Rethinking the Future of News Literacy Education: Results from a Mixed Methods Study

In an era where most people rely on social media for their news and claims of fake news are rampant, news literacy is seen as increasingly important. In recent years, there has been a surge in initiatives to enhance news literacy among news consumers. However, our understanding of the effectiveness of these initiatives is limited. This study presents the findings from a mixed methods examination of the effectiveness of an online, asynchronous news literacy program offered to adults across the United States. While quantitative findings show that the program made little difference in participants’ already high levels of news literacy, the qualitative findings reveal that participating in the program provided people with a more nuanced, reflective, and less normative understanding of the news. Findings also point to the affective nature of news consumers’ interaction with news content, and a need to rethink news literacy education and assessment from a more learner-centered perspective

Teaching News Literacy During a Pandemic:: Adapting to the Virtual Learning Environment

This lesson plan is based on a collaborative teaching project between the co-authors that was implemented for an online community, over the course of a week in the fall of 2020, in response to the specific teaching and learning challenges presented by the pandemic. The online news literacy program was adapted and expanded from previous iterations of a one-day, in-person workshop, integrating specific pedagogical and engagement strategies for a much broader and more diverse learning community. The authors detail their approach to news literacy from a critical media and information literacy (CMIL) framework and how the program's content and activities were distributed and scaffolded across five days of online engagement

Teaching News Literacy During a Pandemic: Adapting to the Virtual Learning Environment

In the fall of 2020, as the coronavirus pandemic shuttered universities and sent much of higher education online, a team of media and information literacy experts at the University of Maine sought meaningful ways to collaboratively teach news literacy from a distance. The result of their efforts was a weeklong virtual program, Friend, Enemy, or Frenemy? A News Literacy Challenge, open to anyone with an internet connection and an email address. This approach to remote learning scaffolded multiple literacies (critical media, news, and information) into five days, as participants examined different aspects of news production and consumption. The overall objective of the challenge was to render participants more aware of how the news is constructed and, subsequently, more critical of the news they consume and share

Model-Augmented Estimation of Conditional Mutual Information for Feature Selection

Markov blanket feature selection, while theoretically optimal, is generally challenging to implement. This is due to the shortcomings of existing approaches to conditional independence (CI) testing, which tend to struggle either with the curse of dimensionality or computational complexity. We propose a novel two-step approach which facilitates Markov blanket feature selection in high dimensions. First, neural networks are used to map features to low-dimensional representations. In the second step, CI testing is performed by applying the $k$-NN conditional mutual information estimator to the learned feature maps. The mappings are designed to ensure that mapped samples both preserve information and share similar information about the target variable if and only if they are close in Euclidean distance. We show that these properties boost the performance of the $k$-NN estimator in the second step. The performance of the proposed method is evaluated on both synthetic and real data.Comment: Accepted to UAI 202

IR Kuiper Belt Constraints

We compute the temperature and IR signal of particles of radius $a$ and albedo $\alpha$ at heliocentric distance $R$, taking into account the emissivity effect, and give an interpolating formula for the result. We compare with analyses of COBE DIRBE data by others (including recent detection of the cosmic IR background) for various values of heliocentric distance, $R$, particle radius, $a$, and particle albedo, $\alpha$. We then apply these results to a recently-developed picture of the Kuiper belt as a two-sector disk with a nearby, low-density sector (40<R<50-90 AU) and a more distant sector with a higher density. We consider the case in which passage through a molecular cloud essentially cleans the Solar System of dust. We apply a simple model of dust production by comet collisions and removal by the Poynting-Robertson effect to find limits on total and dust masses in the near and far sectors as a function of time since such a passage. Finally we compare Kuiper belt IR spectra for various parameter values.Comment: 34 pages, LaTeX, uses aasms4.sty, 11 PostScript figures not embedded. A number of substantive comments by a particularly thoughtful referee have been addresse

In-vivo force, frequency, and velocity of dog gastrointestinal contractile activity

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44364/1/10620_2005_Article_BF02233438.pd

An automated framework for image classification and segmentation of fetal ultrasound images for gestational age estimation

Accurate assessment of fetal gestational age (GA) is critical to the clinical management of pregnancy. Industrialized countries rely upon obstetric ultrasound (US) to make this estimate. In low- and middle- income countries, automatic measurement of fetal structures using a low-cost obstetric US may assist in establishing GA without the need for skilled sonographers. In this report, we leverage a large database of obstetric US images acquired, stored and annotated by expert sonographers to train algorithms to classify, segment, and measure several fetal structures: biparietal diameter (BPD), head circumference (HC), crown rump length (CRL), abdominal circumference (AC), and femur length (FL). We present a technique for generating raw images suitable for model training by removing caliper and text annotation and describe a fully automated pipeline for image classification, segmentation, and structure measurement to estimate the GA. The resulting framework achieves an average accuracy of 93% in classification tasks, a mean Intersection over Union accuracy of 0.91 during segmentation tasks, and a mean measurement error of 1.89 centimeters, finally leading to a 1.4 day mean average error in the predicted GA compared to expert sonographer GA estimate using the Hadlock equation

2-Bromo-N-(4-chloro­phen­yl)-2-methyl­propanamide

In the title mol­ecule, C10H11BrClNO, there is a twist between the mean plane of the amide group and the benzene ring [C(=O)—N—C—C torsion angle = −27.1 (3)°]. In the crystal, inter­molecular N—H⋯O and weak C—H⋯O hydrogen bonds link the mol­ecules into chains along [010]