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

Abstract

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.

The Potential of Absorbing Foreign Agricultural Investment to Improve Food Security in Developing Countries

“…Spearman correlation coefficient, also known as rank correlation coefficient, is a linear correlation analysis using the rank of two variables. It does not require the distribution of the original variables and has a wider scope than other correlation analysis and can effectively reveal the correlation between variables [55]. After conducting the principal component analysis, we use Spearman correlation analysis to obtain the correlation and regularity between the investment demand score, investment attractiveness score, and investment environment score, and then form the results and inspiration.…”