Towards understanding the effective design of automated formative feedback for programming assignments. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Hao, Qiang; Smith IV, David H.; Ding, Lu; Ko, Amy; Ottaway, Camille; Wilson, Jack; Arakawa, Kai H.; Turcan, Alistair; Poehlman, Timothy; Greer, Tyler
• Is Part Of: Computer science education. Volume 32:Issue 1(2022)
• Page Start: 105
• Page End: 127
• Publication Date: 2022-01-02
• Subjects: Automated feedback -- formative feedback -- programming assignments -- system gaming behavior; Computer science -- Study and teaching -- Periodicals; 004
• DOI: 10.1080/08993408.2020.1860408
• Languages: English
• ISSNs: 0899-3408

Abstract

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.