Predicting Student Success in an Introductory Programming Course at an Urban Midwestern Community College with Computer Programming Experience, Self-Efficacy, and Hope

Date of Award

2021

Degree Name

Ph.D. in Educational Leadership

Department

School of Education and Health Sciences

Advisor/Chair

Charles J. Russo

Abstract

Abstract Predicting Student Success in an Introductory Programming Course at an Urban Midwestern Community College with Computer Programming Experience, Self-Efficacy, and Hope Name: Newman, Reece Elton University of Dayton Advisor: Dr. Charles J. Russo This study of a convenience sample of 66 Introductory Computer Programming students at an urban Midwestern community college used age, computer programming experience, self-efficacy, and hope to predict overall course score. The age, computer programming experience, self-efficacy, and hope frequency distributions were not statistically normal or Gaussian in the sample. Computer programming experience statistically significantly correlated with both computer programming self-efficacy and computer programing hope. Age and computer programming experience, age and computer programming self-efficacy, and age and computer programming hope did not statistically significantly correlate. Computer programming self-efficacy and computer programming hope did not statistically significantly correlate. Relations between age and overall course score, computer programming experience and overall course score, computer programming self-efficacy and overall course score, and computer programming hope and overall course score were nonlinear, so the assumptions for correlation, simple linear regression, and hierarchical multiple linear regression did not hold for the sample data. Correlational, simple regression, and multiple hierarchical regression results were not statistically significant, nor were Student's independent samples t-tests, one-way ANOVAs, and twoway 2 X 2 and 3 X 2 ANOVAs. Despite the overall lack of statistical significance in the findings, there were novel contributions to human knowledge discovered through the observational study of the sample data. Instrument response patterns were internally consistent, providing evidence that the instruments are reliable in the introductory computer programming community college student sample. There were clustering and clear trends in the data indicating a broad range of responses to each instrument. The highly heterogeneous community college population was quite clearly distinct and different from much more homogeneous four-year college and university student populations.

Keywords

Educational Leadership, Higher Education Administration, Education, Computer Science, predicting student success, introductory computer programming, urban Midwestern community college, age, computer programming experience, computer programming self-efficacy, computer programming hope

Rights Statement

Copyright © 2021, author.

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