Engaging Students to Learn Coding in the AI Era with Emphasis on the Process - Arendes, Kate; Kerkhoff, Shea; Adhikari, Badri (Papanda Publisher, 2024)
Students learning to code for the first time face several challenges. For instance, they struggle to interpret the error messages they see when their code fails to run. Since teaching standards in coding are focused primarily on whether a student’s code runs successfully, students are often penalized in their grades not for the effort they put into their work but for the code they turn in. Automated grading tools deployed in educational institutions, unfortunately, make the issue worse. In this work, we discuss a novel approach to motivate students to learn coding by shifting the focus of both educators and students from outcomes to the process behind the outcomes. In this study, educators and students are introduced to a new tool, Process Feedback (PF), which shows each student’s work as a visual journey. After using both qualitative and quantitative data collection and analysis, the paper discusses how using PF can help students code insightfully and help educators grade quickly and thoroughly. Our findings reveal that tools such as PF can be beneficial in addressing challenges in learning to code and encouraging students to be original in the age of AI. The results also imply that the incorporation of process-oriented learning tools can make coding education more effective and that the process-centric approach can play a key role in the development and effectiveness of educational tools for students.