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Blending Measures of Programming and Social Behavior into Predictive Models of Student Achievement in Early Computing Courses

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Title: Blending Measures of Programming and Social Behavior into Predictive Models of Student Achievement in Early Computing Courses
Language: English
Authors: Carter, Adam S., Hundhausen, Christopher D., Adesope, Olusola
Source: ACM Transactions on Computing Education. Aug 2017 17(3).
Availability: Association for Computing Machinery. 2 Penn Plaza Suite 701, New York, NY 10121. Tel: 800-342-6626; Tel: 212-626-0500; Fax: 212-944-1318; e-mail: acmhelp@acm.org; Web site: http://toce.acm.org/
Peer Reviewed: Y
Page Count: 20
Publication Date: 2017
Sponsoring Agency: National Science Foundation (NSF)
Contract Number: IIS1321045
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Social Behavior, Academic Achievement, Programming, Computer Science Education, Assignments, Outcomes of Education, Online Courses, Correlation, Learning Theories, Prediction, Learning Processes, Undergraduate Students, Course Descriptions, Grades (Scholastic), Student Participation, Computer Mediated Communication, Group Discussion, Measurement Techniques, Validity
Geographic Terms: Washington
DOI: 10.1145/3120259
ISSN: 1946-6226
Abstract: Analyzing the process data of students as they complete programming assignments has the potential to provide computing educators with insights into both their students and the processes by which they learn to program. In prior research, we explored the relationship between (a) students' programming behaviors and course outcomes, and (b) students' participation within an online social learning environment and course outcomes. In both studies, we developed statistical measures derived from our data that significantly correlate with students' course grades. Encouraged both by social theories of learning and a desire to improve the accuracy of our statistical models, we explore here the impact of incorporating our predictive measure derived from social behavior into three separate predictive measures derived from programming behaviors. We find that, in combining the measures, we are able to improve the overall predictive power of each measure. This finding affirms the importance of social interaction in the learning process, and provides evidence that predictive models derived from multiple sources of learning process data can provide significantly better predictive power by accounting for multiple factors responsible for student success.
Abstractor: As Provided
Entry Date: 2020
Accession Number: EJ1252443
Database: ERIC
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  Data: Blending Measures of Programming and Social Behavior into Predictive Models of Student Achievement in Early Computing Courses
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  Data: Association for Computing Machinery. 2 Penn Plaza Suite 701, New York, NY 10121. Tel: 800-342-6626; Tel: 212-626-0500; Fax: 212-944-1318; e-mail: acmhelp@acm.org; Web site: http://toce.acm.org/
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– Name: Abstract
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  Data: Analyzing the process data of students as they complete programming assignments has the potential to provide computing educators with insights into both their students and the processes by which they learn to program. In prior research, we explored the relationship between (a) students' programming behaviors and course outcomes, and (b) students' participation within an online social learning environment and course outcomes. In both studies, we developed statistical measures derived from our data that significantly correlate with students' course grades. Encouraged both by social theories of learning and a desire to improve the accuracy of our statistical models, we explore here the impact of incorporating our predictive measure derived from social behavior into three separate predictive measures derived from programming behaviors. We find that, in combining the measures, we are able to improve the overall predictive power of each measure. This finding affirms the importance of social interaction in the learning process, and provides evidence that predictive models derived from multiple sources of learning process data can provide significantly better predictive power by accounting for multiple factors responsible for student success.
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  Data: 2020
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  Data: EJ1252443
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        Value: 10.1145/3120259
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    Subjects:
      – SubjectFull: Social Behavior
        Type: general
      – SubjectFull: Academic Achievement
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      – SubjectFull: Programming
        Type: general
      – SubjectFull: Computer Science Education
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      – SubjectFull: Assignments
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      – TitleFull: Blending Measures of Programming and Social Behavior into Predictive Models of Student Achievement in Early Computing Courses
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