Training Quality Evaluation Research Articles

Training approaches for the dissemination of clinical guidelines for NSSI: a quasi-experimental trial

BackgroundNon-suicidal self-injury (NSSI) is of high clinical relevance due to its high prevalence and negative long-term implications. In 2016, the German consensus-based clinical guidelines for diagnostic, assessment and treatment of NSSI in childhood and adolescence were published. However, research indicates that clinical guidelines are often poorly implemented in clinical practice. One crucial part of this process is the training of healthcare professionals to transfer knowledge and capacities to bring guideline recommendations into clinical practice.MethodsThe effect of three different dissemination strategies (printed educational material, e-learning, and blended-learning) on the NSSI guidelines´ recommendations was examined among 671 physicians and psychotherapists via an online-survey. The quasi-experimental study included three measurement points (before the training, after the training, 3-month follow-up) and mixed effects models were used to test for changes in knowledge, competences and attitudes toward NSSI and treatment. Moreover, the transfer of gained competences to practical work and user satisfaction were reviewed.ResultsWith all three training formats, the intended changes of the outcome variables could be observed. Hereby, the printed educational material condition showed the lowest improvement in the scores for the ‘negative attitudes toward NSSI and those who self-injure’. The training effect remained stable throughout the follow-up measurement. The highest application rate of acquired intervention techniques in clinical practice was reported for the blended-learning condition. For all three training strategies, user satisfaction was high and evaluation of training quality was positive, with printed educational material receiving the lowest and blended-learning the highest evaluations.ConclusionsIn summary, all three training formats were perceived to be of high quality and seem to be suited to cover the needs of a heterogeneous group of physicians and psychotherapists. The choice of training method could be driven by considering which training goals are desired to be achieved and by the benefit-cost ratio allowing for tailored training approaches.

Design and application of a quality evaluation index system for sustainable cultivation of big data professionals in universities based on Bloom cognitive domain classification method

This article aims to design and apply a quality evaluation index system based on Bloom's cognitive domain classification method to evaluate the training quality of big data professionals in universities. With the rapid development of big data technology, the importance of cultivating big data professionals in universities is becoming increasingly prominent. However, the existing training quality evaluation system often lacks systematicity and scientificity, making it difficult to fully reflect the comprehensive abilities of students. This article combines key knowledge of big data in the field of finance and economics with Bloom’s cognitive domain classification method to divide the cognitive process of talent cultivation into six levels: memory, understanding, application, analysis, evaluation, and creation. Through expert interviews and the Delphi method, specific evaluation indicators for each cognitive level were determined, and 18 indicators were extracted. Through data collection and empirical analysis, the performance of students at various cognitive levels was evaluated. The results showed that third year students performed the best on average at all cognitive levels, reflecting their rich learning experience and practical skills. The analysis also shows that there are significant differences in the performance of students at higher-order cognitive levels, with a coefficient of variation of 0.23 for the A1 indicator and 0.84 for the F3 indicator. This indicates that higher-order cognitive activities require higher demands from students, with significant individual differences. Through correlation analysis between various indicators, it was found that the proficiency level of tool usage in the project is strongly correlated with other indicators. Bloom’s cognitive domain classification system can reflect students’ comprehensive abilities and provide a scientific evaluation method for universities. Through continuous improvement and in-depth research, we hope to provide more scientific and comprehensive support for the cultivation of big data talents in universities.

Research on Quality Evaluation of Talent Cultivation in Modern Industrial Colleges based on CIPP Model Evaluation of Talent Cultivation by CIPP

As an important carrier for the implementation of transformation and development of applied undergraduate colleges and universities and the carrier for the integration of industry and education, as well as collaborative education, modern industrial colleges are the solid foundation for establishing a modern application-oriented vocational education system for undergraduate students and cultivating more highly skilled personnel. The quality evaluation of their talent cultivation involves multiple dimensions and levels, including both time and space dimensions, as well as value dimension. Taking the CIPP evaluation model as the basis, following the principles of dynamic development and effectiveness balance, targeting at at the talent cultivation goal of modern industry college of applied undergraduate colleges, this paper constructs a talent training quality evaluation index system with four primary indicators and their corresponding secondary indicators from four dimensions: background evaluation, input evaluation, process evaluation and result evaluation, so as to conduct comprehensive and systematic supervision and feedback on the dynamic process of talent cultivation for the purpose of providing reference for optimizing the talent cultivation quality of modern industrial colleges.

