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A Journal for Interdisciplinary Data Science Education


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Orit Hazzan and Koby Mike of Technion

https://bit.ly/3eBrHgk April 26, 2021

If your research is in data science and you wish to present your insights and research about the teaching of data science, you probably have no choice but to try submitting your manuscript to a journal that deals with a data science-related topic and is dedicating a special issue to data science education, or to an educational conference on a topic related to data science.

One exception is the Journal of Statistics Education published by The American Statistical Association, which in January changed its name to the Journal of Statistics and Data Science Education. The change reflects the growing attention and importance attributed to data science education. This journal, however, still holds the statistics perspective and targets the statistician community.

In other words, no journal exists today that deals exclusively with data science education, let alone highlights data science education from an inter-disciplinary perspective.

In this blog post, we describe our vision for a journal that would focus on data science education from the interdisciplinarity perspective. In this blog, we will call it The Interdisciplinary Journal of Data Science Education (IJDSE).

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Motivation

Data science is a new interdisciplinary field of research focused on extracting value from data and integrating knowledge and methods from computer science, mathematics and statistics, and the domain knowledge of the data. As an interdisciplinary field, it receives attention from each of the disciplines that comprise it, so the potential of its interdisciplinary nature has not been fully exhausted and should be explored more deeply. We suggest this applies also to the educational aspect of data science.

Accordingly, IJDSE would aim to explore the interdisciplinarity of data science from the education perspective. Such an examination would educate future learners better and more effectively than when the educational perspective is taken up separately by each of the disciplines that make up data science. This statement relies on the working assumption that when data science education is explored from each angle separately, learners are not exposed to the comprehensive picture and interdisciplinary nature of data science needed to use it meaningfully in their personal and professional lives.

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Target Audience

We anticipate professionals and researchers from all disciplines would be interested in the subjects the journal would cover. In addition, since data science is relevant to all industries today and they all need to educate all their employees on how to use data to add value to the firm, they probably would find IJDSE relevant for their growth.

We anticipate a data science education community would form and grow in the coming years, a trend reflected, for example, by the growing number of data science programs recently established in K-12.

Accordingly, IJDSE would:

  • Target the three communities that comprise data science: computer science, math and statistics, and the various data domains (the natural sciences, social science, and digital humanities), and within these communities, scholars interested in data science education.
  • Explore data science education for all levels" from K-12, through undergraduate and graduate programs, to academic scholars and industry professionals.

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Scope and Topics

To capture the interdisciplinarity of data science education, IJDSE would encompass a broad topic list and welcome all research methods. It would publish research papers, case study reports, opinion papers, work-in-progress papers, and commentaries on the following topics:

  • Data science education for all levels: K-12, undergraduate as major or minor, graduate, research, and industry.
    • Data science curriculum and study programs.
    • Design of data science study programs.
    • The structure of data science study programs:
    • Connections between data science education and education in mathematics, statistics, computer science, and the domain of the data.
  • Data science education pedagogy.
    • Pedagogies and approaches to teaching data science to diverse populations.
    • Pedagogy of interdisciplinary education bridging the humanities, social sciences, sciences, and other disciplines.
    • Teaching methods, tools, and practices of data science education:
    • Assignments and tutorials.
    • Class activities.
    • Data visualization and animation tools.
    • Data science project management.
    • Assessment.
    • Applications in various domains.
    • Pedagogical challenges, opportunities, and risks.
  • Learners' cognitive and social processes.
    • Cognitive and behavioral perspective on data science education.
    • Cognitive and social biases in data science.
    • Metacognition.
    • Learner difficulties.
    • Learning styles and models.
    • Diversity, representation, and equality; recruitment and retention of underrepresented groups in data science.
  • Teacher preparation for data science.
    • Teacher training programs.
    • Communities of data science educators.
  • The essence of data science education.
    • Is data science education different from other educational fields?
    • Policy: Data science for all; Should everyone take an introductory data science course?
    • The interdisciplinary challenges of data science education.
    • Characteristics of data scientists.
    • Ethical issues of data science.
    • Connection between data literacy, data science education, and related topics.
    • University/public/for-profit and non-profit collaborations in data science education.
  • Specific topics in data science education - representative list.
    • Machine learning.
    • Artificial intelligence.
    • Data mining.
    • Bayesian statistics.
    • Historical perspective.
    • The data science cycle.

