Assessment of self-regulated attitudes and behaviors of introductory programming students

2012 ◽  
Author(s):  
Ana Paula Ambrosio ◽  
Leandro Almeida ◽  
Amanda Franco ◽  
Scheila Martins ◽  
Fouad Georges
Keyword(s):  
Introductory Programming ◽  
Attitudes And Behaviors ◽  
Programming Students ◽  
And Behaviors

An automatic tutor for introductory programming students

1975 ◽  
Vol 7 (1) ◽  
pp. 47-50 ◽  
Author(s):  
Ronald L. Danielson ◽  
Jurg Nievergelt
Keyword(s):  
Introductory Programming ◽  
Programming Students

scholarly journals Machine Learning Prediction and Recommendation Framework to Support Introductory Programming Course

2021 ◽  
Vol 16 (17) ◽  
pp. 42
Author(s):  
Ijaz Muhammad Khan ◽  
Abdul Rahim Ahmad ◽  
Nafaa Jabeur ◽  
Mohammed Najah Mahdi
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Early Stage ◽  
Introductory Programming ◽  
Machine Learning Classifiers ◽  
Programming Students ◽  
New Students ◽  
Precautionary Measures ◽  
Introductory Programming Courses ◽  
Programming Courses

The new students struggle to understand the introductory programming courses, due to its intricate nature, which results in higher dropout and increased failure rates. Despite implementing productive methodologies, the instructor struggles to identify the students with distinctive levels of skills. The modern institutes are looking for technology-equipped practices to classify the students and prepare personalized consultation procedures for each class. This paper applies decision tree-based machine learning classifiers to develop a prediction model competent to forecast the outcome of the introductory programming students at an early stage of the semester. The model is then transformed into an adaptive consultation framework which generates three types of colored signals; red, yellow, and green which illustrates whether the student is performing low, average, or high respectively. This provides an opportunity for the instructor to set precautionary measures for low performing students and set complicated tasks that help the highly skilled students to improve their skills further. The experiments compare a set of decision tree-based classifiers and conclude J48 as an efficient model in classifying students in all classes with high accuracy, sensitivity, and F-measure. Even though the aim of the research is to focus on introductory programming courses, however, the framework is flexible and can be implemented in other courses.

An automatic tutor for introductory programming students

1975 ◽  
Author(s):  
Ronald L. Danielson ◽  
Jurg Nievergelt
Keyword(s):  
Introductory Programming ◽  
Programming Students
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