Abstract
The aim of the paper is to present an idea on how to introduce students of social sciences to computational approach. We discuss why there is a need to develop computational education in the social sciences and why it is a challenge. We consider barriers related to students and to academic teachers. Then we present the idea on how to help overcome those barriers. Within the project Action for Computational Thinking in Social Sciences we plan to set up a MOOC program on social computation at an introductory level. The program is addressed to learners of social sciences (mostly bachelor level) who often experience high levels of anxiety when it comes to mathematics, computers and formal modeling and have no working knowledge of advanced algebra, mathematical analysis, programming etc., but it is open for larger audience. We aim at providing them with a series of short introductory courses that will give them an opportunity to peek over this fence built of fears, stereotypes and lack of practice. We want to show the learners that computational approach to social sciences is, first, worthwhile, as it provides a new angle to look at societal phenomena and, second, accessible, if only approached from the story side rather than from the mathematical formulas side. We hope this will encourage the learners to engage in more demanding courses or, at minimum, approach the computational social sciences with a better general understanding.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The first part of the paper is based on a conference paper: Jager et al.“Looking into the educational mirror: why computation is hardly being taught in the social sciences, and what to do about it” [8].
References
M.H. Ashcraft, Math anxiety: Personal, educational, and cognitive consequences. Curr. Dir. Psychol. Sci. 11(5), 181–185 (2002)
C.B. Buschor, S. Berweger, A. Keck Frei, C. Kappler, Majoring in STEM—what accounts for women’s career decision making? A mixed methods study. J. Educ. Res. 107(3), 167–176 (2014)
C. Cioffi-Revilla, Introduction to Computational Social Science. Principles and Applications. London, UK: Springer-Verlag (2014)
Eurostat database, European Comission. (2017)https://ec.europa.eu/eurostat/data/database. Last accessed 2018/03/21.
J. Fraillon, J. Ainley, W. Schulz, T. Friedman, E. Gebhardt, Preparing for life in a digital age: the IEA international computer and information literacy study international report. (2014)
N. Gilbert, K. Troitzsch, Simulation for the Social Scientist. Maindenhead, UK: Open University Press, 2nd ed. (2005)
W. Jager, Enhancing the realism of simulation (EROS): on implementing and developing psychological theory in social simulation. J. Artif. Soc. Soc. Simul. 20(3), 14 (2017). https://doi.org/10.18564/jasss.3522
W. Jager, K. Abramczuk, A. Baczko-Dombi, A. Komendant-Brodowska, B. Fecher, N. Sokolovska, T. Spits, Looking into the educational mirror: why computation is hardly being taught in the social sciences, and what to do about it, in Advances in Social Simulation: Looking in the Mirror Verhagen, ed. by H. Verhagen (Springer Nature, 2020)
K. Metzler, D.A. Kim, N. Allum, A. Denman, Who is doing computational social science? Trends in big data research (White paper). London, UK: SAGE Publishing, (2016). doi: https://doi.org/10.4135/wp160926
P. Nightingale, A. Scott, Peer review and the relevance gap: ten suggestions for policy-makers. Sci. Pub. Policy 34(8), 543–553 (2007)
A.J. Onwuegbuzie, V.A. Wilson, Statistics Anxiety: Nature, etiology, antecedents, effects, and treatments–a comprehensive review of the literature. Teach. High. Educ. 8(2), 195–209 (2003)
Open Science Collaboration, Estimating the reproducibility of psychological science. Science 349(6251) (2015). doi: https://doi.org/10.1126/science.aac4716
I. Rafols, L. Leydesdorff, A. O’Hare, P. Nightingale, A. Stirling, How journal rankings can suppress interdisciplinary research: a comparison between innovation studies and business & management. Res. Policy 41(7), 1262–1282 (2012)
F. Squazzoni, W. Jager, B. Edmonds, Social simulation in the social sciences: a brief overview. Soc. Sci. Comput. Rev. 32(3), 279–294 (2014)
R.R. Vallacher, S.J. Read, A. Nowak (eds.), Computational Social Psychology (Routledge, New York, 2017)
U. Wilensky, Center for Connected Learning and Computer-Based Modeling (Northwestern University, Netlogo, 1999)
D.J. Watts, Should social science be more solution-oriented? Nat. Hum. Behav. 1, 0015 (2017)
M. Zeidner, Statistics and mathematics anxiety in social science students: some interesting parallels. Br. J. Educ. Psychol. 61(3), 319–328 (1991)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Komendant-Brodowska, A. et al. (2021). Peek Over the Fence—How to Introduce Students to Computational Social Sciences. In: Ahrweiler, P., Neumann, M. (eds) Advances in Social Simulation. ESSA 2019. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-61503-1_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-61503-1_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61502-4
Online ISBN: 978-3-030-61503-1
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)