Problem Of Quality Evaluation Research Articles

Analysis of the Evaluation Data of College Classroom Teaching Quality based on Fuzzy Mathematics Model

This paper mainly studies the problem of teaching quality evaluation. According to the data in the annex, the transformation of quantitative indicators and the evaluation of teaching quality are realized by establishing fuzzy mathematical model, grey correlation degree and other methods. For problem 1: according to the quantitative values of the eight evaluation indexes, a fuzzy mathematical model is established, and the comprehensive evaluation is transformed into a quantitative index to evaluate the teaching level of teachers. See the appendix for the sequence of serial numbers 4450-4469 in Annex 1. For question 2: considering the large amount of data in Annex 1, the courses that have participated in the evaluation more than 50 times are selected as the research object. Using hierarchical clustering processing, it is concluded that there are some differences in the teaching quality of each course in the school. According to the overall teaching quality, the seven teaching teams are: course 0097, course 0057, course 0322, course 0320, course 0486, course 0058, course 0138. For question 3: the grey correlation model is established according to the factors that affect the professionalism and objectivity of evaluation experts, such as teachers' professional titles, the number of evaluation sessions and the average number of sessions. The evaluation conclusions of the evaluation experts are given in the appendix. For question 4: the data in Annex 1 are sorted out in chronological order to obtain 46 evaluation objects and three evaluation indexes, namely, the number of evaluation sections, the average number of sections and the quantification of professional titles, for analysis. Then through the fitting of data processing, it is concluded that the teaching quality of the school has declined with the growth of time in the past three years. In response to question 5: according to the data in the annex, this paper puts forward the question "what is the relationship between the scores of teachers' comprehensive evaluation of teaching and students' comprehensive evaluation of learning in the evaluation of classroom teaching quality in Colleges and universities?" by fitting the fitting image with MATLAB, it is concluded that the scores of teachers' comprehensive evaluation of teaching and students' comprehensive evaluation of learning in the evaluation of classroom teaching quality in Colleges and universities are very similar.

An Evidential Reasoning Method of Comprehensive Evaluation of Water Quality Based on Gaussian Distribution

This study provides an evidential reasoning method for water quality evaluation based on Gaussian distribution to handle the problem of comprehensive water quality evaluation for a region across a period (multiple sections and multiple time points). The method turns the collection of observed water quality indicator values into a probability distribution of water quality grades by using the Gaussian distribution to compute the confidence assessment of water quality grades over one period. It eliminates the subjectivity involved in determining confidence levels and the problem of information loss during data fusion that arises with conventional approaches. The probability distribution of each assessment grade is then determined by repeatedly synthesizing evidence of the same water quality grade using the improved evidential reasoning synthesis rule. To avoid the subjectivity included in experience-based weight settings, principal component analysis (PCA) is utilized to calculate the weights of water quality indicators based on contribution rates and load coefficients. In the end, utility theory is presented to modify the discrete probability distribution of precise numerical expressions, offering thorough results for the evaluation of water quality and facilitating the comparison of various water quality grades. Using the Xiangjiang River Basin as a case study, the proposed evaluation method is contrasted with popular techniques for assessing water quality, including the Single-Factor Evaluation Method, the Fuzzy Comprehensive Evaluation Method, and the Evidential Reasoning Comprehensive Evaluation Method. The findings suggest that the evidence reasoning approach for evaluating water quality that is based on Gaussian distribution is more rational, accurate, and scientific. Additionally, empirical studies on the annual water quality trends in various regions, the upstream, midstream, and downstream trends, and the water quality trends during wet and dry periods are conducted using this method to assess and analyze changes in water quality in the Xiangjiang River Basin during the “11th Five-Year Plan” and “12th Five-Year Plan” periods. The analysis findings demonstrate that, even if the rate of progress has slowed, the Xiangjiang River Basin’s overall water quality has been steadily improving since management and protection measures were put in place. This shows that the preventive and control efforts implemented in the “11th Five-Year Plan” and “12th Five-Year Plan” periods were successful; nevertheless, carrying out the current tactics might only have a limited impact. As a result, more advanced and creative approaches are required to encourage the ongoing enhancement of the water quality in the Xiangjiang River Basin.