The construction of physical training and nurturing system for aviation service talents under the background of deep learning

Abstract In the current aviation industry’s demand for excellent service personnel surge, this paper emphasizes the importance of physical training for college aviation service personnel to improve the image of individuals and companies. To this end, this study is oriented to the employment needs of aviation enterprises, creates the “9C cooperation” physical training and nurturing model, and establishes a detailed set of physical training quality evaluation index system. Taking University S as a case study, the data were collected through questionnaires, and factor analysis was applied to comprehensively assess the quality of training and nurturing of aviation service talents. Combined with the IPA analysis model, this paper further explores the correlation between corporate satisfaction and importance and the physical training system. The study results showed that the total variance contribution of the four main factors to the quality of physical training and nurturing was as high as 91.33%, while the overall score of the quality of training and nurturing was 3.153. The importance and satisfaction of enterprises to the quality of form training are distributed in a high range, showing that the training system needs to be further improved. Therefore, colleges and universities need to refine the teaching objectives and contents to ensure that the quality of physical training for aviation service talents can meet the actual needs of aviation enterprises and promote the industry’s overall development.

Model Innovation Research of Cultivating Applied Art and Design Talents Based on Environmental Psychology

Abstract To achieve the training goal of applied innovative talents, this paper designs an innovative model of training applied art and design talents based on environmental psychology to improve the quality level and practical ability of art and design talents. The k-means algorithm is improved by introducing a particle swarm algorithm, using information entropy to verify the effect of the clustering. The improved algorithm is used in developing the talent training model to make the development results more reasonable. Based on the strength of the training performance of each classifier in the random forest, the individual classifiers are weighted to reduce the influence of the poorly performing classifiers on the talent training quality evaluation results. A multi-level fuzzy evaluation model is used to design a quality evaluation index system for applied talent training, which is used to measure the merits of the talent training model. To verify the feasibility of the designed model, the simulation analysis results show that the assessment achievement on course objectives 1-5 reached 0.6784, the average score on design content, methods and ideas was 4.34, and the comprehensive score of talent training quality evaluation was 85.64. It shows that environmental psychology can support the cultivation of applied art and design talents and promote the overall development of applied talents.

Training Quality Evaluation of Innovative and Entrepreneurial Talents for Smart Tourism

The smart tourism industry is developing at an incredible pace and has posed requirements for persons with related skills and talents, especially those with creative thinking, innovation ability, and entrepreneur quality. The training quality evaluation of innovative and entrepreneurial talents could provide guidance for the decision-making of higher educational institutions and enterprises so that proper adjustments can be made to current education system and curriculum setting. However, little existing research has addressed this issue, so this study attempts to develop a method for evaluating the training quality of innovative and entrepreneurial talent responding to the requirements of smart tourism. The quality control of innovative and entrepreneurial talents for the smart tourism industry is discussed to ensure that the evaluation criteria can be implemented during the training process, then an evaluation index system (EIS) is created with weight values assigned to each index by the entropy method, and the gray comprehensive evaluation method is employed for the said evaluation task. Finally, linear regression and other experiments are carried out to prove the scientific validity of the proposed method. Research conclusions attained in this study provide useful evidence for optimizing the training strategies of innovative and entrepreneurial talents.

GRA method for probabilistic simplified neutrosophic MADM and application to talent training quality evaluation of segmented education

The “3 + 2” segmented training between higher vocational colleges and applied undergraduate courses has opened up the rising channel of vocational education from junior college level to undergraduate level, and promoted the organic connection between higher vocational colleges and Universities of Applied Sciences. It is one of the important ways to establish a modern vocational education system. Exploring the monitoring mechanism of talent training quality is an important measure to ensure the achievement of the segmented training goal, and it is a necessary condition to successfully train high-quality skilled applied talents. The talent training quality evaluation of segmented education is viewed as multiple attribute decision-making (MADM) issue. In this paper, an extended probabilistic simplified neutrosophic number GRA (PSNN-GRA) method is established for talent training quality evaluation of segmented education. The PSNN-GRA method integrated with CRITIC method in probabilistic simplified neutrosophic sets (PSNSs) circumstance is applied to rank the optional alternatives and a numerical example for talent training quality evaluation of segmented education is used to proof the newly proposed method’s practicability along with the comparison with other methods. The results display that the approach is uncomplicated, valid and simple to compute.

The shortcomings of quality evaluation of postgraduate training under the orientation of “five only” and its countermeasures

This paper analyzes the causes and the current situation of the traditional education evaluation in China based on the goal of breaking the “five only”, and discusses its drawbacks in terms of distorting the real level of scholars, hindering the diversified innovation of schools, driving the academic style and merit, and harming the national education development. Finally, the paper proposes countermeasures to break the “five only” reform in terms of establishing a new style of education, improving result evaluation, strengthening process evaluation, exploring value-added evaluation, and improving comprehensive evaluation. The paper may be useful to promote the reform of postgraduate training quality evaluation and improve the institutional mechanism of moral education.

