# Fuzzy System for the Quality Assessment of Educational Multimedia Edition Design
## 5 Discussion  
Educational multimedia editions are an important tool in modern education, which contribute to increasing the efficiency of the educational process and developing skills for independent information processing [38; 39; 40]. Multimedia data activate several channels of perception simultaneously, which significantly improve memorization and understanding of the material. In addition, the integration of theoretical knowledge with practical tasks meets the requirements of the modern educational environment, focused on a competency-based approach. The developed fuzzy system for the quality assessment of educational multimedia editions allows determining an integral quality indicator based on input parameters specified by the user. In addition, there is the possibility of a variable selection of values for adjusting the predicted indicator. A significant advantage is the specification of input parameters in conventional units, depending on the complexity of a certain operation or the data volume. For example, the user can independently determine how difficult the process of layout or proofreading a particular edition is for his team. Alternatively, how large the volume of illustrative or multimedia content is in terms of the number of specialists involved in its processing. This allows for a high level of system adaptability to changes in requirements or analysis conditions. Accordingly, during the design process, the team leader can make informed management decisions to obtain a product of proper quality. For this purpose, a model of predictive quality assessment of educational multimedia editions is developed, which involves performing fuzzification and defuzzification. Fuzzification consists of replacing the concepts of a clear set with concepts of a fuzzy set. For this purpose, the process of creating a design of a multimedia edition is represented by a function, the arguments of which are linguistic variables selected on the basis of the expert evaluation. The predicted integral indicator is expressed through partial indicators that represent the essence of the quality of multimedia editions: the quality of the output data, the quality of the edition processing, the quality of the edition design. A detailed description of linguistic variables is carried out by isolating universal sets of values  
Figure 5: Histogram of the obtained quality indicators of educational multimedia editions. and the corresponding linguistic terms (Table 2). The results obtained are presented in a multi-level model of fuzzy logical inference (Figure 1), which contributes to the consistent establishment of the predictive quality of the multimedia edition design by accumulating knowledge from the lowest to the highest of its levels. The values of the membership functions for each term of linguistic variables are determined and normalized relative to the unit, which make it possible to form fuzzy sets according to the formula (8). Fuzzy knowledge bases, knowledge matrices and fuzzy logic equations are formed for partial quality indicators, and for the highest level--\"quality of multimedia edition\". This becomes the basis for defuzzification--the reverse process of fuzzification. The values of the terms are substituted into fuzzy logic equations and an integral quality indicator of educational multimedia editions is determined according to expression (16). The main restrictions of the developed software product are that the user does not have the ability to add linguistic variables or change the value of the universal set. This is due to the complexity and clarity of the algorithm for determining the integral quality indicator. In addition, the sets of possible parameters are determined by experts in this subject area, which provides a theoretically sound result. The most common alternative approach to the quality assessment of processes is the use of machine learning methods. However, machine learning methods may require significant amounts of data for training and fine-tuning hyper-parameters.