ASPECTS OF TRANSLATION QUALITY PROBLEM

Translation quality evaluation is generally referred to as one the mechanisms for translation industry development and the most important tool in the field of translator training, which determines the relevance of the stated topic. The purpose of this study is to review the problems and approaches to translation quality evaluation highlighting the differences in their treatment. The paper focuses on the study of the cognitive-terminological and system-activity aspects of translation evaluation, with special emphasis on the borderline between the process of quality evaluation and its result. An analysis of the term translation quality as used in meta-translation discourse demonstrated significant differences in the conceptualization of this notion. In translation theory, the term is generally used as an evaluative indicator of translation results, although in a broader sense it also refers to one of the main categories of translation studies that requires systematic research and description. In meta-translation discourse, translation quality may refer to a fairly high level of translation per se, but in combination with additional descriptive characteristics the term can also indicate different levels of quality, down to the lowest. To assess the results of translation using classical evaluative terms, namely equivalence and adequacy, it is generally necessary to clarify their conceptual content, which corresponds to a particular scientific paradigm. The existence of different and/or opposing points of view on the theory and practice of translation quality evaluation is accounted for by the complex and multidimensional nature of the phenomenon under consideration. The novelty of the conducted research lies in the distinction between translation evaluation as a result and translation evaluation as a process considered as a type of intellectual activity. Finally, we developed a model which reflects the structure of evaluation activities in the field of translation. The main components of this model are: the evaluation sphere, the evaluation actor, the object under evaluation (i.e. translation results), the purpose of evaluation activity, the evaluation method, the criteria and the parameters of evaluation. Following the system-activity approach one can take into account various facets of the problem of translation quality evaluation as well as the complex of motives and individual independent steps that determine the end result.

Algorithm and simulation study of oil painting classification based on visual perception and improved embedded learning

This paper presents an in-depth study and analysis of oil painting classification and simulation using an improved embedded learning fusion vision perception algorithm. This paper analyzes and models the image quality evaluation problem by simulating the human visual system and extracting quality perception features as the main entry point to improve the prediction accuracy of the overall algorithm. This paper proposes a multi-classification method of CCNN, which uses the similarity measure based on information first to achieve multi-classification of artwork styles and artists, and this part is the main part of this paper. This paper uses the wiki art repository to construct a dataset of oil paintings, including over 2000 works by 20 artists in 13 styles. CNN achieves an accuracy of 85.75% on the artist classification task, which is far more effective than traditional deep learning networks such as Resnet. Finally, we use the network model of this paper and other network models to train the classification of 3, 4, and 6 categories of art images. The accuracy of art image classification by this paper’s algorithm is higher than that of the current mainstream convolutional neural network models, and the extracted features are more comprehensive and more accurate than traditional art image feature extraction methods, which do not rely on researchers to extract image features. Experiments show that the proposed method can achieve excellent prediction accuracy for both synthetic distorted images and distorted images.

A decision support system for supplier quality evaluation based on MCDM-aggregation and machine learning

Evaluating suppliers’ quality performance is one of critical tasks of quality management since it is directly related to quality assurance, improvement and development, especially for manufacturing companies. However, the supplier quality evaluation remains challenging due to the involvement of a large amount of qualitative and quantitative data, multi-dimensional attributes, as well as a large number of items and suppliers, etc. In addition, while various multiple criteria decision making methods (MCDMs) exist and can be used for supplier quality evaluation, individual choice and application of the methods often result in inconsistent or conflicting evaluation results. To overcome the challenges, in this study, we develop a decision support system for supplier quality evaluation to deal with the practical complexity and to take advantage of the availability of the big industrial data. The system is presented as a conceptual framework including three modules: the decision matrix and criteria initialization module, the MCDM method selection and implementation module, and the aggregation module. The sequential conduction of each module formulates and solves the multiple supplier and item quality evaluation problem. The implementation of the system can be applied to dynamically monitor and evaluate supplier and item quality performance. Moreover, as the core intelligent part of the decision support system, in the aggregation module, we propose an aggregated MCDM model based on the machine learning concepts. It aims at providing more reliable evaluations across multiple suppliers and items. The aggregation model also contributes to the MCDM literature as an independent method which is not restricted to the purpose of supplier quality evaluation. The methods shed lights on how to construct and solve MCDM problems in the big-data era. Finally, the application of the decision support system is illustrated by a case of a large automotive company. The case shows that our decision support system provide more robust and reliable evaluation results than the traditional individual multiple criteria decision making method.

