Skip to main content

Advertisement

SpringerLink
Log in

Aerial Drone: an Effective Tool to Teach Information Technology and Cybersecurity through Project Based Learning to Minority High School Students in the U.S.

  • Original Paper
  • Published:
TechTrends Aims and scope Submit manuscript

Abstract

This paper describes the design, implementation, and results of an NSF funded Summer Academy from 2016 to 2018, which engaged, on an annual basis, 30 to 60 rising 10th and 11th grade high school science students in an innovative, technology-enriched Project Based Learning (PBL) environment. This Academy emphasized how tech gadgets work and the impact that technology can have on improving communities by immersing students in the exploration of one such device that is a growing phenomenon, the “aerial drone.” In this Academy, the students learned various operations of the drone through Python programming language, and some cybersecurity issues and solutions. The student teams, under the guidance of diverse mentors, comprehensively fortified their STEM problem-solving skills and critical thinking. Both formative and summative evaluations for this Academy showed that it helped students improve their critical thinking ability and motivated them to pursue careers in STEM-related disciplines, specifically in information technology and cybersecurity areas.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Abdelmoula, M., Chakroun, W., & Akrout, F. (2015). The effect of sample size and the number of items on reliability coefficients: Alpha and rho: A meta-analysis. International Journal of Numberical Methods and Applications, 13(1), 1–20. https://doi.org/10.17654/IJNMAMar2015_001_020.

    Article  Google Scholar 

  • Bujang, M. A., Omar, E. D., & Baharum, N. A. (2018). A review on sample size determination for Cronbach’s alpha test: A simple guide for researchers. Malaysian Journal of Medical Sciences, 25(6), 85–99. https://doi.org/10.21315/mjms2018.25.6.9.

    Article  Google Scholar 

  • Burrell, D. N. (2018). An exploration of the cybersecurity workforce shortage. International Journal of Hyperconnectivity and the Internet of Things, 2(1), 29–41.

    Google Scholar 

  • Cervantes, H., Kazman, R., Ryoo, J., Choi, D., & Jang, D. (2016). Architectural approaches to security: Four case studies. Computer, 49(11), 60–67.

    Google Scholar 

  • Chen, P., Hernandez, A., & Dong, J. (2015). Impact of collaborative project-based earning self-efficacy of urban minority students in engineering. Journal of Urban Learning Teaching and Research, 11, 26–39.

    Google Scholar 

  • Cheng, L., Liu, F., & Yao, D. (2017). WIREs data mining and knowledge discovery, 7(e1211), 1–14.

  • Cohen, N., Hulvey, R. Mongkolnchaiarunya, J. Novak, A., Morgus, R. & Segal, A. (2017). Cybersecurity as an engine for growth: Policy paper. New America.

  • Funk, C., & Parker, K. (2018). Women and men in stem often at odds over workplace equity. Retrieved 2020-09-02, from https://www.pewsocialtrends.org/2018/01/09/women-and-men-in-stem-often-at-odds-over-workplace-equity/

  • Gainor, K. A., & Lent, R. W. (1998). Social cognitive expectations and racial identity attitudes in predicting the math choice intentions of black college students. Journal of Counseling Psychology, 45(4), 403–413.

    Google Scholar 

  • Garcia-Reid, P., Reid, R., & Peterson, N. A. (2005). School engagement among Latino youth in an urban middle school context: Valuing the role of social support. Education and Urban Society, 37(3), 257–275.

    Google Scholar 

  • Graff, J. (2012). The ps-drone-api: Programming a parrot ar.Drone 2.0 with python - the easy way. Retrieved from www.playsheep.de/drone

  • Gudivada, V., Ramaswamy, S., & Srinivasan, S. (2018). In L. Deka & M. Chowdhury (Eds). Transportation Cyber-Physical Systems (pp. 173–200). New York: Elsevier.

  • Hinton, P. R., Brownlow, C., McMurray, I., & Cozens, B. (2004). SPSS explained. New York: Routledge.

