Access to Educational Robotics is Linked to Socioeconomic Status: A Correlational Analysis
DOI:
https://doi.org/10.47611/jsrhs.v13i3.7426Keywords:
STEM Education, Educational Robotics, Socioeconomic Status, Educational Technology, RoboticsAbstract
The study investigates the relationship between a region's average socioeconomic status (SES) and the accessibility of educational robotics (ER) in that region. The study sampled 120 regions of varying SES across NJ to examine the presence of robotics education for high school students. Then, a correlational analysis was conducted, revealing a relationship between the SES of a region and the accessibility of ER. As SES increases, the accessibility of ER also increases among high schools. The study’s findings play a role in promoting a more equal STEM landscape, across both education and the workforce. ER is a tool that can be used to provide an integrated STEM education, and, therefore promote both STEM skills and interest. With lower SES regions not having access to ER, students from those backgrounds do not have the same opportunities to enhance their STEM skills and interests. These findings can be used by both policymakers and educational organizations in an effort to make the landscape of STEM education and ER more equal. As that happens, an increase in diversity of the STEM field can be expected.
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Angel-Fernandez, J. M., & Vincze, M. (2018). Towards a Formal Definition of Educational Robotics. Proceedings of the Austrian Robotics Workshop 2018. https://doi.org/10.15203/3187-22-1-08
Bhalla, A. S. (1992). Uneven Development in the third world (pp. 208–241). Springer.
Broer, M., Bai, Y., & Fonseca, F. (2019). Socioeconomic inequality and educational outcomes. IEA Research for Education. https://doi.org/10.1007/978-3-030-11991-1
Brown, P. (2013). Education, opportunity and the prospects for social mobility. British Journal of Sociology of Education, 34(5/6), 678–700. https://doi.org/10.1080/01425692.2013.816036
Chachashvili-Bolotin, S., Milner-Bolotin, M., & Lissitsa, S. (2016). Examination of factors predicting secondary students’ interest in tertiary STEM education. International Journal of Science Education, 38(3), 366–390. https://doi.org/10.1080/09500693.2016.1143137
Daniela, L., & Lytras, M. D. (2018). Educational robotics for inclusive education. Technology, Knowledge and Learning, 24(2), 219–225. https://doi.org/10.1007/s10758-018-9397-5
Dotson, V. M., Kitner-Triolo, M. H., Evans, M. K., & Zonderman, A. B. (2009). Effects of race and socioeconomic status on the relative influence of education and literacy on cognitive functioning. Journal of the International Neuropsychological Society: JINS, 15(4), 580–589. https://doi.org/10.1017/S1355617709090821
Education Law Center. (2017). ELC’s updated district factor groups. Education Law Center. https://edlawcenter.org/research/elcs-updated-district-factor-groups/
Evripidou, S., Georgiou, K., Doitsidis, L., Amanatiadis, A. A., Zinonos, Z., & Chatzichristofis, S. A. (2020). Educational robotics: platforms, competitions and expected learning outcomes. IEEE Access, 8, 219534–219562. https://doi.org/10.1109/access.2020.3042555
FIRST. (2023). Annual report & Financials. FIRST. https://www.firstinspires.org/about/annual-report
FIRST. (2024, April 28). Team and Event Search. FIRST. https://www.firstinspires.org/team-event-search#type=teams&sort=name&programs=FLLJR
Francisco José García‐Peñalvo, Conde, M. Á., José Gonçalves, & Lima, J. (2020). Advances in computational thinking and robotics in education. TEEM’20: Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality. https://doi.org/10.1145/3434780.3436703
Hall, C., Dickerson, J., Batts, D., Kauffmann, P., & Bosse, M. (2011). Are we missing opportunities to encourage interest in STEM fields? Journal of Technology Education, 23(1). https://doi.org/10.21061/jte.v23i1.a.4
Hendricks, C. C., Meltem Alemdar, & Ogletree, T. (2020). The impact of participation in VEX Robotics Competition on middle and high school students’ interest in pursuing STEM studies and STEM-related careers. 2012 ASEE Annual Conference & Exposition. https://doi.org/10.18260/1-2--22069