Problems of Adult Higher Education Development in China Based on Multiple Intelligences Theory and Related Countermeasures

Abstract This paper first focuses on the problem of quality evaluation of adult education, proposes a data pre-processing method suitable for quality evaluation of adult education - the efficacy coefficient method, and introduces the subjective and objective evaluation factors and the time sequence factors through a variety of weighting methods, and designs the comprehensive evaluation algorithms based on the “pulling the grades method” and the “time sequence weighting method”. Lastly, evaluation algorithms were utilized to analyze teaching satisfaction, student motivation, and internal and external influences on adult education. The results showed that the students’ satisfaction index for all levels of adult education ranged from 0.4 to 0.6, which is between unsatisfactory and average. The main external influences are lack of flexibility in the mechanism, insufficient teaching staff, lack of funding, no advantage, and irrational curricula, which account for 72%, 53%, 48%, 39%, and 24% of the perceptions, respectively. This study is of great significance for the further deepening of the reform and development of adult higher education in China and the healthy development of lifelong learning for all.

A novel decision-making method based on complex cubic q-rung orthopair fuzzy information

To improve the accuracy of decision results in complex fuzzy environments, complex cubic fuzzy sets are studied, which can not only measure the periodicity of decision-making data, but also use interval values and single values to act together on the data. However, the fuzzy sets do not provide a reasonable explanation for some special cases of input arguments. Thus, the power average operator is used to eliminate the influence of extreme input arguments on decision results, and the Maclaurin symmetric mean operator considers the correlation between inputs in this paper. Firstly, we define the operation rules, distance measures, evaluation index function, and evaluation criteria in a complex cubic q-rung orthopair fuzzy environment. Then, some aggregation operators are proposed to aggregate complex cubic q-rung orthopair fuzzy numbers, and their desirable properties and some special cases are discussed. Next, we use the subjective and objective fusion method to determine the weight of attributes. Further, a multi-attribute decision-making method is established by combining aggregation operator, evaluation function, and weight determination method. Finally, the proposed method is applied to a specific quality evaluation problem, and the effectiveness and practicability of the proposed method are illustrated by other methods and parameter analysis.

A Novel Method of Teaching Quality Monitoring and Evaluation from the Perspective of Educational Balance

Exploring the problem of teaching quality monitoring and evaluation from the perspective of educational balance can help educators and policy makers discover educational inequality problems, thereby promoting fair allocation of educational resources and opportunities. In light of this, this study attempts to research teaching quality monitoring and evaluation from the perspective of educational balance. At first, the mechanism of promoting educational balance by information technology was expounded, and the principle and necessity of a comprehensive assessment based on the evaluation results of different evaluators were explained. Then, this study innovatively introduced the whitening weight function (WWF)-grey evaluation model into the conventional grey clustering evaluation, calculated the unit comprehensive clustering coefficients of the indexes of teaching quality evaluation from the perspective of educational balance, and gave fair, accurate, and effective evaluations. After that, the grey GM (1,1) model was applied to the prediction of teaching quality improvement effect, the future trend of teaching quality changes was predicted based on history data, and the results provided references for teaching reform and educational decision-making. At last, experimental results verified the validity of the proposed method.

