Effects of Mastery-Inspired Checkpoint Quizzes in a Large Introductory CS Course: A Mixed Methods Study

Abstract

Students may learn at different paces due to differences in prior programming experience (PPE), physical or mental health challenges, and economic or lifestyle barriers (e.g., employment or caregiving responsibilities). Additionally, increasing use of AI tools for homework can contribute to inaccurate self-assessment and poor preparation for supervised tests. Motivated by these challenges, we introduced bi-weekly low-stakes checkpoint quizzes in a large (~500-student) introductory CS course. Inspired by alternative grading paradigms such as mastery learning (ML), each quiz could be attempted multiple times without penalty, offering students frequent feedback and opportunities for iterative improvement. Our mixed-methods study investigated the impact of these quizzes on performance, self-assessment, stress levels, and overall experience, using performance and survey data (N=456). Results showed that though retake opportunities allowed students to improve quiz performance, frequent retake attempts were associated with lower final exam outcomes, suggesting continued struggle on novel problems. Despite limited performance benefits, survey data revealed strong affective outcomes based on overwhelmingly positive student sentiment: students reported high Likert-scale ratings for learning/engagement and stress reduction value (though subgroup differences by gender, PPE, English fluency, and retake frequency suggest room to improve equity outcomes), and the majority of open-ended responses described the quizzes as helpful for improving self-assessment, reducing stress, and supporting meaningful learning. Overall, our implementation allowed students to experience some benefits of ML while retaining enough structure to prevent procrastination, illustrating how ML?inspired assessment can be incorporated into courses without a full course redesign.