Graduate Studies, UNL

 

Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2023–

First Advisor

Leen-Kiat Soh

Degree Name

Doctor of Philosophy (Ph.D.)

Committee Members

Ashok Samal, Justin Olmanson, Stephen Cooper

Department

Computer Science

Date of this Version

2025

Document Type

Dissertation

Citation

A dissertation presented to the faculty of the Graduate College of the University of Nebraska in partial fulfillment of requirements for the degree Doctor of Philosophy (Ph.D.)

Major: Computer Science

Under the supervision of Professor

Lincoln, Nebraska, December 2025

Comments

Copyright 2025, the author. Used by permission

Abstract

Introductory programming courses are foundational to developing students’ problem-solving abilities and shaping their persistence in computing pathways. Engagement with programming tasks plays a central role in student learning and experience. Many research measures, including self-reports and code submissions, offer only a limited view of student engagement with programming tasks. This dissertation leverages programming process data, consisting of keystrokes and compilation events, to capture the programming process as it unfolds and to investigate observable programming behaviors. Guided by educational theories, three studies examine how students’ programming behaviors vary across instructional and assessment contexts, how they relate to motivational profiles, and how specific behaviors, such as code pasting, can support or hinder learning. Across these studies, programming process data reveals patterns in persistence, pausing, error resolution, and reliance on external resources. The findings suggest that student motivations are enacted in engagement behaviors and that inauthentic engagement during assignments—especially over-reliance on AI—can undermine learning and leave students unprepared for secure programming assessments. By connecting observable programming behaviors with established educational frameworks, this dissertation provides empirical evidence to advance the computing education literature, while informing instructional design to promote authentic engagement and success in computer science.

Advisor: Leen-Kiat Soh

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