Towards understanding the effective design of automated formative feedback for programming assignments

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. to investigate the impacts of different designs of automated feedback on student learning at a fine-grained leve...

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Vydáno v:Computer science education Ročník 32; číslo 1; s. 105 - 127
Hlavní autoři: Hao, Qiang, Smith IV, David H., Ding, Lu, Ko, Amy, Ottaway, Camille, Wilson, Jack, Arakawa, Kai H., Turcan, Alistair, Poehlman, Timothy, Greer, Tyler
Médium: Journal Article
Jazyk:angličtina
Vydáno: Norwood Routledge 02.01.2022
Taylor & Francis Ltd
Témata:
Assignments
Automated feedback
Automation
Comparative Analysis
Computer Science Education
Computer Software
Context
Feedback
Feedback (Response)
Formative Evaluation
formative feedback
Grading
Instructional Effectiveness
Learning
Likert Scales
Programming
programming assignments
Student Attitudes
Students
system gaming behavior
Teaching Methods
Undergraduate Students
ISSN:0899-3408, 1744-5175
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Abstract 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. 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. 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. feedback addressing the gap between expected and actual outputs is critical to effective learning; feedback lacking enough details may lead to system gaming behaviors. 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.
AbstractList 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. 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. 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. feedback addressing the gap between expected and actual outputs is critical to effective learning; feedback lacking enough details may lead to system gaming behaviors. 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.
Background and Contextautomated 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.Objectiveto 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.Methoda 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.Findingsfeedback addressing the gap between expected and actual outputs is critical to effective learning; feedback lacking enough details may lead to system gaming behaviors.Implicationsthe 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.
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.
Audience Higher Education
Postsecondary Education
Author Arakawa, Kai H.
Smith IV, David H.
Ottaway, Camille
Hao, Qiang
Turcan, Alistair
Ding, Lu
Greer, Tyler
Wilson, Jack
Ko, Amy
Poehlman, Timothy
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Snippet automated feedback for programming assignments has great potential in promoting just-in-time learning, but there has been little work investigating the design...
Background and Context: automated feedback for programming assignments has great potential in promoting just-in-time learning, but there has been little work...
Background and Contextautomated feedback for programming assignments has great potential in promoting just-in-time learning, but there has been little work...
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Enrichment Source
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StartPage 105
SubjectTerms Assignments
Automated feedback
Automation
Comparative Analysis
Computer Science Education
Computer Software
Context
Feedback
Feedback (Response)
Formative Evaluation
formative feedback
Grading
Instructional Effectiveness
Learning
Likert Scales
Programming
programming assignments
Student Attitudes
Students
system gaming behavior
Teaching Methods
Undergraduate Students
Title Towards understanding the effective design of automated formative feedback for programming assignments
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Volume 32
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