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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
Language: English
Authors: Hao, Qiang (ORCID 0000-0001-6361-5035), Smith, David H., IV, Ding, Lu, Ko, Amy, Ottaway, Camille, Wilson, Jack, Arakawa, Kai H., Turcan, Alistair, Poehlman, Timothy, Greer, Tyler
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
FullText Text:
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  Data: Towards Understanding the Effective Design of Automated Formative Feedback for Programming Assignments
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  Data: <searchLink fieldCode="AR" term="%22Hao%2C+Qiang%22">Hao, Qiang</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0001-6361-5035">0000-0001-6361-5035</externalLink>)<br /><searchLink fieldCode="AR" term="%22Smith%2C+David+H%2E%2C+IV%22">Smith, David H., IV</searchLink><br /><searchLink fieldCode="AR" term="%22Ding%2C+Lu%22">Ding, Lu</searchLink><br /><searchLink fieldCode="AR" term="%22Ko%2C+Amy%22">Ko, Amy</searchLink><br /><searchLink fieldCode="AR" term="%22Ottaway%2C+Camille%22">Ottaway, Camille</searchLink><br /><searchLink fieldCode="AR" term="%22Wilson%2C+Jack%22">Wilson, Jack</searchLink><br /><searchLink fieldCode="AR" term="%22Arakawa%2C+Kai+H%2E%22">Arakawa, Kai H.</searchLink><br /><searchLink fieldCode="AR" term="%22Turcan%2C+Alistair%22">Turcan, Alistair</searchLink><br /><searchLink fieldCode="AR" term="%22Poehlman%2C+Timothy%22">Poehlman, Timothy</searchLink><br /><searchLink fieldCode="AR" term="%22Greer%2C+Tyler%22">Greer, Tyler</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Computer+Science+Education%22"><i>Computer Science Education</i></searchLink>. 2022 32(1):105-127.
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  Data: 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
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  Data: 0899-3408<br />1744-5175
– Name: Abstract
  Label: Abstract
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  Data: 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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