In her 2020 article1, Jeannette M. Wing asks, "What will data science be in 10 or 50 years? The answer to this question is in the hands of the next-generation researchers and educators."a

In this post, we addressed the educational aspect of Wing's answer by proposing our vision for a journal dedicated to data science education that would highlight the interdisciplinary nature of data science and address the growing community of data science educators from a wide range of disciplines, organizations, and fields of research. IJDSE would be a place where such scholars would be able to share their practice and research-based expertise in data science education. Thus, beyond contributing to the creation of a new interdisciplinary field of research, IJDSE would support the creation of an important, influential worldwide community of data science education researchers.

As an interdisciplinary journal, IJDSE would enable scholars from different fields of research to learn from each other, improve their professional work, and educate 21st-century professional data scientists and non-scientist citizens to use data in a responsible, ethical, meaningful way. Furthermore, since potential contributors to IJDSE are the pioneers of data science education, they would have the potential to influence the creation of new curricula and study programs.

We note IJDSE would aim to address challenges of data science education we identified in our post "Ten Challenges of Data Science Education" (https://bit.ly/2R2c9cu), including the interdisciplinary nature of data science and its diverse body of learners and teachers.

We suggest it is urgent to establish an educational journal for data science that approaches data science education from the interdisciplinary perspective. To that end, we call on all potential stakeholders to take up the gauntlet and establish such a platform for all data science educators. We would be happy to assist in any such initiative in its establishment process and to offer our comprehensive perspective on data science education.

* Further Reading

  1. Anderson, P. et al. An undergraduate degree in data science: Curriculum and a decade of implementation experience. In SIGCSE 2014 - Proceedings of the 45th ACM Technical Symposium on Computer Science Education (2014), pp. 145–150; https://bit.ly/2SsJLAP
  2. Gould, R. Mobilize: a Data Science Curriculum for 16-Year-Old Students. (2018); https://bit.ly/3gqW2xx
  3. Heinemann, B. et al. Drafting a data science curriculum for secondary schools. In Proceedings of the ACM Int. Conf. Proceeding Ser. (Nov. 2018), 1-5; https://bit.ly/3wgv6Yb
  4. Havill, J. Embracing the liberal arts in an interdisciplinary data analytics program. SIGCSE 2019—Proceedings of the 50th ACM Tech. Symp. Comput. Sci. Educ., (2019), 9-14; https://bit.ly/3xelSf5
  5. Khuri, S., Vanhoven, M., and Khuri, N. Increasing the capacity of STEM workforce: Minor in bioinformatics. In Proceedings of the Conference on Integrating Technology into Computer Science Education, ITiCSE (Mar. 2017), 315–320; https://bit.ly/3gfzVv4.
  6. Mike, K., Hazan, T., and Hazzan, O. Equalizing Data Science Curriculum for Computer Science Pupils. Koli Calling—International Conference on Computing Education Research 20 (Nov. 2020), 1-5; https://bit.ly/35emPYJ
  7. Tartaro, A., and Chosed, R.J. Computer scientists at the biology lab bench. In SIGCSE 2015—Proceedings of the 46th ACM Tech. Symp. Comput. Sci. Educ. (2015), 120-125; https://bit.ly/3xd6wre.

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Authors

Orit Hazzan is a professor at the Technion's Department of Education in Science and Technology, whose research focuses on computer science, software engineering, and data science education.

Koby Mike is a Ph.D. student in the Technion's Department of Education in Science and Technology under the supervision of Orit Hazzan.

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Footnotes

1. Wing, J.M. Ten research challenge areas in data science. Harvard Data Science Review 2, 3 (Sept. 2020); https://bit.ly/3znFnna


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