Innovative Evaluation Method of Government Physical Education Training Quality Based on Fuzzy Mathematics

Current sports training quality evaluation methods cannot accurately detect the integrity of training data, resulting in low accuracy of training quality evaluation. Considering the function of the government in providing public sports services and the role of the government in coordinating the sports training of all parties, this paper puts forward an innovative evaluation method of government physical education training quality based on fuzzy mathematics. First of all, the real-time data on sports training sensor signal is obtained by using SiCN (Silicon Carbonitride) sensor, and the real-time detection circuit and data amplification module are designed. Based on the core hardware for instant data detection of sports training sensor signal, a fuzzy support vector machine model is established to detect the integrity of government sports innovation training data, reconstruct the track of sports training action, and thus construct an innovative evaluation model of government sports training quality based on heredity and support vector machine. Experimental results show that compared with existing methods, the proposed method has higher accuracy in data integrity detection, better reconstruction effect in sports training action, with evaluation results reaching more than 90 points.

The Use of Machine Learning Model in the Evaluation of College Students’ Employment and Entrepreneurship Level

This exploration aims to investigate the employment and entrepreneurship of college students. College students majoring in information and computing sciences in Xinxiang City are taken as the research object. A machine learning model for college students’ employment and entrepreneurship is established. Experiments are conducted using Python language and a machine learning framework. First, based on the employment and entrepreneurship indexes of college students in machine learning, the initial data of college students’ training quality evaluation are obtained from the educational administration system, library management system, college evaluation materials, and questionnaire survey. Then, the combination weighting method is adopted to determine the index weight, quantify the data, and modify the parameters of the data provided by the framework. The Gaussian kernel is selected as the kernel function, and the sample data used by the machine learning model are labeled. Finally, the sample data are employed to train and test the model. After the consistency test, the model reaches the optimal value after 7 iterations, with an error rate of 0.01 and an accuracy rate of 99%. The final error rate of entrepreneurship and innovation model based on machine learning is less than 0.1, which is consistent with the actual situation. The model can meet the requirements of college students’ entrepreneurship and employment evaluation. It proves that it can be applied to the research of college students’ employment and entrepreneurship and has certain theoretical guidance and practical significance for the evaluation of college students’ employment and entrepreneurship level.

Evaluation Method of Public Physical Training Quality Based on Global Topology Optimization Deep Learning Model.

In the quality evaluation of public sports training, the selected indicators are not comprehensive, resulting in some errors in the results of quality evaluation. Therefore, this paper designs a public sports training quality evaluation method based on the deep learning model of global topology optimization. Determine the basic principles of public sports training quality evaluation, determine the human coordinate points of public sports training by determining the basic section and basic axis of human training, and extract the data of public sports training quality evaluation. On this basis, quantify the public sports training quality evaluation index, construct the evaluation matrix, calculate the weight of the evaluation index, and determine the importance of the public sports training quality evaluation index. Preprocess the public sports training quality evaluation index set, search the optimal fitness value in the global topology optimization depth learning model, introduce the global topology optimization depth learning model, input the evaluation index and output the evaluation quality results, and realize the quality evaluation of public sports training. The experimental results show that the evaluation method in this paper can improve the accuracy of the evaluation results and is feasible.

Application of the Big Data Analysis Model in Higher Education Talent Training Quality Evaluation

With the increasing demand for applied and professional talents, the talent market has been in short supply. Although there are many talents in the talent market, the quality of talents cannot keep up with the development of quantity. Therefore, it is of great practical significance to establish a visual evaluation system of personnel training quality in the field of higher education. In view of the unreasonable evaluation and unclear weight relationship in the evaluation of educational indicators, this paper puts forward a big data analysis model to comprehensively evaluate teaching evaluation indicators, which has more scientific significance. In this paper, different systems in the index system are used as the analysis objects and the first-level weight relationship is normalized, which can distribute the weights more reasonably. Through the big data analysis method, the teaching quality evaluation system is more reasonable and scientific. In this paper, the quality index system for higher education background is designed and constructed and the weight relationship of different educational indicators is analyzed through big data, and four main indicators are obtained; then, the weight relationship of secondary indicators is analyzed, and finally, the weight relationship of all indicators is formed. The results show that the weight relationship of four indexes is 0.3285, 0.1973, 0.2967, and 0.1755, and the evaluation model of education quality is given.

Network Game of Student Training Quality Assessment System Based on KOHONEN Neural Network

Objectives: In recent years, with the continuous improvement of the requirements of student training quality, the evaluation results of the existing evaluation system of student training quality are mostly unsatisfactory. Therefore, by integrating c-mean algorithm and Kohonen clustering algorithm, a non-sequential artificial neural network is obtained, a student training quality evaluation system based on KOHONEN neural network is designed by automatically adjusting the size of the objective function nodes of the non-sequential artificial neural network. Then the evaluation system is applied to the expected evaluation of the training quality of students in two science classes of Xinghua Middle School in Shenyang, Liaoning Province. The comparison between the test result data and the expected results of the model after the experiment confirms that the evaluation results obtained by using the evaluation system based on KOHONEN neural network have high accuracy.