    Google Scholar 

  • Kendricks, K., Nedunuri, K. V., & Arment, A. (2013). Minority student perceptions of the impact of mentoring to enhance academic performance in STEM disciplines. Journal of STEM Education: Innovations and Research, 14(2), 38–46.

    Google Scholar 

  • Krajcik, J. S., & Blumenfeld, P. (2006). Project-based learning. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences. New York: Cambridge.

    Google Scholar 

  • Lachney, M., Babbitt, W., Bennett, A., & Eglash, R. (2019). Generative computing: African-American cosmetology as a link between computing education and community wealth. Interactive Learning Environments, 1-21.

  • Liu, M., Hsieh, P., Cho, Y., & Schallert, D. (2006). Middle school students’ self-efficacy, attitudes, and achievement in a computer-enhanced problem-based learning environment. Journal of Interactive Learning Research, 17(3), 225–242.

    Google Scholar 

  • Liu, M., Horton, L., Olmanson, J., & Toprac, P. (2011). A study of learning and motivation in a new media enriched environment for middle school science. Educational Technology Research and Development, 59(2), 249–265.

    Google Scholar 

  • Marx, R. W., Blumenfeld, P. C., Krajcik, J. S., Fishman, B., Soloway, E., Geier, R., & Tal, R. T. (2004). Inquiry-based science in the middle grades: Assessment of learning in urban systemic reform. Journal of Research in Science Teaching, 41(10), 1063–1080.

    Google Scholar 

  • Modi, K., Schoenberg, J., & Salmond, K. (2012). Generation stem: What girls say about science, technology, engineering, and math. In A Report from the Girl Scout Research Institute. New York: Girl Scouts of the USA.

    Google Scholar 

  • Mouza, C., Marzocchi, A., Pan, Y., & Pollock, L. (2016). Development, implementation, and outcomes of an equitable computer science after-school program: Findings from middle-school students. Journal of Research on Technology in Education, 48(2), 84–104.

    Google Scholar 

  • Museus, S. D., Palmer, R., Davis, R., & Maramba, D. C. (2011). Special issue: Racial and ethnic minority students’ success in STEM education. ASHE Higher Education Report, 36(6), 1–140.

    Google Scholar 

  • Nandakumar, N., & Lakshmi, L. (2017). Emerging and upcoming threats in cyber security in 21 century. A Monthly Journal of Computer Science and Information Technology, 6(2), 107–118.

    Google Scholar 

  • National Science Board (2018). Science and Engineering Indicators 2018. Alexandria, VA: National Science Foundation (NSB-2018-1).

  • Noonan, R. (2017). Women in STEM: 2017 update. ESA issue brief# 06–17. US Department of Commerce.

  • Nugent, G., Barker, B. S., Grandgenett, N., Adamchuk, V. I. (2010). Impact of robotics and geospatial technology interventions on youth STEM learning and attitudes, Teacher Education Faculty Publications, 33. Retrieved from https://digitalcommons.unomaha.edu/tedfacpub/33

  • Nunnally, J. C., & Bernstein, I. H. (2017). Psychometric theory (3rd ed.). New York: McGraw-Hill.

    Google Scholar 

  • Office of Management and Budget (2016). Annual report to congress: Federal information security management act. Washington D.C. Retrieved from https://www.whitehouse.gov/sites/whitehouse.gov/files/omb/assets/egov_docs/final_fy14_fisma_report_02_27_2015.pdf

  • Palmer, R. T., Davis, R. J., & Maramba, D. C. (2011). The impact of family support for African American males at an historically Black University: Affirming the revision of Tinto's theory. Journal of College Student Development, 52(5), 577–593.

    Google Scholar 

  • Pintrich, P. R., Smith, D. A. F., Garcia, T., & Mckeachie, W. (1993). Reliability and predictive validity of the motivated strategies for learning questionnaire (mslq). Educational and Psychological Measurement, 53(3), 801–813.

