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ERIC Number: EJ1191151
Record Type: Journal
Publication Date: 2018
Pages: 9
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0146-3934
Using Logistic Regression Model to Identify Student Characteristics to Tailor Graduation Initiatives
Chatterjee, Ayona; Marachi, Christine; Natekar, Shruti; Rai, Chinki; Yeung, Fanny
College Student Journal, v52 n3 p352-360 Fall 2018
Improving graduation rates is one of the biggest missions in many universities across the country and it is surely the case on the campus of this institution. The work here presents a statistical tool box to use early academic performance as a predictor for graduation with logistic regression and machine learning techniques. The methods described in this paper utilized data from one academic cohort across 6 years to identify significant student academic characteristics that are related to graduation. The model can then be applied to current students finishing their freshmen year and assign probabilities to successfully graduate in a pre-determined framework. The study and the significant factors are specific to the institutions' campus but the model allows the study to be replicated on any campus to support graduation initiatives. Early interventions can be most beneficial for students to realign and reorganize their academic path as needed and in our study, results show that total credits accumulated by the end of first year and retention at the end of first year have a significant positive impact on graduation success.
Project Innovation, Inc. P.O. Box 8508 Spring Hill Station, Mobile, AL 36689-0508. Tel: 251-343-1878; Fax: 251-343-1878; Web site: http://www.projectinnovation.com/college-student-journal.html
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: California