationof suppliers in supply chain management practices. Mathematical and Computer Modelling, 58(11-12), 1679-1695. https://doi.org/10.1016/j.mcm.2013.07.003. Accessed o 20-05-2018

      [23] Mihaela, P., Gabriela, G., Catalin, P., Gabriel, P. and Nicolae, E. (2013). Relationship between General Intelligence

      [24] and Motor Skills Learning Specific to Combat Sports. Elsevier:

      [25] Piaget, J. (1952). The origins of intelligence in children. New York: Norton & Company.

      [26] Rasch, G. (1980). Probabilistic models for some intelligence and attainment tests. Copenhagen, Danish Institute for

      [27] Educational Research, expanded edition (1980) with foreword and afterword by B.D. Wright. Chicago: The

      [28] University of Chicago Press.

      [29] Santos, M. and Lopez, V. (2012). Decision System for Safety on Roads. In: Lu J., Jain L.C., Zhang G. (eds)

      [30] Handbook on Decision Making. Intelligent Systems Reference Library, vol 33. Springer, Berlin:

      [31] Heidelberg

      [32] Thompson, B.R. (2004). Exploratory and Confirmatory Factor Analysis: Understanding Concepts and

      [33] Applications. New York: American Psychological Association.

      [34] van der Fels, I.M.J., Sanne, C.M.W., Hartmana, E., Elferink-Gemser, M.T., Smitha, J. and Visscher, C., (2015). The

      [35] relationship between motor skills and cognitive skills in 4–16 year old typically developing children: A

      [36] systematic review. Journal of Science and Medicine in Sport, 18, 697–703. Elsevier: https://www.jsams.org/article/S1440-2440(14)00177-7/pdf.

      [37] Veenman, M.V.J., Wilhelm, P. and Beishuizen, J.J. (2004). The relation between intellectual and metacognitive

      [38] skills from a developmental perspective. Learning and Instruction 14, 89–109. www.elsevier.com/locate/learninstruc. doi:10.1016/j.learninstruc.2003.10.004.




Article ID: 16986
DOI: 10.14419/ijet.v7i3.19.16986

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