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Examining the role of self-regulated learning on introductory programming performance
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Source International Computing Education Research Workshop archive
Proceedings of the first international workshop on Computing education research table of contents
Seattle, WA, USA
Pages: 81 - 86  
Year of Publication: 2005
ISBN:1-59593-043-4
Authors
Susan Bergin  National University of Ireland Maynooth, Maynooth, Co. Kildare
Ronan Reilly  National University of Ireland Maynooth, Maynooth, Co. Kildare
Desmond Traynor  National University of Ireland Maynooth, Maynooth, Co. Kildare
Sponsors
ACM: Association for Computing Machinery
SIGCSE: ACM Special Interest Group on Computer Science Education
Publisher
ACM  New York, NY, USA
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ABSTRACT

The purpose of this study was to investigate the relationship between self-regulated learning (SRL) and introductory programming performance. Participants were undergraduate students enrolled in an introductory computer programming module at a third-level (post-high school) institution. The instrument used in this study was designed to assess the motivations and learning strategies (cognitive, metacognitive and resource management strategies) of college students. The data gathered was analyzed to determine if a relationship existed between self-regulation and programming performance and investigate if SRL could be used to predict performance on the module. The study found that students who perform well in programming use more metacognitive and resource management strategies than lower performing students. In addition, students who have high levels of intrinsic motivation and task value perform better in programming and use more metacognitive and resource management strategies than students with low levels of intrinsic motivation and task value. Finally, a regression model based on cognitive, metacognitive and resource management strategies was able to account for 45% of the variance in programming performance results.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Susan Bergin: colleagues
Ronan Reilly: colleagues
Desmond Traynor: colleagues