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Salud, Ciencia y Tecnología Metaverse Basic and Applied Research Data & Metadata Interdisciplinary Rehabilitation Salud, Ciencia y Tecnología - Serie de Conferencias Community and Interculturality in Dialogue Seminars in Medical Writing and Education Health Leadership and Quality of Life Gamification and Augmented Reality Southern perspective / Perspectiva austral
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Published: 2023-08-15
DOI: https://doi.org/10.56294/dm202338

None Deep Learning Based Analysis of Student Aptitude for Programming at College Freshman Level

By
Lakshmi Narasimhan ,
Lakshmi Narasimhan

Georgia Southern University

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PubMed | Google Scholar
G. Basupi ,
G. Basupi

University of Botswana, Botswana

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Abstract

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.

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),

How to Cite

1.
Simhan L, Basupi G. None Deep Learning Based Analysis of Student Aptitude for Programming at College Freshman Level. Data and Metadata [Internet]. 2023 Aug. 15 [cited 2024 Jul. 27];2:38. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/38

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The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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Vol. 2 (2023)
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