Multi-View MOOC Quality Evaluation via Information-Aware Graph Representation Learning

In this paper, we study the problem of MOOC quality evaluation that is essential for improving the course materials, promoting students' learning efficiency, and benefiting user services. While achieving promising performances, current works still suffer from the complicated interactions and relationships of entities in MOOC platforms. To tackle the challenges, we formulate the problem as a course representation learning task based, and develop an Information-aware Graph Representation Learning(IaGRL) for multi-view MOOC quality evaluation. Specifically, We first build a MOOC Heterogeneous Network (HIN) to represent the interactions and relationships among entities in MOOC platforms. And then we decompose the MOOC HIN into multiple single-relation graphs based on meta-paths to depict multi-view semantics of courses. The course representation learning can be further converted to a multi-view graph representation task. Different from traditional graph representation learning, the learned course representations are expected to match the following three types of validity: (1) the agreement on expressiveness between the raw course portfolio and the learned course representations; (2) the consistency between the representations in each view and the unified representations; (3) the alignment between the course and MOOC platform representations. Therefore, we propose to exploit mutual information for preserving the validity of course representations. We conduct extensive experiments over real-world MOOC datasets to demonstrate the effectiveness of our proposed method.

Quality evaluation of preschool video games based on RNN

With the rapid development of video multimedia technology, video games have started to be applied in the field of education. A video game quality evaluation method needs to be designed to help teaching staff select video materials. In view of this, a Recurrent Neural Network (RNN)-based video game quality evaluation method for preschool education is proposed. Firstly, an improved attention mechanism null domain weight assignment method is proposed for the video quality evaluation problem. Then a video game quality evaluation model based on RNN is proposed. Finally, performance testing is conducted on the proposed quality evaluation method. The results show that in the comparison of fitness values, when the number of iterations is 4 or 16, the maximum fitness value and the average fitness value of the research method are 97 and 96 respectively. In the prediction of different video datasets, the research methods can achieve prediction accuracy of 0.958 and 0.947 for LIVE and CSIQ videos respectively. In the analysis of practical application effects, all students show significant improvement in language reading, writing, understanding, and spelling. The above results indicate that the research method has a faster convergence speed and prediction accuracy. It can effectively improve students' learning efficiency and provide new references for the education and teaching evaluation of preschool children.

Effect Evaluation of Multilevel Fuzzy Comprehensive Evaluation Mechanism Combined with Multimedia Teaching on the Cultivation of Comprehensive Quality of Vocational Students

The core goal of higher vocational education is to cultivate students with good professional quality. The employability of college students depends on their professional quality to a large extent. From the perspective of school education, all kinds of higher vocational colleges have entered a new level in practice, but the evaluation of comprehensive quality is still relatively weak. Therefore, in order to enhance the comprehensive ability of higher vocational students and cultivate their excellent comprehensive quality, this paper adopts the fuzzy comprehensive evaluation method and puts forward the evaluation method of the comprehensive evaluation system of higher vocational students, so as to provide reference for solving the problem of comprehensive quality evaluation. The specific process is as follows: in this paper, a two-level fuzzy mathematical model is established by using fuzzy mathematics theory and multimedia technology. First of all, according to the educational characteristics such as learning interest, psychological quality when encountering difficulties, and the ability to solve problems cooperatively, a set of secondary indicator system reflecting vocational college students is established. With this indicator, quantitative indicators are fuzzified by introducing membership functions, and a single factor evaluation matrix is established. Then, the specific steps are illustrated with examples to realize the qualitative analysis and quantitative evaluation of students’ comprehensive quality. The results show that the new comprehensive quality evaluation method established in this paper has strong practicality and innovation.

Evaluation Method of Vocal Music Teaching Quality for Music Majors Based on the Theory of Multiple Intelligences

The quality evaluation of vocal music teaching for music majors is of great significance to music education. Based on the theory of multiple intelligences, this paper constructs a model for evaluating the quality of vocal music teaching for music majors and introduces the theory of multiple intelligences into the operation form, design requirements, and recommended lesson examples of music teaching unit design. The experimental data and questionnaire data collected by the model verify that this operation is beneficial to the improvement of students’ music listening scores, vocal music comprehension scores, and total scores and solves the quantitative problem of vocal music teaching quality evaluation. In the simulation process, the engineering testing and analysis method uses the correlation rate and correlation strength as the analysis indicators, comparing the platforms for the quality evaluation of vocal music teaching in music majors and the corresponding quality evaluation data preprocessing process. The experimental results show that the algorithm performance evaluation is carried out based on the three aspects of quality evaluation association rules, algorithm running time, and algorithm memory consumption. The multiple intelligence algorithm is applied to vocal music teaching analysis of association rules for quality evaluation: when 4000 tasks call for 20–40 virtual resources, the total time spent is reduced by 61.7%, which has a significant positive effect on the knowledge expansion and ability improvement of music majors.

