Using Data Mining Techniques to Develop a Model for Scratch Programming Assessment
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Abstract
There is a lot of educational data, which is big data.
A few research has been conducted in a field of educational data mining in order to predict academic achievement in programming at secondary level.
The objectives of this research were to develop a model for learning assessment of Mathayomsuksa 1 students, which consider from Scratch projects using Data Mining, and to test the model performance. This research chose to apply CRISP-DM data mining framework to develop models using three classification techniques: Naïve Bayes, Decision Tree and K-Nearest Neighbor, and to validate these models using a 10-fold cross validation technique. The input of the model development was 113 samples of Scratch projects, which was divided into a training data set and a testing data set. The developed models using the complexity features of Scratch projects consisted of 9 features as the predictor variables, and grades were the target variable.
The results showed that the Decision Tree technique was able to predict the most effective results out of the three models, which had an accuracy of 93.67%, a precision of 93.47%, a recall of 85.71% and a F-measurement of 87.24%. Testing the model performance using the testing data set, was found that an accuracy of all data in prediction was 64.71%. The prediction model can be used as a learning assessment tool for teachers in computing science courses.
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