Date of Award

Spring 2012

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Educ Foundations & Leadership

Program/Concentration

Community College Leadership

Committee Director

Steve Myran

Committee Member

Mitchell Williams

Committee Member

Natalie Harder

Abstract

With a large global, national, state, and local drive for post-secondary credentials, higher education institutes are exploring new retention and graduation strategies to meet the needs of the employers and employees. Many students who are unprepared for college level work will enter a community college to take developmental courses. Developmental mathematics has been a large barrier to completion and success in community college.

The purpose of this study was to explore the ability of non-cognitive traits to predict persistence in completion of a developmental math sequence at a community college. Non-cognitive traits were identified from the three components of strategic learning found in the Learning and Study Strategy Inventory (LASSI): (a) the skill component (information processing, selecting main ideas and test strategies), b) the will of the student component (attitude, motivation and anxiety), and (c) the self-regulation component (concentration, time management, self-testing and study aids).

A logistical regression showed the strength of correlation to predict the success of students at a community college. Such factors that were significant in predicting success were age, Pell Grant status, three individual LASSI questions, motivation subscale, testing strategies subscale, and when combined testing and concentration. Even though weak as individual predictors, the use of multiple variables strengthens the prediction.

With an open door policy, colleges need to identify students that are in danger of attrition and provide additional support that will increase the likelihood of their success. Along with prior academic background and demographics, non-cognitive variables and learning strategies can only strengthen predictability of risk of student attrition. With this knowledge, proper and timely intervention strategies can be used to support student success. This research challenges community colleges to target all problem areas in its approach to identify high risk students.

DOI

10.25777/gr65-1z57

ISBN

9781267349040

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