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The Next Generation Science Standards (NGSS) reflect an ambitious vision for science education where students investigate phenomena or solve problems through using and applying disciplinary core ideas in concert with science and engineering practices and crosscutting concepts. Because the NGSS are so different from prior standards, the need for high-quality curriculum materials is especially great. As new curricula go to scale, it will be important to conduct evidence-based research on their efficacy. We conducted a randomized experiment to examine the efficacy of a widely available NGSS-designed middle school curriculum for improving seventh grade students’ learning in physical science. A hierarchical linear modeling approach was applied to analyze student learning outcomes as measured by an NGSS-aligned assessment. Initial findings demonstrate evidence of promise of the curriculum materials for supporting three-dimensional teaching and learning. The findings provide support for further research on NGSS-designed materials at other grade levels and within other science domains.
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K-12 Engineering and the Next Generation Science Standards (NGSS): A Network Visualization and Analysis
We present an interactive network visualization of the Next Generation Science Standards (NGSS) and its coverage by collections of aligned curriculum. The visualization presents an alternative to the usual presentation of the NGSS as a set of linked tables. Users can view entire grade bands, search for or drill down to the level of individual NGSS standards or curricular items, or display groups of standards across grade bands. NGSS-aligned curriculum collections can be switched on and off to visually explore their NGSS coverage. Viewing the NGSS and associated curriculum this way facilitates navigating the NGSS and can help with assessment of alignments as lacking or anomalous. Modeling the NGSS as a network also allows for the computation of network metrics to provide insight into core characteristics of the network. It also provides for detecting anomalies and unexpected patterns.
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- Award ID(s):
- 1941701
- PAR ID:
- 10164529
- Date Published:
- Journal Name:
- 2020 ASEE Annual Conference and Expo
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
