Evaluation index system of education quality for nursing professional degree postgraduate using the analytic hierarchy process

Abstract

Nursing is an inseparable job with the healthy life of human beings. High-level nursing talents have a greater influence on patients. It is the future trend for schools to train Nursing Professional Degree Postgraduate, and the evaluation of their education quality is the top priority.To construct the education quality evaluation index system of Nursing Professional Degree Postgraduate and to determine the weight of each indicator.Firstly, the indicators of the evaluation index system of education quality were identified from the literature review. Meanwhile, the Delphi questionnaire was designed and 13 experts evaluated and rated the indictors who were invited to conduct two rounds of the questionnaire. The weights associated with the factors were determined using the analytic hierarchy process and percentage methods, Finally, we developed the evaluation index system of education quality for a postgraduate nursing professional degree.The evaluation system consisted of 4 first-level indicators, 17 second-level indicators, and 71 third-level indicators. According to the weights computed by analytic hierarchy process, first-level indicators are ranked as "Input quality" (0.1273), "Process quality" (0.3111), "Output quality" (0.0846), "Development quality" (0.4770). Among the secondary indicators, experts pay the most attention to career development (0.3180). The top three indicators of third-level indicators are workplace (0.2385), matching degree between personal expectations and job opportunities (0.1272), and promotion opportunities (0.0795).The quality index system of nursing postgraduate education is scientific and reliable, and the weight distribution is reasonable. It is an effective tool for evaluating the quality of nursing graduate education.

Application of BP Neural Network in Teaching Quality Evaluation of Higher Vocational Education

“…e basic characteristics of teaching attitude, teaching content, teaching methods, and teachers, build a postgraduate teaching quality assessment index system and use BP neural network algorithm to build evaluation models, application sensitivity test identification key indicators [15]. Wei et al has built a graduate nursing professional degree education quality assessment index system from the four aspects of input quality, process quality, output quality, and development quality, and empowering weights through Delphi law and level analysis [16]. Liu and Yin proposed a hybrid intelligence algorithm based on a genetic algorithm and reverse propagation neural network to assess the quality level of teaching, and play an intelligent algorithm in the evaluation model construction [17].…”