    Google Scholar 

  • Rivet, A. E., & Krajcik, J. S. (2004). Achieving standards in urban systemic reform: An 15 example of a sixth grade project-based science curriculum. Journal of Research in Science Teaching, 41(7), 669–692.

    Google Scholar 

  • Rouquette, A., & Falissard, B. (2011). Sample size requirements for the internal validation of psychiatric scales. International Journal of Methods in Psychiatric Research, 20(4), 235–249. https://doi.org/10.1002/mpr.352.

    Article  Google Scholar 

  • Russell, M. L. (2014). Motivation in the science classroom: Through a lens of equity and social justice. In M. M. Atwater, M. Russell, & M. Butler (Eds.), Multicultural science education: Preparing teachers for equity and social justice (pp. 103–116). New York: Springer.

    Google Scholar 

  • Sadler, P. M., Sonnert, G., Hazari, Z., & Tai, R. H. (2012). Stability and volatility of STEM career interest in high school: A gender study. Science Education, 96(3), 411–427.

    Google Scholar 

  • Sinatra, G. M., Mukhopadhyay, A., Allbright, T. N., Marsh, J. A., & Polikoff, M. S. (2017). Speedometry: A vehicle for promoting interest and engagement through integrated STEM instruction. Journal of Educational Research, 110(3), 308–316.

    Google Scholar 

  • Singh, K., Granville, M., & Dika, S. (2002). Mathematics and science achievement: Effects of motivation, interest, and academic engagement. The Journal of Educational Research, 95(6), 323–332.

    Google Scholar 

  • State of Computer Science Education (2019). Equity and diversity. Retrieved from https://advocacy.code.org/2019_state_of_cs.pdf

  • Thomas, J. W. (2000). A review of research on project-based learning. San Rafael: The Autodesk Foundation.

    Google Scholar 

  • Unfried, A., Faber, M., & D.S., S., & Wiebe, E. (2015). The development and validation of a measure of student attitudes toward science, technology, engineering, and math (s-stem). Journal of Psychoeducational Assessment, 33(7), 622–639.

  • Varma, R. (2018). U.S. science and engineering workforce: Underrepresentation of women and minorities. American Behavioral Scientist, 62(5), 692–697.

    Google Scholar 

  • Williams, M., & Linn, M. C. (2002). Wise inquiry in fifth grade biology. Research in Science Education, 32(4), 415–436.

    Google Scholar 

  • Yurdugul, H. (2008). Minimum sample size for Cronbach’s coefficient alpha: A Monte-Carlo study. H. U. Journal of Education, 35, 397–405.

    Google Scholar 

Download references

Acknowledgements

This research was supported in part by National Science Foundation grants #1761735,

Author information

Authors and Affiliations

  1. College of Business and Information Science, Tuskegee University, Tuskegee, AL, USA

    Jay Bhuyan, Fan Wu, Cassandra Thomas & Kai Koong

  2. Department of Educational Foundations, Leadership& Technology, Auburn University, Auburn, AL, USA

    Jung Won Hur & Chih-hsuan Wang

Authors
  1. Jay Bhuyan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cassandra Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kai Koong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jung Won Hur
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chih-hsuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jay Bhuyan.

Ethics declarations

Disclosure of Potential Conflicts of Interest

#1723586, #1663350, #1614845, National Institute of Health grant NIH TU CBR/RCMI #U54MD007585, and a grant from Rockwell Collins, U.S.A. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies. We have no potential conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee (Tuskegee University Human participant Review Committee, Institutional Review Board-IRB # 00001137 + reference number HPRC #110915) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bhuyan, J., Wu, F., Thomas, C. et al. Aerial Drone: an Effective Tool to Teach Information Technology and Cybersecurity through Project Based Learning to Minority High School Students in the U.S.. TechTrends 64, 899–910 (2020). https://doi.org/10.1007/s11528-020-00502-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11528-020-00502-7

Keywords

Search

Navigation