Support Vector Machine with Robust Low-Rank Learning for Multi-Label Classification Problems in the Steelmaking Process

In this paper, we present a novel support vector machine learning method for multi-label classification in the steelmaking process. The steelmaking process involves complicated physicochemical reactions. The end-point temperature is the key to the steelmaking process. According to the initial furnace condition information, the end-point temperature can be predicted using a data-driven method. Based on the setting value of the temperature before tapping, multi-scale predicted errors of the end-point temperature can be calculated and divided into different ranges. The quality evaluation problem can be attributed to the multi-label classification problem of molten steel quality. To solve the classification problem, considering that it is difficult to capture nonlinear relationships between the input and output in linear models, we propose a novel support vector machine with robust low-rank learning, which has the characteristics of class imbalance without label correlations; a low-rank constraint is used to deal with high-order label correlations in low-dimensional space. Furthermore, we derive an accelerated proximal gradient algorithm and then extend it to handle the nonlinear multi-label classifiers. To validate the proposed model, experiments are conducted with real data from a practical steelmaking problem. The results show that the proposed model can effectively solve the multi-label classification problem in industrial production. To evaluate the proposed approach as a general classification approach, we test it on multi-label classification benchmark datasets. The results illustrate that the proposed approach performs better than other state-of-the-art approaches across different scenarios.

Evaluating the construct validity of text embeddings with application to survey questions

Text embedding models from Natural Language Processing can map text data (e.g. words, sentences, documents) to meaningful numerical representations (a.k.a. text embeddings). While such models are increasingly applied in social science research, one important issue is often not addressed: the extent to which these embeddings are high-quality representations of the information needed to be encoded. We view this quality evaluation problem from a measurement validity perspective, and propose the use of the classic construct validity framework to evaluate the quality of text embeddings. First, we describe how this framework can be adapted to the opaque and high-dimensional nature of text embeddings. Second, we apply our adapted framework to an example where we compare the validity of survey question representation across text embedding models.

СТАВЛЕННЯ БАТЬКІВ ДІТЕЙ ІЗ ОСОБЛИВИМИ ОСВІТНІМИ ПОТРЕБАМИ ДО ІНКЛЮЗИВНОЇ ОСВІТИ В УКРАЇНІ

The article is devoted to the acute problems of quality evaluation of inclusive education in Ukraine, mainly, to exploring the attitudes of parents of children with special educational needs (SEN) towards the educational services they receive from the state. The author reports the results of the undertaken in 2019 anonymous internet questionnaire, participants of which expressed their thoughts about the level of inclusive education in Ukraine, as well as about their own expectations and hopes concerning the ways of its development.

An Extended Shapley TODIM Approach Using Novel Exponential Fuzzy Divergence Measures for Multi-Criteria Service Quality in Vehicle Insurance Firms

Classification of the divergence measure for fuzzy sets (FSs) has been a successful approach since it has been utilized in several disciplines, e.g., image segmentation, pattern recognition, decision making, etc. The objective of the manuscript is to show the advantage of the combined methodology. A comparison clearly shows the usefulness of the proposed technique over the existing ones under the fuzzy environment. This study presents novel exponential-type divergence measures with some elegant features, which can be applied to FSs. Next, a TODIM (an acronym in Portuguese for Interactive Multicriteria Decision Making) approach derived from prospect theory, Shapley function, and divergence measure for multi-criteria decision-making (MCDM) is proposed. Besides, for the reason of evaluating the dominance degree of the option, and the weights of the criteria, proposed divergence measures are implemented. Evaluating and selecting the service quality is the most important issue in management; it has a direct influence on the way the manufacturer performs its tasks. Selecting the service quality can be thought of as a problem of MCDM involving numerous contradictory criteria (whether of a quantitative or qualitative nature) for the evaluation processes. In recent years, the service quality assessment is becoming increasingly complex and uncertain; as a result, some criteria assessment processes cannot be efficiently done by numerical assessments. In addition, decision experts (DEs) may not always show full rationality in different real-life situations that need decision making. Here, a real service quality evaluation problem is considered to discuss the efficacy of the developed methods. The algorithm (TODIM based on the Shapley function and divergence measures) has a unique procedure among MCDM approaches, which is demonstrated for the first time in this paper.

