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.

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A model for predicting student performance on introductory programming modules is presented. The model uses attributes identified in a study carried out at four third-level institutions in the Republic of Ireland. Four instruments were used to collect the data and over 25 attributes were examined. A data reduction technique was applied and a logistic regression model using 10-fold stratified cross validation was developed. The model used three attributes: Leaving Certificate Mathematics result (final mathematics examination at second level), number of hours playing computer games while taking the module and programming self-esteem. Prediction success was significant with 80% of students correctly classified. The model also works well on a per-institution level. A discussion on the implications of the model is provided and future work is outlined.

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