• Corpus ID: 212517413

An Educational Data Mining Model for Predicting Student Performance in Programming Course

  title={An Educational Data Mining Model for Predicting Student Performance in Programming Course},
  author={S K Pal and Murat Koklu and Humar Kahramanli and Novruz Allahverdi and P. Andreeva and Maya Dimitrova and Plamena Adriyan Radeva and L. Ozbakir and Adil Baykasoğlu and Sinem Kulluk},
This paper presents an educational data mining model for predicting student performance in programming courses. Identifying variables that predict student programming performance may help educators. These variables are influenced by various factors. The study engages factors like students' mathematical background, programming aptitude, problem solving skills, gender, prior experience, high school mathematics grade, locality, previous computer programming experience, and e learning usage… 


Modelling programming performance: Beyond the influence of learner characteristics
While this study shows the influence of learner characteristics such as gender, learning styles, and mental models on programming performance, it highlights the effect that prior composite academic ability and medium of instruction exert on learning outcomes, which is uncommon among studies of similar purpose.
Predicting the Benefit of Rule Extraction: A Novel Component in Data Mining
The somewhat surprising answer, found from an empirical study conducted on several publicly available data sets, is that it is possible to predict, from the characteristics of a data set, if rule extraction is likely to produce an accurate model using only a few data set features.
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The empirical results demonstrate that an equivalent symbolic interpretation in the form of rules with predicates, terms and variables can be derived describing the overall behaviour of the trained ANN with improved comprehensibility while maintaining the accuracy and fidelity of the propositional rules.
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