2026 ASEE Annual Conference & Exposition

This paper presents two complementary pedagogical innovations implemented in an introductory programming course (Algorithm Design and Programming I) to enhance student engagement, metacognitive reflection, and ownership of learning. The first innovation, "Bounty Hunting/Pest Control," which is actually debugging sessions, consists of short, gamified exercises conducted weekly at the start of class, where students work collaboratively in pairs or small groups to identify and fix intentional bugs in concise code snippets aligned with recent lecture topics. Successful participants earn symbolic "bounties" recorded, with small, non-grade incentives such as class-branded items awarded at semester's end. This approach promotes authentic, low-stakes problem-solving, normalizes debugging as a core learning process, and fosters a sense of community and motivation without grade pressure. The second innovation, a Grade Weight Adjustment Policy, provides students with structured flexibility in determining the relative weights of course components (labs, exams, and quizzes) within bounded ranges at the end of the semester. This design empowers students to reflect on their strengths, mitigate anxiety associated with assessment, and perceive the grading process as more transparent and equitable. Early feedback suggests these interventions have led to higher participation, improved self-assessment, and greater confidence in programming skills. Together, the two strategies create a learner-centered environment that integrates autonomy, active engagement, and self-regulation, which are key components of effective engineering education. These practices demonstrate how gamification and flexible assessment can coexist within rigorous, large-enrollment STEM courses to strengthen both skill development and student agency, offering an adaptable model for other engineering and computing programs.