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Journals / Turkish Journal of Electrical Engineering and Computer Science / 2014 / Cilt: 22 - Sayı: 1

Analysis of a rule-based curriculum plan optimization system with Spearman rank correlation

Didem ABİDİN
Hafize Şen ÇAKIR
Pages
176–190
Publication Date
2014-05-16
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the tests, the results gathered are considered to be 97% reliable when compared with the suggested module range.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In corporations, accurate planning should be applied to manage the in-service training task within an optimum time period and without hindering the working tempo of the employees.
📊
Key Findings & Data:For this reason, it is better to consider the curriculum planning task as a timetabling problem.
💡
Conclusion & Impact:According to the tests, the results gathered are considered to be 97% reliable when compared with the suggested module range.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Genetic algorithm · rule base · curriculum plan optimization

Abstract

In corporations, accurate planning should be applied to manage the in-service training task within an optimum time period and without hindering the working tempo of the employees. For this reason, it is better to consider the curriculum planning task as a timetabling problem. However, when the timetables are prepared manually, it may turn out to be a complicated and time-consuming problem. In this study, it is aimed to evaluate the results of software introduced previously, which seeks to find a solution to the curriculum planning problem of in-service training programs in corporations using a rule-based genetic algorithm (GA). The input data of the GA is the prerequisite rule set of the modules of the training program, where these rules are used for the fitness function of the system. The results are compared with the suggestion of an expert trainer using a nonparametric correlation test, and the best parameter combination of the GA giving the most similar result to that of the expert's is determined. According to the tests, the results gathered are considered to be 97% reliable when compared with the suggested module range.

Özet

In corporations, accurate planning should be applied to manage the in-service training task within an optimum time period and without hindering the working tempo of the employees. For this reason, it is better to consider the curriculum planning task as a timetabling problem. However, when the timetables are prepared manually, it may turn out to be a complicated and time-consuming problem. In this study, it is aimed to evaluate the results of software introduced previously, which seeks to find a solution to the curriculum planning problem of in-service training programs in corporations using a rule-based genetic algorithm (GA). The input data of the GA is the prerequisite rule set of the modules of the training program, where these rules are used for the fitness function of the system. The results are compared with the suggestion of an expert trainer using a nonparametric correlation test, and the best parameter combination of the GA giving the most similar result to that of the expert's is determined. According to the tests, the results gathered are considered to be 97% reliable when compared with the suggested module range.

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Cite This Article

ABİDİN, D., ÇAKIR, H. Ş. (2014). Analysis of a rule-based curriculum plan optimization system with Spearman rank correlation. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 22 - Sayı: 1, 176–190. https://ajindex.com/articles/188674

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References (11)

  1. S. Petrovic, E.K. Burke, “University timetabling” in: Handbook of Scheduling: Algorithms, Models and Performance Analysis (J.Y.T. Leung, editor), Boca Raton, FL, USA, CRC Press, pp. 45-1–46-1, 2004.
  2. R. Qu, E.K. Burke, B. McCollum, L.T. Merlot, S.Y. Lee, “A survey of search methodologies and automated approaches for examination timetabling”, Journal of Scheduling, Vol. 12, pp. 55–89, 2009.
  3. N. Pillay, W. Banzhaf, “An informed genetic algorithm for the examination timetabling problem”, Applied Soft Computing, Vol. 10, pp. 457–467, 2010. C ¸.H. Alada˘ g, G. Hocao˘ glu, M.A. Ba¸saran, “The effect of neighbourhood structures on tabu search algorithm in solving course timetabling problem”, Expert Systems with Applications, Vol. 36, pp. 12349–12356, 2009.
  4. H.J. Holland, Adaptation in Natural and Artificial Systems, Cambridge, MA, USA, MIT Press, 1975.
  5. D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Boston, MA, USA, Addison Wesley, 1989.
  6. P.C. Chang, J.C. Hsieh, S.H. Chen, J.L. Lin, W.H. Huang, “Artificial chromosomes embedded in genetic algorithm for a chip resistor scheduling problem in minimizing the makespan”, Expert Systems with Applications, Vol. 36, pp. 7135–7141, 2009.
  7. C.C. Tsai, S.H.A. Li, “A two-stage modeling with genetic algorithms for the nurse scheduling problem”, Expert Systems with Applications, Vol. 36, pp. 9506–9512, 2009.
  8. J. Giarratano, G. Riley, Expert Systems: Principles and Programming, 4th ed., Boston, MA, USA, Course Technology, 2004.
  9. N. Verma, Y. Jindal, R. Aggarwal, S. Jain, “An approach towards designing of car troubleshooting expert system”, International Journal of Computer Applications, Vol. 1, pp. 65–67, 2010. L. Chaoan, “The expert system of product design based on CBR and GA”, IEEE International Conference on Computational Intelligence and Security Workshops, pp. 144–147, 2007. J.S. Chou, “Generalised linear model-based expert system for estimating the cost of transportation projects”, Expert Systems with Applications, Vol. 36, pp. 4253–4267, 2009.
  10. Y. Kuroki, G.S. Young, S.E. Haupt, “UAV navigation by an expert system for contaminant mapping with a genetic algorithm”, Expert System with Applications, Vol. 37, pp. 4687–4697, 2010.
  11. W.K. Wong, X.H. Zeng, W.M.R. Au, “A decision support tool for apparel coordination through integrating the knowledge-based attribute evaluation expert system and the T-S fuzzy neural network”, Expert Systems with Applications, Vol. 36, pp. 2377–2390, 2009. J.S. Kim, “Developme