Towards Understanding the Effective Design of Automated Formative Feedback for Programming Assignments
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| Title: | Towards Understanding the Effective Design of Automated Formative Feedback for Programming Assignments |
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| Language: | English |
| Authors: | Hao, Qiang (ORCID |
| Source: | Computer Science Education. 2022 32(1):105-127. |
| Availability: | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: | Y |
| Page Count: | 23 |
| Publication Date: | 2022 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Computer Science Education, Feedback (Response), Teaching Methods, Comparative Analysis, Programming, Assignments, Undergraduate Students, Formative Evaluation, Instructional Effectiveness, Grading, Computer Software, Likert Scales, Student Attitudes |
| DOI: | 10.1080/08993408.2020.1860408 |
| ISSN: | 0899-3408 1744-5175 |
| 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. |
| Abstractor: | As Provided |
| Entry Date: | 2022 |
| Accession Number: | EJ1345047 |
| Database: | ERIC |
| 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. |
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| ISSN: | 0899-3408 1744-5175 |
| DOI: | 10.1080/08993408.2020.1860408 |
