The Academic Advising Issue

PurposeCoaching in higher education has become increasingly common across the United States. Our qualitative study explores the perceptions of coaches and advisors, as they consider academic coaching as a role distinct from academic advising.Design/methodology/approachOur study adopts a qualitative research approach. Two focus groups were conducted with 14 coaching and academic advising professionals.FindingsOur findings identify at least three major themes when considering academic coaching as a role distinct from academic advising: (1) Potential role overlap, (2) Caseload disparities and (3) Philosophical differences. The indiscriminate use of the title of “coach” contributed to confusion, ambiguity and tension.Practical implicationsWithout a clear understanding of the coach role as a distinct type of support in higher education, confusion and ambiguity are likely to continue.Originality/valueNo studies have explored the perceptions of coaches and advisors, as they consider academic coaching as a role distinct in the United States.

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

Journal of College Student Retention Research Theory & Practice ◽

10.1177/1521025118772307 ◽

2018 ◽

Vol 22 (3) ◽

pp. 497-521 ◽

Cited By ~ 1

Author(s):

Yu (April) Chen ◽

Sylvester Upah

Keyword(s):

Higher Education ◽

Student Success ◽

Academic Advising ◽

Data Analytics ◽

Predictive Analytics ◽

First Year ◽

Program Of Study ◽

Science Technology ◽

Mathematics Student ◽

And Mathematics

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.

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