What factors influence undergraduate students to study in a big data curriculum: The case of a Chinese University

Abstract

With the support of the Chinese government's big data strategy, China's big data industry has achieved rapid development, which is also stimulating the talent gap of big data. As an applied university, Baise University has an inescapable responsibility for the cultivation of big data talents. The main objectives of this study are: to study the reasons of choosing the big data as education of choice of undergraduate students of Baise University. To study the reasons of choosing the big data as education of choice of experience people. To identify the admission and college administration which can attract the students decisions. This IS applied the sequential explanatory method of the qualitative and quantitative mixed- method. First used qualitative research, and then extend the results of qualitative research to quantitative research. In the qualitative research stage, structured interviews were used, and 10 open questions were used for interviews. The sample comes from 2 undergraduates in Baise University's IT-related majors and 2 experienced staffs with more than 5 years of experiences in relevant data sciences fields. As for the quantitative, 56 questionnaires are randomly sampling，38 undergraduate students form computer science, mathematics and statistics, and Electronic information engineering of Baise University, 18 experienced staffs with over than 5 years of experiences in relevant data sciences fields. The questionnaires are validated the quality of 26 items by 4 experts (IOC value >.70). The finding，respondents' awareness of big data is not high, even IT engineering professionals have little knowledge of big data. The respondents between the ages of 31 and 40 have medium to high level knowledge of big data. Experienced employees need big data more than students. Mathematics and statistics students need big data more than computer science students or electronic information engineering students. The main goals of computer science students learning big data are "mastering big data software development technology" and "improving big data literacy", the goals of professional big data learning are mainly "improving big data literacy" and "mastering big data modeling and analysis technology", the goal of electronic information engineering students learning big data is "to master the security and system maintenance technology of big data", and the goal of in-service staff to learn big data is "to master the development technology of big data application software". The biggest barrier for students to participate in big data degree education is "big data technology difficulty", followed by "not interested in big data", "economic difficulties" and "entrance difficulty", the biggest barrier for experienced employees to participate in big data academic education is "Insufficient learning time after work." Respondents paid a lot of attention to "research strength", "teacher level" and "production and teaching integration level", followed by "school facilities", "school popularity and reputation" and "tuition fees and scholarships". Students are more concerned about "tuition fees and scholarships" than those with work experience. The higher the family's economic income, the less attention is paid to "tuition fees and scholarships". These findings can provide valuable insights into the recruitment and management of big data graduate students at Baise University.