2026 ASEE Annual Conference & Exposition

(Theory/Methods, Full Paper) The reliance on highly structured, syntactically focused assignments in introductory programming often limits students' exposure to ill-defined, complex, and real-world problems. This narrow focus can hinder transferability, the critical skill of applying computational concepts to novel contexts, and negatively impact student motivation and creative problem definition. Addressing this gap is essential for preparing students for subsequent coursework and professional engineering environments.
This paper presents a systematic investigation into the impact of integrating research-inspired, open-ended programming projects into a high-enrollment, lower-division CS course. The study is currently ongoing with 89 students enrolled at a large central public university over eight weeks. The primary research objective is to assess whether this project-based approach significantly enhances students' conceptual understanding, intrinsic engagement, and perceived self-efficacy compared to traditional assignment structures.
Research Framework and Methodology
The research methodology is grounded in providing strong, structured support mechanisms to guide novice programmers through the inherent ambiguity of open-ended design. This includes clearly defined milestones, mandatory peer feedback sessions, mentoring sessions, and brainstorming ideas to tackle the open-ended project, along with a basic architectural design document before full implementation. This intentional structuring is vital, as prior work in project-based learning has shown that managing complexity improves collaboration, critical thinking, and algorithmic reasoning. Furthermore, studies focusing on support for open-ended projects show that reducing common novice barriers (e.g., decision and search hurdles) leads to greater self-efficacy and better integration of learned concepts.
Assessment utilizes a mixed-methods approach:
1. Quantitative: Performance data from project deliverables and scores from conceptual understanding tests.
2. Qualitative: Mid- and end-of-project reflective surveys measuring motivation, persistence, and perceived competence.
The data will allow us to analyze how varying degrees of project independence affect different student populations, particularly those entering with weaker prior programming experience.
In the full paper, we hope to provide preliminary results detailing project outcomes, shifts in student self-reports regarding motivation and persistence, and qualitative analysis of student feedback on the project structure. The findings will offer practical, evidence-based recommendations for faculty seeking to embed deep, authentic, research-inspired work into their introductory computer science curricula to cultivate robust computational thinking skills in early career engineers.
Keywords: Project-Based Learning, Introductory Programming, Transfer Learning, Curriculum Design, Student Engagement