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Teachers’ integration of the Next Generation Science Standards and corresponding Science and Engineering Practices (SEPs) illustrate current science education reform in the United States. Effective teacher professional development (PD) on SEPs is essential for reform success. In this study, we evaluated the Nebraska STEM Education Conference, a PD program for middle school, high school, and first- and second-year post-secondary STEM teachers. This SEP-oriented PD program focused predominantly on the SEPs ‘developing and using models’ and ‘using mathematics and computational thinking.’ An electronic survey was used to measure participants’ (n = 45) prior integration of SEPs, influential factors and barriers to using SEPs, and changes to interest and confidence in using SEPs as a result of attending the PD program. Our results showed that teachers had limited prior use of SEPs in their teaching. Student interest and learning outcomes were the factors found to be most influential to teachers’ use of SEPs, while limited knowledge, confidence, and resources were the most commonly identified barriers. As a result of attending the PD program, participants significantly improved their confidence and interest to incorporate SEPs. We recommend continued SEP-oriented PD to foster successful NGSS integration and to advance reforms in science education. 

In many areas of science, the ability to use computers to process, analyze, and visualize large data sets has become essential. The mismatch between the ability to generate large data sets and the computing skill to analyze them is arguably the most striking within the life sciences. The Digital Image and Vision Applications in Science (DIVAS) project describes a scaffolded series of interventions implemented over the span of a year to build the coding and computing skill of undergraduate students majoring primarily in the natural sciences. The program is designed as a community of practice, providing support within a network of learners. The program focus, images as data, provides a compelling ‘hook’ for participating scholars. Scholars begin the program with a one-credit spring semester seminar where they are exposed to image analysis. The program continues in the summer with a one-week, intensive Python and image processing workshop. From there, scholars tackle image analysis problems using a pair programming approach and can finish the summer with independent research. Finally, scholars participate in a follow-up seminar the subsequent spring and help onramp the next cohort of incoming scholars. We observed promising growth in participant self-efficacy in computing that was maintained throughout the project as well as significant growth in key computational skills. DIVAS program funding was able to support seventeen DIVAS over three years, with 76% of DIVAS scholars identifying as women and 14% of scholars identifying as members of an underrepresented minority group. Most scholars (82%) entered the program as first year students, with 94% of DIVAS scholars retained for the duration of the program and 100% of scholars remaining a STEM major one year after completing the program. The outcomes of the DIVAS project support the efficacy of building computational skill through repeated exposure of scholars to relevant applications over an extended period within a community of practice.