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1. The Development of a Measure of Engineering Identity

   https://doi.org/10.18260/p.26122  

   Godwin, Allison (January 2016, ASEE Annual Conference & Exposition)

   Measures of subject-related role identities in physics and math have been developed from research on the underlying constructs of identity in science education. The items for these measures capture three constructs of identity: students’ interest in the subject, students’ feeling of recognition by others, and students’ beliefs about their performance/competence in the subject area. In prior studies with late secondary and early post-secondary students, participants did not distinguish between performance beliefs (e.g., believing that they can do well in a particular subject) and competence beliefs (e.g., believing that they can understand a particular subject); therefore, performance/competence beliefs are measured as a single construct. These validated measures have been successful in predicting STEM career choices including physics, math, and engineering. Based on these measures of identity, literature on engineering identity, and my prior work on understanding engineering choice and belongingness through students’ science and math identities at the transition from high school to college, I developed a set of new engineering identity measures that capture and overall identification as an engineer, future engineering career identification, and students’ engineering-related interest, recognition, and performance/competence beliefs. I conducted a pilot survey of 371 first-year engineering students at three institutions within the U.S. during the spring semester of 2015. An exploratory factor analysis (EFA) was performed to examine the underlying structure of the piloted questions about students’ engineering identity. The measures loaded on three separate constructs that were consistent with the hypothesized constructs of interest, performance/competence and recognition. The developed items were used in a subsequent study deployed in the fall semester of 2015 that measured more than 2500 first-year engineering students’ attitudes and beliefs at four institutions within the U.S. The data on engineering identity measures from this second survey were analyzed using confirmatory factor analysis (CFA). The results indicated that the developed measures do extract a significant portion of the average variance in the latent constructs and the internal consistency of the measures (Cronbach’s α) falls within the acceptable and better range. The development of these items provides ways for engineering education researchers to more deeply explore the underlying self-beliefs in students’ engineering identity formation through quantitative measures with strong evidence for validity. 

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2. Decoding of EEG Signals Shows No Evidence of a Neural Signature for Subitizing in Sequential Numerosity

   https://doi.org/10.1162/jocn_a_01734  

   Fornaciai, Michele; Park, Joonkoo (May 2021, Journal of Cognitive Neuroscience)

   Abstract Numerosity perception is largely governed by two mechanisms. The first so-called subitizing system allows one to enumerate a small number of items (up to three or four) without error. The second system allows only an approximate estimation of larger numerosities. Here, we investigate the neural bases of the two systems using sequentially presented numerosity. Sequential numerosity (i.e., the number of events presented over time) starts as a subitizable set but may eventually transition into a larger numerosity in the approximate estimation range, thus offering a unique opportunity to investigate the neural signature of that transition point, or subitizing boundary. If sequential numerosity is encoded by two distinct perceptual mechanisms (i.e., for subitizing and approximate estimation), neural representations of the sequentially presented items crossing the subitizing boundary should be sharply distinguishable. In contrast, if sequential numerosity is encoded by a single perceptual mechanism for all numerosities and subitizing is achieved through an external postperceptual mechanism, no such differences in the neural representations should indicate the subitizing boundary. Using the high temporal resolution of the EEG technique incorporating a multivariate decoding analysis, we found results consistent with the latter hypothesis: No sharp representational distinctions were observed between items across the subitizing boundary, which is in contrast with the behavioral pattern of subitizing. The results support a single perceptual mechanism encoding sequential numerosities, whereas subitizing may be supported by a postperceptual attentional mechanism operating at a later processing stage. 

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3. Spatial concepts of number, size, and time in an indigenous culture

   https://doi.org/10.1126/sciadv.abg4141  

   Pitt, Benjamin; Ferrigno, Stephen; Cantlon, Jessica F.; Casasanto, Daniel; Gibson, Edward; Piantadosi, Steven T. (August 2021, Science Advances)

   In industrialized groups, adults implicitly map numbers, time, and size onto space according to cultural practices like reading and counting (e.g., from left to right). Here, we tested the mental mappings of the Tsimane’, an indigenous population with few such cultural practices. Tsimane’ adults spatially arranged number, size, and time stimuli according to their relative magnitudes but showed no directional bias for any domain on any spatial axis; different mappings went in different directions, even in the same participant. These findings challenge claims that people have an innate left-to-right mapping of numbers and that these mappings arise from a domain-general magnitude system. Rather, the direction-specific mappings found in industrialized cultures may originate from direction-agnostic mappings that reflect the correlational structure of the natural world. 

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4. Density estimation using deep generative neural networks

   https://doi.org/10.1073/pnas.2101344118  

   Liu, Qiao; Xu, Jiaze; Jiang, Rui; Wong, Wing Hung (April 2021, Proceedings of the National Academy of Sciences)

   null (Ed.)

   Density estimation is one of the fundamental problems in both statistics and machine learning. In this study, we propose Roundtrip, a computational framework for general-purpose density estimation based on deep generative neural networks. Roundtrip retains the generative power of deep generative models, such as generative adversarial networks (GANs) while it also provides estimates of density values, thus supporting both data generation and density estimation. Unlike previous neural density estimators that put stringent conditions on the transformation from the latent space to the data space, Roundtrip enables the use of much more general mappings where target density is modeled by learning a manifold induced from a base density (e.g., Gaussian distribution). Roundtrip provides a statistical framework for GAN models where an explicit evaluation of density values is feasible. In numerical experiments, Roundtrip exceeds state-of-the-art performance in a diverse range of density estimation tasks. 

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5. Unveiling the Cosmic Chemistry. II. “Direct” T _e -based Metallicity of Galaxies at 3 < z < 10 with JWST/NIRSpec

   https://doi.org/10.3847/1538-4357/adc7b5  

   Chakraborty, Priyanka; Sarkar, Arnab; Smith, Randall; Ferland, Gary J; McDonald, Michael; Forman, William; Vogelsberger, Mark; Torrey, Paul; Garcia, Alex M; Bautz, Mark; et al (May 2025, The Astrophysical Journal)

   Abstract We report the detection of the [Oiii] auroral line in 42 galaxies within the redshift range of 3 <z< 10. These galaxies were selected from publicly available JWST data releases, including the JADES and PRIMAL surveys, and observed using both the low-resolution PRISM/CLEAR configuration and medium-resolution gratings. The measured electron temperatures in the high-ionization regions of these galaxies range fromT_e([Oiii]) = 12,000 to 24,000 K, consistent with temperatures observed in local metal-poor galaxies and previous JWST studies. In 10 galaxies, we also detect the [Oii] auroral line, allowing us to determine electron temperatures in the low-ionization regions, which range betweenT_e([Oii]) = 10,830 and 20,000 K. The directT_e-based metallicities of our sample span from 12 + log(O/H) = 7.2 to 8.4, indicating these high-redshift galaxies are relatively metal-poor. By combining our sample with 25 galaxies from the literature, we expand the data set to a total of 67 galaxies within 3 <z< 10, effectively more than doubling the previous sample size for directT_e-based metallicity studies. This larger data set allows us to derive empirical metallicity calibration relations based exclusively on high-redshift galaxies, using six key line ratios: R3, R2, R23, Ne3O2, O32, and O3N2. Notably, we derive a novel metallicity calibration relation for the first time using high-redshiftT_e-based metallicities: $\hat{R}$= 0.18log R2 + 0.98log R3. This new calibration significantly reduces the scatter in high-redshift galaxies compared to the $\hat{R}$relation previously calibrated for low-redshift galaxies. 

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