Search for: All records

In this work, convolutional neural networks (CNN) are developed to detect and characterize sporadic E (E_s), demonstrating an improvement over current methods. This includes a binary classification model to determine ifE_sis present, followed by a regression model to estimate theE_sordinary mode critical frequency (foEs), a proxy for the intensity, along with the height at which theE_slayer occurs (hEs). Signal‐to‐noise ratio (SNR) and excess phase profiles from six Global Navigation Satellite System (GNSS) radio occultation (RO) missions during the years 2008–2022 are used as the inputs of the model. Intensity (foEs) and the height (hEs) values are obtained from the global network of ground‐based Digisonde ionosondes and are used as the “ground truth,” or target variables, during training. After corresponding the two data sets, a total of 36,521 samples are available for training and testing the models. The foEs CNN binary classification model achieved an accuracy of 74% and F1‐score of 0.70. Mean absolute errors (MAE) of 0.63 MHz and 5.81 km along with root‐mean squared errors (RMSE) of 0.95 MHz and 7.89 km were attained for estimating foEs and hEs, respectively, when it was known thatE_swas present. When combining the classification and regression models together for use in practical applications where it is unknown ifE_sis present, an foEs MAE and RMSE of 0.97 and 1.65 MHz, respectively, were realized. We implemented three other techniques for sporadic E characterization, and found that the CNN model appears to perform better.

 

Integrated approaches to teaching science, technology, engineering, and mathematics (commonly referred to as STEM education) in K-12 classrooms have resulted in a growing number of teachers incorporating engineering in their science classrooms. Such changes are a result of shifts in science standards to include engineering as evidenced by the Next Generation Science Standards. To date, 20 states and the District of Columbia have adopted the NGSS and another 24 have adopted standards based on the Framework for K-12 Science Education. Despite the increased presence of engineering and integrated STEM education in K-12 education, there are several concerns to consider. One concern is the limited availability of observation instruments appropriate for instruction where multiple STEM disciplines are present and integrated with one another. Addressing this concern requires the development of a new observation instrument, designed with integrated STEM instruction in mind. An instrument such as this has implications for both research and practice. For example, research using this instrument could help educators compare integrated STEM instruction across grade bands. Additionally, this tool could be useful in the preparation of pre-service teachers and professional development of in-service teachers new to integrated STEM education and formative learning through professional learning communities or classroom coaching. The work presented here describes in detail the development of an integrated STEM observation instrument - the STEM Observation Protocol (STEM-OP) - that can be used for both research and practice. Over a period of approximately 18-months, a team of STEM educators and educational researchers developed a 10-item integrated STEM observation instrument for use in K-12 science and engineering classrooms. The process of developing the STEM-OP began with establishing a conceptual framework, drawing on the integrated STEM research literature, national standards documents, and frameworks for both K-12 engineering education and integrated STEM education. As part of the instrument development process, the project team had access to over 2000 classroom videos where integrated STEM education took place. Initial analysis of a selection of these videos helped the project team write a preliminary draft instrument consisting of 79 items. Through several rounds of revisions, including the construction of detailed scoring levels of the items and collapsing of items that significantly overlapped, and piloting of the instrument for usability, items were added, edited, and/or removed for various reasons. These reasons included issues concerning the intricacy of the observed phenomenon or the item not being specific to integrated STEM education (e.g., questioning). In its final form, the STEM-OP consists of 10 items, each comprising four descriptive levels. Each item is also accompanied by a set of user guidelines, which have been refined by the project team as a result of piloting the instrument and reviewed by external experts in the field. The instrument has shown to be reliable with the project team and further validation is underway. The STEM-OP will be of use to a wide variety of educators and educational researchers looking to understand the implementation of integrated STEM education in K-12 science and engineering classrooms. 

The lack of a definition of the T in STEM (science, technology, engineering, and mathematics) acronym is pervasive, and it is often the teachers of STEM disciplines who inherit the task of defining the role of technology within their K-12 classrooms. These definitions often vary significantly, and they have profound implications for curricular and instructional goals within science and STEM classrooms. This theoretical paper summarizes of technology initiatives across science and STEM education from the past 30 years to present perspectives on the role of technology in science-focused STEM education. The most prominent perspectives describe technology as the following: (a) vocational education, industrial arts, or the product of engineering, (b) educational or instructional technology, (c) computing or computational thinking, and (d) the tools and practices used by practitioners of science, mathematics, and engineering. We have identified the fourth perspective as the most salient with respect to K-12 science and STEM education. This particular perspective is in many ways compatible with the other three perspectives, but this depends heavily on the beliefs, prior experiences, and instructional goals of teachers who use technology in their science or STEM classroom. 

Abstract In this work, we present polarization profiles for 23 millisecond pulsars observed at 820 and 1500 MHz with the Green Bank Telescope as part of the NANOGrav pulsar timing array. We calibrate the data using Mueller matrix solutions calculated from observations of PSRs B1929+10 and J1022+1001. We discuss the polarization profiles, which can be used to constrain pulsar emission geometry, and present both the first published radio polarization profiles for nine pulsars and the discovery of very low-intensity average profile components (“microcomponents”) in four pulsars. We obtain the Faraday rotation measures for each pulsar and use them to calculate the Galactic magnetic field parallel to the line of sight for different lines of sight through the interstellar medium. We fit for linear and sinusoidal trends in time in the dispersion measure and Galactic magnetic field and detect magnetic field variations with a period of 1 yr in some pulsars, but overall find that the variations in these parameters are more consistent with a stochastic origin.