Development of competencies: artificial intelligence and machine learning in supervised internships of computer science students
DOI:
https://doi.org/10.15359/ru.39-1.3Keywords:
Artificial intelligence, machine learning, convolution neural networks, knowledge, Computer Science and Information TechnologyAbstract
[Objective] The objective of this research focuses on developing competencies in artificial intelligence and machine learning for students wishing to experiment in the creation of machine learning models. [Methodology] The study was conducted in the Digital Image Processing Laboratory of Universidad Nacional de Costa Rica, during the first cycle of 2023 by faculty and students doing their internships. For this exploratory-applied research, a step-by-step methodology was created and used to classify benign and malignant mammograms by developing a machine-learning model. A data set comprised of 118 previously diagnosed mammogram images was used. [Results] Results are divided into two areas. The first area is the systematization of the teaching and learning process based on competencies, learning results, and the rubric to evaluate learning in artificial intelligence, machine learning, and neural networks. The second area involves the products generated by developing the computer application Mammography Classification Model, which integrates a convolution neural network that includes image transformation, creation, and training. [Conclusions] It is concluded that, in the academic field, student-centered learning models can be innovated to strengthen skills that enhance their professional profile. In the medical field, the classification of mammograms is an opportunity to develop competencies in a real environment.
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