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Teacher professional development (PD) is a key factor in enabling teachers to develop mindsets and skills that positively impact students. It is also a key step in building capacity for computer science (CS) education in K-12 schools. Successful CS PD meets primary learning goals and enable teachers to grow their self-efficacy, asset and equity mindset, and interest in teaching CS. As part of a larger study, we conducted a secondary analysis of CS PD evaluation instruments (). We found that instruments across providers were highly dissimilar with limited data collected for measures related to teacher learning, which has implications for future K-12 CS education. Likewise, the instruments were limited in being connected to student learning and academic growth. As a way to enable PD providers to construct measures that align with known impacting factors, we offer recommendations for collecting demographic data and measuring program satisfaction, content knowledge, pedagogical content knowledge, growth and equity mindset, and self-efficacy. We also highlight questions for PD providers to consider when constructing their evaluation, including reflecting community values, the goals of the PD, and how the data collected will be used to continually improve CS programs.
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This content will become publicly available on February 10, 2027
Spectrometer Assembly and Python-Based Data Science Lab on the Reduction Kinetics of Methylene Blue
Not AvailableIntegrating data science and Python programming into secondary chemistry education addresses gaps in preparing students for STEM (science, technology, engineering, and mathematics) careers, where computational and analytical skills are essential. Traditional laboratories often treat instruments as “black boxes,” restricting students’ ability to grasp basic principles. High instrumentation costs and limited access to advanced data analysis tools restrict hands-on learning. We present a cost-effective laboratory module combining a do-it-yourself (DIY) spectrometer kit with data analysis using Python in Google Colaboratory (Colab). Students learn to plot data, perform least-squares fitting, calculate errors, and conduct kinetic studies, thereby developing analytical chemistry skills. These skills are applied in a problem-based learning environment to bridge introductory chemistry with quantitative analysis. Student feedback indicated a perceived improvement in understanding of instrumental components, calibration, and analytical techniques such as determining limits of linearity and dynamic range of detectors. By demystifying instruments and promoting chemical literacy and computational proficiency, this curriculum offers a model for integrating data science into secondary chemistry education.
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- Award ID(s):
- 2413590
- PAR ID:
- 10667454
- Publisher / Repository:
- American Chemical Society
- Date Published:
- Journal Name:
- Journal of Chemical Education
- Volume:
- 103
- Issue:
- 2
- ISSN:
- 0021-9584
- Page Range / eLocation ID:
- 986 to 995
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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