Kennedy, C. M. (2015). Lessons from outside the classroom: What can New Zealand learn from the long Chilean winter? Asia Pacific View Point, 56(1), 169–181. https://doi.org/10.1111/apv.12056
Khanlari, A. (2013). Effects of educational robots on learning STEM and on students’ attitude toward STEM. 2013 IEEE 5th Conference on Engineering Education (ICEED). https://doi.org/10.1109/iceed.2013.6908304
Kim, C., Kim, D., Yuan, J., Hill, R. B., Doshi, P., & Thai, C. N. (2015). Robotics to promote elementary education pre-service teachers’ STEM engagement, learning, and teaching. Computers & Education, 91, 14–31. https://doi.org/10.1016/j.compedu.2015.08.005
Kliucharev, G. A., & Kofanova, E. N. (2005). On the dynamics of the educational behavior of well-off and low-income Russians. Russian Education and Society, 47(11), 22–36. https://doi.org/10.1080/10609393.2005.11056929
Martín Páez, et al. (2019). What are we talking about when we talk about STEM education? A review of literature. Science Education. 103. 10.1002/sce.21522. https://doi.org/10.1002/sce.21522
Martín‐Páez, T., Aguilera, D., Perales‐Palacios, F. J., & Vílchez‐González, J. M. (2019). What are we talking about when we talk about STEM education? A review of literature. Science Education, 103(4), 799–822. https://doi.org/10.1002/sce.21522
National Center for Education Statistics. (2013). Improving the measurement of socioeconomic status for the National Assessment of Educational Progress. https://nces.ed.gov/nationsreportcard/pdf/researchcenter/Socioeconomic_Factors.pdf
Negrini, L., & Giang, C. (2019). How do pupils perceive educational robotics as a tool to improve their 21st century skills?. Journal of E-Learning and Knowledge Society, 15(2). https://doi.org/10.20368/1971-8829/1628
New Jersey Department of Education. (1990). District Factor Groups (DFG) for school districts. www.nj.gov. https://www.nj.gov/education/finance/rda/dfg.shtml
Oakes, M. (2006). Measuring socioeconomic status . https://obssr.od.nih.gov/sites/obssr/files/Measuring-Socioeconomic-Status.pdf
Peter Ngugi Mwangi, Christopher Maina Muriithi, & Peace Byrne Agufana. (2022). Exploring the benefits of educational robots in STEM learning: A systematic review. International Journal of Engineering and Advanced Technology, 11(6), 5–11. https://doi.org/10.35940/ijeat.f3646.0811622
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763–1768. https://doi.org/10.1213/ane.0000000000002864
Simon, M. K., & Goes, J. (2011). Correlational research [Unpublished Manuscript]
State of New Jersey. (January 14th, 2022). School district boundaries [Interactive Map]. Retrieved from https://njogis-newjersey.opendata.arcgis.com/datasets/newjersey::school-districts-unified-for-new-jersey/about
Tsui, L. (2007). Effective strategies to increase diversity in STEM fields: A review of the research literature. The Journal of Negro Education, 76(4), 555–581. http://www.jstor.org/stable/40037228
United Nations Education, Scientific, and Cultural Organization, & Global Education Monitoring Report. (2020). Defining the scope of inclusive education (pp. 2–67).
U.S. Census Bureau. (2022). 2018-2022 American Community Survey 5-year Public Use [Data Set]. data.census.gov
US National Science Foundation. (2023, January 30). Diversity and STEM: Women, minorities, and persons with disabilities 2023. NSF - National Science Foundation. Ncses.nsf.gov. https://ncses.nsf.gov/pubs/nsf23315/report
Walpole, M. (2003). Socioeconomic status and college: How SES affects college experiences and outcomes. The Review of Higher Education, 27(1), 45–73. https://doi.org/10.1353/rhe.2003.0044
Wallace, M., & Poulopoulos, V. (2022). Pursuing social justice in educational robotics. Education Sciences, 12(8), 565. https://doi.org/10.3390/educsci12080565
Widya, Rifandi, R., & Laila Rahmi, Y. (2019). STEM education to fulfil the 21st century demand: a literature review. Journal of Physics: Conference Series, 1317(1317), 012208. https://doi.org/10.1088/1742-6596/1317/1/012208
Ziaeefard, S., Miller, M. H., Rastgaar, M., & Mahmoudian, N. (2017). Co-robotics hands-on activities: A gateway to engineering design and STEM learning. Robotics and Autonomous Systems, 97, 40–50. https://doi.org/10.1016/j.robot.2017.07.013
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