Impact of Robotics and Geospatial Technology Interventions on Youth STEM Learning and Attitudes
Open accessAbstract
This study examined the impact of robotics and geospatial technologies interventions on middle school youth’s learning of and attitudes toward science, technology, engineering, and mathematics (STEM). Two interventions were tested. The first was a 40-hour intensive robotics/GPS/GIS summer camp; the second was a 3-hour event modeled on the camp experiences and intended to provide an introduction to these technologies. Results showed that the longer intervention led to significantly greater learning than a control group not receiving the instruction, whereas the short-term intervention primarily impacted youth attitude and motivation. Although the short-term intervention did not have the learning advantages of a more intensive robotics camp, it can serve a key role in getting youth excited about technology and encouraging them to seek out additional opportunities to explore topics in greater detail, which can result in improved learning.
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Educational robots may help students gain experiences that will facilitate a deep and abstract understanding required for constructing knowledge and enhancing critical thinking.
“In this way, educational robots may help students gain experiences that will facilitate a deep and abstract understanding required for constructing knowledge and enhancing critical thinking (Nugent et al., 2010).”
From A Systematic Review of Studies on Educational Robotics (2019) · cites this paper as Nugent et al., 2010
Cites
Reliability and Predictive Validity of the Motivated Strategies for Learning Questionnaire (Mslq)
Pintrich, P. R., Smith, D. A. F., Garcia, T., & Mckeachie, W. J. (1993) · Educational and Psychological Measurement · doi:10.1177/0013164493053003024 · Cited by 3,646 · see citing works
Problem-based learning in medicine and beyond: A brief overview
Barrows, H. S. (1996) · New Directions for Teaching and Learning · doi:10.1002/tl.37219966804 · Cited by 2,007 · see citing works
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A Systematic Review of Studies on Educational Robotics
Anwar, S., Bascou, N. A., Menekse, M., & Kardgar, A. (2019) · Journal of Pre-College Engineering Education Research (J-PEER) · doi:10.7771/2157-9288.1223 · Cited by 419 · see citing works
“…camps (Balaguer Alvarez, 2017; Barger, Gilbert, & Boyette, 2011; Doerschuk, Liu, & Mann, 2007; Ericson & Mcklin, 2012; van Delden & Yang, 2014), project-based learning environments (Carbonaro et al., 2004), and various STEM fields (Hussain et al., 2006; Williams et al., 2007; Nugent et al., 2010).”
Kim, C., Kim, D., Yuan, J., Hill, R. B., Doshi, P., & Thai, C. N. (2015) · Computers & Education · doi:10.1016/j.compedu.2015.08.005 · Cited by 344 · see citing works
The cognitive benefits of learning computer programming: A meta-analysis of transfer effects.
Scherer, R., Siddiq, F., & Sánchez Viveros, B. (2019) · Journal of Educational Psychology · doi:10.1037/edu0000314 · Cited by 205 · see citing works
A Proposed Taxonomy of Teaching Models in STEM Education: Robotics as an Example
Zhong, B., Liu, X., Xia, L., & Sun, W. (2022) · Sage Open · doi:10.1177/21582440221099525 · Cited by 44 · see citing works
Wu, S.-Y., & Yang, K.-K. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.868623 · Cited by 37 · see citing works
Luo, T., So, W. W. M., Li, W. C., & Yao, J. (2021) · Journal of Science Education and Technology · doi:10.1007/s10956-020-09882-0 · Cited by 22 · see citing works
- Authors
- Nugent, G., Barker, B., Grandgenett, N., & Adamchuk, V. I.
- Affiliations
- University of Nebraska–Lincoln (US)
- University of Nebraska at Omaha (US)
- Venue
- Journal of Research on Technology in Education
- Publisher
- Informa UK Limited
- Published
- 2010-06
- Volume
- 42
- Issue
- 4
- Pages
- 391-408
- ISSN
- 1539-1523 (ISSN-L)
- 1945-0818
- OpenAlex
- W1966127013
- Cited by
- 279
- References
- 2
- Citations by year
- 2026: 14 · 2025: 9 · 2024: 12 · 2023: 27 · 2022: 24 · 2021: 22 · 2020: 32 · 2019: 25 · 2018: 21 · 2017: 22 · 2016: 27 · 2015: 13 · 2014: 10 · 2013: 11 · 2012: 8
- FWCI
- 7.11
- Type
- journal article
- Language
- en
- Open access
- Locations
- Journal of Research on Technology in Education (publishedVersion)
- DigitalCommons@UNO (submittedVersion)
- Funders
- National Science Foundation (0624591)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Robotics and Engineering
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Social Sciences › Gender Studies › Gender and Technology in Education
- Keywords
- robotics education, geotechnologies, GPS, geographic information systems, STEM learning, STEM attitudes, middle school youth, intervention duration, student motivation, informal science education
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