Academic performance analysis to support proactive student advising for an electrical engineering program
by Richelle V. Adams; Cathy-Ann Radix
International Journal of Quantitative Research in Education (IJQRE), Vol. 5, No. 1, 2020

Abstract: Using correlation, regression and hierarchical clustering methods, the authors examined three consecutive graduating cohorts of students in an electrical and computer engineering undergraduate program to determine which courses (or groups of courses) were the best predictors of graduation GPA. The aim was to develop predictive models that support a consistent proactive advising experience. The main impact of this study is the methodology which can be applied to other programs with similar weighted GPA schemes and with limited data sources. Other impacts were: the model identified which types of courses impacted GPA performance most, bringing clarity as to where cohort-wide intervention may be required; and the model can help us identify earlier 'at-risk' and 'exceptional' students.

Online publication date: Wed, 15-Apr-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Quantitative Research in Education (IJQRE):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com