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Research On Satisfaction Of Online Education Based On SERVQUAL-IPA Model

Posted on:2024-04-12Degree:MasterType:Thesis
Country:ChinaCandidate:B LiuFull Text:PDF
GTID:2557307052981519Subject:Project management
Abstract/Summary:
With the integrated development of information technology and education,the traditional education model has been unable to meet the demands for the production and transmission of educational information in the era of knowledge economy,and online education has gradually become an important form of education development.At the beginning of 2020,in the face of a sudden epidemic,265 million students in schools and universities nationwide switched from traditional classrooms to online classes,and online education showed explosive growth.During this period,the Ministry of Education launched 22 online teaching platforms and 24,000 online courses,which provided a strong guarantee for schools across the country to implement "no classes without teaching,no classes without learning" during the epidemic prevention and control period.At the same time,the Ministry of Education stressed the need to accelerate the transformation of online education from "novelty" to "new normal",and that it will be a trend for traditional classrooms and online classrooms to coexist and integrate in depth in the long term.Through analyzing the factors influencing online education satisfaction,we hope that the students can quickly find the right course for themselves and improve their learning efficiency and interest in learning.It will enable teachers to accurately identify the problems in teaching,continuously improve teaching methods and cater to students’ learning patterns.It can help online education institutions to improve the quality and service awareness of their online courses,thus increasing the company’s popularity in the industry.The data selected for the research evaluation of satisfaction with online education in this thesis mainly comes from the review texts of online courses in online education platforms.However,influenced by the epidemic,the number of online education platforms is also rapidly increasing,the content of course reviews varies,the time span of the reviews is large,and even some newly started online education institutions take false reviews and make high course ratings in order to make profits from marketing.So it is necessary to screen the current existing online education platforms.Finally,the required course review texts in Tencent Classroom,MOOC and Hujiang Online School websites were selected as the data for the current online education satisfaction analysis study.In order to make the evaluation model more suitable for the online education industry,we first establish the SERVQUAL evaluation model of online education initially by reviewing relevant literature,then write a crawler program to collect the course review data of online education,and conduct text analysis on the review data to extract high-frequency words,further refine and perfect the influencing factors in the SERVQUAL evaluation model,and finally consult the users and experts of online education to determine the final SERVQUAL evaluation model.According to the refined evaluation data,the overall situation of current online education is analyzed emotionally,and it is concluded that most users are satisfied with the current experience of online education.The factors influencing online education satisfaction are analyzed in depth through the IPA model to analyze the satisfaction of online education.The results show that three main aspects affecting current online education satisfaction are related to teacher teaching,classroom interaction,and information security.For the main influencing factors,this thesis gives specific countermeasures and suggestions: first,to shape the image of teachers and optimize teaching methods,second,to increase the kinetic energy of bulletins and integrate science and technology,with third,to strengthen security management and protect intellectual property rights.
Keywords/Search Tags:Online education, Review text analysis, Sentiment analysis, SERVQUAL model, IPA analysis
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