The Holistic Course Delivery: A Novel Pedagogy for Collegiate Introductory Computer Programming
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For many years there have not been enough computer science graduates to fill open positions. One of the chief barriers to the formation of computer science graduates is that many students are unsuccessful in the introductory programming course. Unsuccessful students often change their major field of study or terminate their collegiate studies. A chief concern is therefore to minimize the DFW rate (grade of D or F, or withdrawal from a course). Student characteristics have been extensively studied to explain, and sometimes justify, the high DFW rate in introductory programming courses. Pairs programming, flipped classrooms, choice of programming language, and a variety of other modifications and novel methods have been devised in efforts to reduce the DFW rate. The collective conclusion has been that there is no silver bullet that has been demonstrated to be universally effective. This quasi-experimental study incorporates four learning theories that inform the design and delivery of an introductory programming course: Neo-Piagetian Theory, Cognitive Apprenticeship Theory, Cognitive Load Theory, and Self-Efficacy Theory. The objective was iv to (1) design a course from the top-down that integrates several pedagogical elements in a holistic way, and (2) deliver it to a group of nascent programming students. The Holistic Course Delivery was implemented in three class sections of an introductory programming course at a midwestern university in which a total of 96 students were enrolled. The Holistic Course Delivery had a significantly lower DFW rate compared to both historic DFW rates at the institution and established international norms and students indicated they felt prepared for subsequent computer science coursework.
Table of Contents
Introduction -- Literature review -- Methodology -- Analysis -- Discussion -- Appendix A. Pre-course survey -- Appendix B. Pre-course test & post-course test -- Appendix C. Post-course survey Appendix D. Homework reflection -- Appendix E. Homework assignments -- Appendix F. Coding sprints -- Appendix G. Final exam programming questions -- Appendix H. Qualitative interview questions -- Appendix I. Curriculum outline -- Appendix J. Syllabus
Ph.D. (Doctor of Philosophy)