None Deep Learning Based Analysis of Student Aptitude for Programming at College Freshman Level
Editor: Prof. Dr. Javier González Argote, https://orcid.org/0000-0003-0257-1176
Keywords:Freshman students, aptitude for programming, Machine learning, K Nearest Neighbor (KNN), Backpropa- gation Neural Network (BPN), Recurrent Neural Network (RNN) and Gated Recurrent Unit (GNN)
Predicting Freshman student’s aptitude for computing is critical for researchers to understand the underlying aptitude for programming. Dataset out of a questionnaire taken from various Senior students in a high school in the city of Kanchipuram, Tamil Nadu, India was used, where the questions related to their social and cultural backgrounds and their experience with computers. Several hypotheses were also generated. The datasets were analyzed using three machine learning algorithms namely, Backpropagation Neural Network (BPN) and Recurrent Neural Network (RNN) (and its variant, Gated Recurrent Network (GNN)) with K-Nearest Neighbor (KNN) used as the classifier. Various models were obtained to validate the underpinning set of hypotheses clusters. The results show that the BPN model achieved a high degree of accuracies on various metrics in predicting Freshman student’s aptitude for computer programming.
Narasimhan L, Basupi G. Deep Learning Based Analysis of Student Aptitude for Programming at College Freshman Level 2023.
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