The Impact of Data-Driven Feedback on Programming Learning: A Cognitive Load Perspective

Data-driven feedback is considered an effective technique to support teaching introductory programming. However, variations in students’ cognitive load may affect its effectiveness. This study examined the impact of data-driven feedback on middle school students’ learning performance in an introductory Python programming course, framed within the context of cognitive load theory. The findings revealed that students in the low cognitive load group achieved higher programming scores than those in the medium and high cognitive load groups. Moreover, the medium cognitive load group achieved the highest code Improvement Rate. Finally, students’ evaluation of feedback emerged as a key determinant of programming success.