Towards understanding the effective design of automated formative feedback for programming assignments
Qiang Hao, David H. Smith, Lu Ding, Amy J. Ko, Camille Ottaway, Jack Wilson, Kai Arakawa, Alistair Turcan, Timothy Poehlman, Tyler Greer
Abstract
Background and Context: automated feedback for programming assignments has great potential in promoting just-in-time learning, but there has been little work investigating the design of feedback in this context.Objective: to investigate the impacts of different designs of automated feedback on student learning at a fine-grained level, and how students interacted with and perceived the feedback.Method: a controlled quasi-experiment of 76 CS students, where students of each group received a different combination of three types of automated feedback for their programming assignments.Findings: feedback addressing the gap between expected and actual outputs is critical to effective learning; feedback lacking enough details may lead to system gaming behaviors.Implications: the design of feedback has substantial impacts on the efficacy of automated feedback for programming assignments; more research is needed to extend what is known about effective feedback design in this context.