"1+X" Course Certificate Fusion and Environmental Design Professional Training Quality Evaluation Model Based on Big Data

"1+X" Course Certificate Fusion and Environmental Design Professional Training Quality Evaluation Model Based on Big Data

Research on the Quality Evaluation System of First-Class Talents Training based on the Background of “Double First-Class” Construction

The construction of “Double First-Class” is a Chinese strategy in line with the development trend of the world's higher education. In the “Double First-Class” construction plan, the state highlights the core position of talent training and plans to cultivate top-notch first-class talents. The cultivation of first-class talents is inseparable from the construction of talent cultivation quality evaluation system. Therefore, it is of great significance to design a reasonable first-class talent training quality evaluation system for improving the quality of first-class talent training and accelerating the development of “Double First-Class” strategy. Based on the background of “Double First-Class” construction and taking the graduate students of Dalian University as an example, this paper first collects data through questionnaire survey, and constructs a set of first-class graduate training quality evaluation system by factor analysis method. Then, based on the evaluation system, this paper evaluates the first-class graduate training quality of Dalian University by using fuzzy comprehensive evaluation method, so as to find out the shortcomings of graduate training in Dalian University. Finally, the paper puts forward the countermeasures and suggestions to improve the quality of first-class graduate training in Dalian University, in order to provide reference for other universities.

Construction and Implementation of Quality Evaluation System of Government Accounting Professional Training based on Cloud Platform

With the rapid development of social economy, sustainable development has gradually become a guiding ideology to guide the development of all walks of life. In the process of modern accounting talent training quality evaluation, we still need to implement the scientific development concept into the whole process of accounting talent training and evaluation according to the corresponding sustainable development concept. Through the quality evaluation, we can find more professional government accounting talents with high quality and high skills, so as to provide support for the further construction of socialist harmonious society[1].

Evaluation of the Effect of Pre-Job Training on New Pediatric Nurses Based on Onion Model

To determine the effect of pre job training on onion model among new pediatric nurses. One hundred and seventeen pediatric nurses that commenced work on July 1, 2019 after 2 w of conventional training and 94 nurses that received the onion mode of training for 2 w before July 1, 2020 were recruited as the control and experimental groups respectively. The theoretical, practical and training quality scores of the new nurses were compared. The experimental group had significantly higher theoretical score (78.53±4.96 vs 69.27±11.38), Cardiopulmonary resuscitation score (92.39±4.06 vs 91.38±3.12), intradermal injection score (94.97±3.16 vs 92.35±3.96) and training quality evaluation score (99.07±4.5 vs 97.27±7.51) compared to that of the control group (p<0.05 for all). The onion training model can improve the theoretical and practical skills of new nurses and the quality of pre-job training.

“万众创新”背景下会计学硕士研究生培养研究—基于北京服装学院视角

本文通过剖析目前我国会计学硕士研究生在培养中存在的问题，同时结合社会调查，分析我国高级会计人才应具备的能力，分别从培养目标、培养过程、质量评价体系等方面对我校会计学硕士研究生的培养模式进行了构建，并提出相应的改进建议。 After analyzing the problems that exist in the accounting postgraduate training in our country, and combining with some investigations, the author analyzes the necessary capabilities needed by senior accountants. Also, the author establishes the accounting postgraduate training model in our school from the aspects of training objective, training process, training quality evaluation system and so on and puts forward corresponding improvement suggestions.

Using training and development to affect job satisfaction within franchising

PurposeA review of the literature on franchising has revealed a significant lack of academic research on employee training in franchise systems. Further, most research in this area tended to focus on training for franchises as part of the overall package. This study seeks to address the shortfall of research in this area by investigating the influence of training and development on an employee's job satisfaction at one of Australia's fastest growing franchises.Design/methodology/approachThis study collected data from 135 frontline staff at one of Australia's largest bakery retail franchises. The data were gathered by means of a structured questionnaire, 16 items of which were devoted to an evaluation of the organisation's training and development programmes and six items were dedicated to job satisfaction.FindingsThere are several key findings for this study. First, the effectiveness and efficacy of a training program are dependent on evaluation of training quality, course design and learning experience. Next, employee satisfaction is found to be influenced by work environment, company values and job responsibilities.Practical implicationsFindings from this study have important managerial implications for retail franchisers and individual franchisees on how to enhance the job satisfaction of employees by the provision of effective training and development programmes.Originality/valueThis empirical study has made a major contribution in adding to the limited body of empirical knowledge on the influence of training and development on job satisfaction in retail franchising. It is hoped that this paper will encourage more academics to investigate the impact of training and development on job satisfaction and retention in franchising.