The hierarchy of learning motives plays an extremely important role for a management of productive activity of learners, their activity and purposefulness. In the process of educational work, such a motivational hierarchy is formed, where some motives are dynamic mechanisms of other motives that are very difficult to identify at the intuitive level, especially considering the influence of each of them. Therefore, to determine the most significant hierarchical sequence of motives, an innovative method was proposed which is based on the ideas of artificial intelligence. As an example, the search was implemented based on the so-called algorithm of imitation roasting, which is capable to take into account the probabilistic nature of motivational indicators. The article highlights the main leading educational motives of students, on the basis of which the “mechanism” of finding their optimal hierarchical system is shown, and one that simultaneously takes into account the multifactorial influence of their driving causes, taking into account their interconnection, interaction and dynamism. A step-by-step realization of construction of such a hierarchical system of main educational motives in combination with casual, minor motives which are difficult for expecting or providing in advance is shown. Given their unpredictability and probabilistic nature of occurrence, the proposed system of intelligent search allows you to select exactly those sequences of motives that provide the highest productivity and effectiveness of training. The value of the proposed algorithm of imitation roasting is that the accuracy of the result is sacrificed, but the number of iteration cycles decreases, which plays a large role in processing a significant number of motivational indicators.
An information aggregation method for hierarchical system reliability analysis with insufficient reliability information based on Bayesian Melding Method