Problems of quality evaluation in process of transport services design

The quality issues of new services directly affect the competitiveness of service organizations. However, the introduction of new services to the market is often limited only to the construction of the logistics of business processes, and the design applies only to technological equipment and infrastructure, the quality of which ultimately does not always ensure the quality of the services provided. At the same time, quality management affects mainly operational aspects, that is, it covers the later stages of the service life cycle. In resource-intensive service industries (for example, transport, communications, etc.), the high cost of equipment and infrastructure reduces the possibility of changes in service delivery technologies to improve their quality, which leads to inefficiency and market failures due to the fact that the new service does not match the real needs of customers. Despite this, forecasting and planning the quality of a service at the stage «making a decision» to launch a new service on the market remains largely without sufficient attention. The authors prove the need to design the quality of services at the stage «making a decision» to bring new services to the market. The purpose of the article is to describe the approach developed by the authors to assessing the quality of projected services at the early stages of their life cycle, which makes it possible to integrate qualitative and quantitative indicators of the future service and take into account the forecast requests of customers. The proposed approach has two features: (1) forecasting customer requirements for the quality of services is based on the results of machine learning based on data on existing and potential customers, as well as on the basis of the accumulated knowledge base of customer experience and expert opinions; (2) multi-criteria optimization is used, while some of the optimized parameters are discrete and high-quality. In conclusion, the authors demonstrated the advantages of the developed model on the examples of transport and logistics business in the field of passenger and freight transportation in Russia.

Evaluation model of art internal auxiliary teaching quality based on artificial intelligence under the influence of COVID-19

At present, the evaluation of normal teaching order and teaching quality has been seriously interfered by the impact of COVID-19. In order to ensure the quality of art classroom teaching, this article uses BP neural network technology to build a model for art teaching quality evaluation during the epidemic. Based on the introduction of the BP neural network model and the problems of art teaching quality evaluation, the article focuses on the art teaching quality evaluation indicators and the BP neural network algorithm and process. In addition, the article also uses an empirical method to verify the effect of the BP network model training method, and obtains the expected effect. Finally, it discusses the problem of information processing in art teaching evaluation.

Fuzzy mathematics and machine learning algorithms application in educational quality evaluation model

Teaching quality evaluation is a complex non-linear system fitting problem under the influence of many factors. The establishment of teaching quality evaluation is to construct a functional relationship between teaching quality evaluation index and teaching effect. In this paper, the authors analyze the fuzzy mathematics and machine learning algorithms application in educational quality evaluation model. Machine learning method has been well applied in complex problems such as classification, fitting, pattern recognition and so on. It can be used to realize a more comprehensive, reasonable and effective evaluation of the classroom teaching quality of university teachers. The simulation results show that the model can well express the complex relationship between the teaching quality evaluation index and the evaluation results. The theoretical values of the evaluation results are in the corresponding confidence interval, which proves that the machine learning algorithm has good reliability for different teaching quality evaluation problems.

The Quality of Data Center Infrastructure Management (DCIM)

Now-a-days, Data Center Infrastructure Management is increasing workplace efficiency, reducing overhead costs, and maintaining availability are some of the top priorities for data center administrators. The markets of data center have evolved not only in reply to the quantity of data, but also to increasing demands approximately connectivity up-time, security, and lesser IT costs. Information Technology is growing more rapidly than ever and new challenges appear every day. Various challenges are data storage and processing, and data centers have a critical role in this situation. As data centers get bigger and bigger, more energy is required and availability is primary. In this paper it is discussed the quality of DCIM by taking into requirements and the quality evaluation problem, here is a proposed method to analyze the quality of DCIM for energy costs, availability and new systems of data center infrastructure management (DCIM). With the proposed method of evolution will definitely resolve the problems of Data Centers so that the throughput of data centre will increase efficiently.