Deep Learning Based Analysis of Student Aptitude for Programming at College Freshman Level

Abstract

Predicting Freshman student’s aptitude for compu-ting 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 back-grounds and their experience with computers. Several hy-potheses were also generated. The datasets were analyzed using three machine learning algorithms namely, Back-propagation Neural Network (BPN) and Recurrent Neural Network (RNN) (and its variant, Gated Recurrent Network (GNN)) with K-Nearest Neighbor (KNN) used as the clas-sifier. Various models were obtained to validate the under-pinning 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.