Visual Web Tools for Studying Machine Learning in Higher Education: A Systematic Mapping
Abstract
The systematic mapping conducted in this study identified eight visual web tools for studying machine learning (ML) in higher education, highlighting their educational characteristics, ML resources offered, visual elements used to facilitate studies and evaluation methodologies. The results revealed that the tools do not require users to have any experience in ML and that most of the tools are used to demonstrate the workings of neural networks, with image classification being the most commonly addressed task. The main contribution lies in the characterization of accessible web-based visual tools for studying ML, intending to help educators and students select those that best suit their interests and study contexts.
Keywords:
visual web tools, machine learning, systematic mapping
References
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Estevez, J. et al. (2019). Using Scratch to teach undergraduate students’ skills on artificial intelligence, arXiv preprint arXiv:1904.00296.
Goodfellow, I. et al. (2016). Deep learning, The Mit Press.
Harley, A. W. (2015). “An interactive node-link visualization of convolutional neural networks”, In: Bebis, G. et al. Advances in Visual Computing, ISVC 2015, Lecture Notes in Computer Science, vol 9474. Springer, Cham.
Kahng, M. et al. (2018). “GAN lab: Understanding complex deep generative models using interactive visual experimentation”, IEEE Transactions on Visualization and Computer Graphics, v. 25, n. 1, p. 310-320.
Keele, S. et al. (2007). Guidelines for performing systematic literature reviews in software engineering, EBSE Technical Report.
Kahn, K. et al. (2020). Deep learning programming by all, Constructionism.
Lalitha, T. B. and Sreeja, P. S. (2021). “Recommendation system based on machine learning and deep learning in varied perspectives: A systematic review”, In: Kaiser, M. S., Xie, J., Rathore, V.S. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2020), Lecture Notes in Networks and Systems, v. 190, Springer, Singapore.
Mitchell, T. M. (1997). Machine learning, McGraw-Hill.
Parekh, D. et al. (2022). “Review on autonomous vehicles: Progress, methods and challenges. Electronics”, v. 11, p. 2162.
Petersen, K. et al. (2015). “Guidelines for conducting systematic mapping studies in software engineering: an update”, Information and Software Technology, v. 64, p. 1-18.
Raschka, S. et al. (2020). “Machine learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence”, Information, v. 11, n. 4, p. 193.
Schultze, S. et al. (2020). “Demystifying deep learning: Developing a learning App for beginners to gain practical experience”, In: Proceedings of the Mensch und Computer 2020 Workshop on User-Centered Artificial Intelligence (UCAI 2020).
Silva Filho, F. R. et al. (2023). “Uso de aprendizado de máquina em fóruns de ambientes virtuais de aprendizagem: Uma revisão sistemática de literatura”, RENOTE, v. 21, n. 2, p. 220–233.
Smilkov, D. et al. (2017). Direct-manipulation visualization of deep networks, arXiv preprint arXiv:1708.03788.
Wang, Z. J. et al. (2020). “CNN explainer: Learning convolutional neural networks with interactive visualization”, IEEE Transactions on Visualization and Computer Graphics, v. 27, n. 2, p. 1396-1406.
Wangenheim, C. G. V. et al. (2021). “Visual tools for teaching machine learning in K‑12: A ten‑year systematic mapping”, Education and Information Technologies, v. 26, n. 5, p. 5733–5778, 2021.
Zhang, A. et al. (2022). “Shifting machine learning for healthcare from development to deployment and from models to data”, Nature Biomedical Engineering, v. 6, p. 1330–1345.
Published
2024-11-04
How to Cite
SILVA, Denis W. da; BARBOSA, Luiz Carlos B.; SEABRA, Rodrigo D..
Visual Web Tools for Studying Machine Learning in Higher Education: A Systematic Mapping. In: BRAZILIAN SYMPOSIUM ON COMPUTERS IN EDUCATION (SBIE), 35. , 2024, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 264-275.
DOI: https://doi.org/10.5753/sbie.2024.242426.
