473
Views
2
CrossRef citations to date
0
Altmetric
Articles

Enacting ambitious engineering curriculum in science classrooms: examining teachers’ implementation of Virtual Engineering Internships

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 2055-2074 | Received 01 Nov 2019, Accepted 14 Aug 2020, Published online: 11 Sep 2020
 

ABSTRACT

The United States’ Next Generation Science Standards (NGSS) elevate engineering design to the same stature as scientific inquiry, calling on science teachers to engage students in engineering practices to solve real-world problems. In response, researchers and curriculum developers designed and studied Virtual Engineering Internships (VEIs) to engage students in engineering and science practices outlined by the NGSS, and alleviate some of the logistical and conceptual burden for teachers unfamiliar with engineering instruction. Nevertheless, the VEIs still require striking shifts in the way teaching and learning happens in middle school science classrooms. In this exploratory study we sought to understand whether and how teachers participating in a curriculum pilot project (n = 26) interacted with the VEIs in ways that helped them carry out pedagogical shifts called for by the NGSS, and to identify curricular features that may have supported these shifts. Findings suggest that teachers leveraged and modified the VEIs to support student engagement in engineering practices and real-world problem-solving, which suggests the potential of the curriculum to catalyse deep pedagogical shifts called for by the NGSS. From this analysis, we propose a set of curriculum design features that can support teachers in implementing ambitious engineering curricula in diverse classroom contexts.

Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant No. 1417939. 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 National Science Foundation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

De-identified data will be made available upon request, and in accordance with the IRB protocol.

Notes

1 The NRC Framework and NGSS outline three components of engineering design as follows: (a) defining and delimiting engineering problems in terms of criteria and constraints, (b) designing possible solutions, and (c) optimising the design solution (NGSS Lead States, Citation2013; NRC, Citation2012a).

2 There is not a one-to-one correspondence between the instructional phases of VEIs and each of the NRC Framework and NGSS’ three components of engineering design. Instead, students engage in different components throughout the VEIs.

3 While not yet implemented in published units, a learning analytics model that analyses students’ digital log files is being developed to provide teachers with more insight into students’ design processes (Montgomery et al., Citation2020). This type of information would allow teachers to more easily evaluate how systematically students iterate.

Additional information

Funding

This material is based upon work supported by the National Science Foundation under grant number 1417939; Division of Research on Learning in Formal and Informal Settings. 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 National Science Foundation.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 388.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.