extended-abstract

Thesis plan: the effect of data science teaching for non-STEM students

Author:

Luiz BarbozaAuthors Info & Claims

JCDL '22: Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries

Article No.: 48, Pages 1 - 2

https://doi.org/10.1145/3529372.3530976

Published: 20 June 2022 Publication History

Abstract

In recent years, the interest in Data Science has increased in both industry and academia. Historically, access to this discipline has been redirected to STEM professionals. However, the ubiquity of cloud computing and the simplicity of modern programming languages such as Python and R have enabled non-STEM students and professionals to leverage it especially to analyze data. Similarly, with what has been conveyed with computational thinking in terms of enabling non-STEM students with com technological competencies, this article aims to present a proposal for improving the teaching of data science specifically to non-stem students.

References

[1]

Barboza, L., and Teixeira, E. S. Effect of data science teaching for non-stem students a systematic literature review. ICSEA 2020 (2020), 128.

[2]

Barr, D., Harrison, J., and Conery, L. Computational thinking: A digital age skill for everyone. Learning & Leading with Technology 38, 6 (2011), 20--23.

[3]

Begley, C. G., and Ellis, L. M. Raise standards for preclinical cancer research. Nature 483, 7391 (2012), 531--533.

[4]

Brunner, R. J., and Kim, E. J. Teaching data science. Procedia Computer Science 80 (2016), 1947--1956.

Digital Library

[5]

Cárdenas-Navia, I., and Fitzgerald, B. K. The broad application of data science and analytics: Essential tools for the liberal arts graduate. Change: The Magazine of Higher Learning 47, 4 (2015), 25--32.

[6]

Carr, V., Jones, M., and Wei, B. Interdisciplinary computing: Applied computing for behavioral and social sciences. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (2020), pp. 400--406.

Digital Library

[7]

Çetinkaya-Rundel, M., and Ellison, V. A fresh look at introductory data science. Journal of Statistics and Data Science Education 29, sup1 (2021), S16--S26.

[8]

Danyluk, A., Leidig, P., Cassel, L., and Servin, C. Acm task force on data science education: Draft report and opportunity for feedback. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (2019), pp. 496--497.

Digital Library

[9]

Dichev, C., Dicheva, D., Cassel, L., Goelman, D., and Posner, M. Preparing all students for the data-driven world. In Proceedings of the Symposium on Computing at Minority Institutions, ADMI (2016), vol. 346.

[10]

Donoho, D. 50 years of data science. Journal of Computational and Graphical Statistics 26, 4 (2017), 745--766.

[11]

Freudenthal, E., Roy, M. K., Ogrey, A., Siegel, A., and Gates, A. Q. A computational introduction to programming, mathematical modeling, and elementary mechanics. In 2009 39th IEEE Frontiers in Education Conference (2009), IEEE, pp. 1--6.

[12]

Grinis, I. The stem requirements of "non-stem" jobs: Evidence from uk online vacancy postings. Economics of Education Review 70 (2019), 144--158.

[13]

Jifa, G., and Lingling, Z. Data, dikw, big data and data science. Procedia computer science 31 (2014), 814--821.

[14]

Long, D., and Magerko, B. What is ai literacy? competencies and design considerations. In Proceedings of the 2020 CHI conference on human factors in computing systems (2020), pp. 1--16.

Digital Library

[15]

Musabirov, I., Okopny, A. B. P., Pozdniakov, S., and Suvorova, A. Teaching basics of data science to non-stem undergraduates. a hybrid learning approach.

[16]

Nasution, M. K., Sitompul, O. S., and Nababan, E. B. Data science. In Journal of Physics: Conference Series (2020), vol. 1566, IOP Publishing, p. 012034.

[17]

O'Neil, M. As data proliferate, so do data-related graduate programs. The Chronicle of Higher Education 60 (2014), 1.

[18]

Shin, J. E. L., Levy, S. R., and London, B. Effects of role model exposure on stem and non-stem student engagement. Journal of Applied Social Psychology 46, 7 (2016), 410--427.

[19]

Smith, F. J. Data science as an academic discipline. Data Science Journal 5 (2006), 163--164.

[20]

Song, I.-Y., and Zhu, Y. Big data and data science: what should we teach? Expert Systems 33, 4 (2016), 364--373.

Digital Library

[21]

Wiedenbeck, S. Factors affecting the success of non-majors in learning to program. In Proceedings of the first international workshop on Computing education research (2005), pp. 13--24.

Digital Library

[22]

Xu, J. J., and Babaian, T. Artificial intelligence in business curriculum: The pedagogy and learning outcomes. The International Journal of Management Education 19, 3 (2021), 100550.

Cited By

Gassmann LCampbell J(2024)Designing an Automated Machine Learning Approach for Transformer Architecture in Education and Non-STEM Research SettingsIntelligent Computing10.1007/978-3-031-62273-1_13(182-200)Online publication date: 15-Jun-2024
https://doi.org/10.1007/978-3-031-62273-1_13

Recommendations

K12 outreach: motivating k12 school counselors to embrace the changing face of computing (abstract only)
SIGCSE '13: Proceeding of the 44th ACM technical symposium on Computer science education

K12 students have limited access to computer science curricula in United States' public schools. Individuals' perceptions of the field as a masculine, elite, and individualistic profession can restrict which K12 students are encouraged to pursue careers ...
Computational thinking in high school courses
SIGCSE '10: Proceedings of the 41st ACM technical symposium on Computer science education

The number of undergraduates entering computer science has declined in recent years. This is paralleled by a drop in the number of high school students taking the CS AP exam and the number of high schools offering computer science courses. The declines ...
From Professional Development to the Classroom: Findings from CS K-12 Teachers
SIGCSE '17: Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education

The CS for All initiative places increased emphasis on the need to prepare K-12 teachers of computer science (CS). Professional development (PD) programs continue to be an essential mechanism for preparing in-service teachers who have little formal ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

JCDL '22: Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries

June 2022

392 pages

ISBN:9781450393454

DOI:10.1145/3529372

General Chairs:
Akiko Aizawa
National Institute of Informatics, Japan
,
Thomas Mandl
University of Hildesheim, Germany
,
Zeljko Carevic
GESIS - Leibniz Institute for the Social Sciences, Germany
,
Program Chairs:
Annika Hinze
University of Waikato, New Zealand
,
Philipp Mayr
GESIS - Leibniz Institute for the Social Sciences, Germany
,
Philipp Schaer
TH Köln (University of Applied Sciences), Germany

Copyright © 2022 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

In-Cooperation

IEEE Technical Committee on Digital Libraries (TC DL)

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 June 2022

Check for updates

Author Tags

Qualifiers

Extended-abstract

Conference

JCDL '22

Sponsor:

JCDL '22: The ACM/IEEE Joint Conference on Digital Libraries in 2022

June 20 - 24, 2022

Cologne, Germany

Acceptance Rates

JCDL '22 Paper Acceptance Rate 35 of 132 submissions, 27%;

Overall Acceptance Rate 415 of 1,482 submissions, 28%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
103
Total Downloads

Downloads (Last 12 months)18
Downloads (Last 6 weeks)3

Reflects downloads up to 27 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Gassmann LCampbell J(2024)Designing an Automated Machine Learning Approach for Transformer Architecture in Education and Non-STEM Research SettingsIntelligent Computing10.1007/978-3-031-62273-1_13(182-200)Online publication date: 15-Jun-2024
https://doi.org/10.1007/978-3-031-62273-1_13

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten