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Mathematics
Special Issues
Data Science Education: Recent Advances and Future Challenges
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Data Science Education: Recent Advances and Future Challenges

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 2465

Special Issue Editors

Prof. Dr. José Miguel Contreras García

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Guest Editor
Department of Didactics of Mathematics, University of Granada, 18010 Granada, Spain
Interests: data science education; statistics education; mathematics education; primary and secondary teachers' education
Prof. Dr. Elena Molina Portillo

E-Mail Website
Guest Editor
Department of Didactics of Mathematics, University of Granada, 18010 Granada, Spain
Interests: data science education; statistics education; mathematics education; primary and secondary teachers' education

Special Issue Information

Dear Colleagues,

Data science has emerged as an interdisciplinary field that attempts to make sense of the exponential growth of data in society and our dependence on it.

Since its formal conception as an area, data science has drawn on techniques from many research fields, highlighting statistics, mathematics, information science, and computer science. In recent years, the academic community is making an effort to establish the foundations of the educational aspects related to this discipline. Thus, traditional research related to mathematics or statistics education is conditional upon new guidelines that emphasize data-based information—structured or not, large or small—that can be analyzed from different viewpoints, including statistics, data mining, machine learning, and predictive analytics, among others.

In this Special Issue we want to address the bases for the construction of a specific framework for data science education, focused on good practices for teaching and learning in this area. 

Dr. José Miguel Contreras García
Dr. Elena Molina Portillo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data science education
  • statistical, mathematical and computational thinking
  • teacher training

Published Papers (1 paper)

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Research

23 pages, 1260 KiB  
Article
Epistemic Configurations and Holistic Meaning of Binomial Distribution
by Nicolás Alonso Fernández Coronado, Jaime I. García-García, Elizabeth H. Arredondo and Ismael Andrés Araya Naveas
Mathematics 2022, 10(10), 1748; https://doi.org/10.3390/math10101748 - 20 May 2022
Viewed by 1522
Abstract
The competencies that today’s citizen must possess have led to a transformation of the teaching of probability, which has been repositioned on the school curriculum from an algorithmic view to a practical one based on the understanding of the concepts and their application [...] Read more.
The competencies that today’s citizen must possess have led to a transformation of the teaching of probability, which has been repositioned on the school curriculum from an algorithmic view to a practical one based on the understanding of the concepts and their application in daily life. In this task, the understanding of the binomial distribution is essential as it allows the analysis of discrete data, the modeling of random situations, and the learning of other notions. However, weaknesses are identified in teachers and students with respect to the binomial distribution attributed to the lack of knowledge of its origin and meaning throughout history. For this reason, our work consists of the identification of its partial meanings and essential components as well as its relationships from a historical epistemological study and its analysis, based on the notions of the Ontosemiotic Approach (OSA) to Mathematical Knowledge and Instruction and the specialized literature on statistics and probability. As a result of our work, we present a reconstruction of the holistic meaning of the binomial distribution from the elements that compose it, which are essential for didactic purposes such as the identification and resolution of learning conflicts, the design or evaluation criteria, and teacher education. Full article
(This article belongs to the Special Issue Data Science Education: Recent Advances and Future Challenges)
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