Data Analytics and STEM Student Success: The Impact of Predictive Analytics-Informed Academic Advising Among Undeclared First-Year Engineering Students

Autor:	Yu (April) Chen, Sylvester Upah
Rok vydání:	2018
Předmět:	Self-efficacy Medical education Higher education business.industry 05 social sciences 050301 education Academic achievement Predictive analytics Academic advising Education Engineering education Propensity score matching ComputingMilieux_COMPUTERSANDEDUCATION Data analysis business 0503 education
Zdroj:	Journal of College Student Retention: Research, Theory & Practice. 22:497-521
ISSN:	1541-4167 1521-0251
DOI:	10.1177/1521025118772307
Popis:	Science, Technology, Engineering, and Mathematics student success is an important topic in higher education research. Recently, the use of data analytics in higher education administration has gain popularity. However, very few studies have examined how data analytics may influence Science, Technology, Engineering, and Mathematics student success. This study took the first step to investigate the influence of using predictive analytics on academic advising in engineering majors. Specifically, we examined the effects of predictive analytics-informed academic advising among undeclared first-year engineering student with regard to changing a major and selecting a program of study. We utilized the propensity score matching technique to compare students who received predictive analytics-informed advising with those who did not. Results indicated that students who received predictive analytics-informed advising were more likely to change a major than their counterparts. No significant effects was detected regarding selecting a program of study. Implications of the findings for policy, practice, and future research were discussed.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d8725d0792b75d38a081fe0a9bce83c4 https://doi.org/10.1177/1521025118772307 Zobrazit plný text záznamu