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As generative AI products could generate code and assist students with programming learning seamlessly, integrating AI into programming education contexts has driven much attention. However, one emerging concern is that students might get answers without learning from the LLM-generated content. In this work, we deployed the LLM-powered personalized Parsons puzzles as scaffolding to write-code practice in a Python learning classroom (PC condition) and conducted an 80-minute randomized between-subjects study. Both conditions received the same practice problems. The only difference was that when requesting help, the control condition showed students a complete solution (CC condition), simulating the most traditional LLM output. Results indicated that students who received personalized Parsons puzzles as scaffolding engaged in practicing significantly longer than those who received complete solutions when struggling.

Planet engulfment has been identified as one of the mechanisms for enhancing lithium abundance in stars. However, comprehensive investigations into lithium signatures following such events remain limited. Stars born together, sharing a common origin and stellar characteristics, provide a unique opportunity to study these signatures and compare lithium abundances. We demonstrate that the distinctive signature of planet engulfment in lithium abundance is only discernible among highly similar stellar twins. We present lithium abundance measurements for 125 co-moving pairs of stars, representing the largest sample to date with a single, homogeneous assessment of high-precision lithium abundance. While lithium abundance enhancements in pairs showing planet engulfment signatures are within 0.35 dex, we find that even at fixed stellar parameters (temperature and age), the intrinsic scatter in lithium abundance is typically 0.35 dex for G/F dwarfs and can be as large as 0.6 dex for older and cooler stars due to internal stellar evolution processes. Since the planet engulfment signature from lithium can be masked by stellar intrinsic scatter, our findings raise questions about relying solely on lithium as an indicator for planet engulfment or attributing lithium-richness in stars primarily to planet engulfment events., Comment: 19 pages, 8 figures, 2 tables, accepted for publication in ApJ

The purpose of this study was to evaluate implementation of a whole school restorative practices model in Saint Paul Public Schools, Minnesota. Their model aims to embed practices to develop equitable relationships, engage students in learning, and respond relationally to behavioral concerns over a three-year period. The evaluation followed three cohorts of students in seven schools (4 elementary, 2 middle, and 1 high school) and included two quasi-experimental designed (QED) impact analyses and an analysis of implementation fidelity. The first QED was a school-level analysis focused on disproportionality in discipline outcomes, using a comparative short interrupted time series design with six treatment schools and a matched comparison group of 12 schools (N = 9,629 students). Outcome measures were school-level differences in dismissal and office discipline referral rates between White students and specific student groups of color (i.e., Black and Multiracial Black, Latinx, and American Indian/Alaskan Native students). The second QED was a longitudinal comparison of student-level attendance and discipline outcomes pre and post-implementation, with N= 5,043 students clustered within seven treatment schools and 14 matched comparison schools. Evaluation of fidelity was conducted on the seven schools that implemented restorative practices. Findings from the first QED showed that implementing restorative practices resulted in reduced disproportionality in differences in school-level dismissal rates between Black/Multiracial Black students and White students. The gap between Black and White student dismissals in treatment schools was reduced by an average of 0.021 dismissals per student more than the gap reduction observed in comparison schools (p = 0.02). No statistical evidence was found for differences between treatment and comparison schools for other analyses. For the second QED, no statistical support for improvements in student attendance and discipline was found, although the difference in dismissal rates between students attending restorative practice schools and comparison schools (1.6% vs. 1.0%) may have practical significance and is congruent with results from the first impact study. Fidelity results showed that treatment schools collectively met study thresholds for two of two core training components in year 1, one of four training and implementation components in year 2, and three of four training and implementation components in year 3. Higher levels of fidelity in year 3 indicate preliminary evidence of the minimum levels of program adherence needed to begin to impact school-level outcomes, such as reduced disproportionality in dismissal rates found in the first impact study. This study contributes promising evidence of whole school restorative practices' potential to reduce disparities in exclusionary discipline. Further research is needed to determine whether higher fidelity thresholds or longer periods of sustained implementation could have greater impacts on school-level and student outcomes. [This report was produced by the University of Minnesota's Healthy Youth Development -- Prevention Research Center.]

NASA's Solar Dynamics Observatory (SDO) mission collects extensive data to monitor the Sun's daily activity. In the realm of space mission design, data compression plays a crucial role in addressing the challenges posed by limited telemetry rates. The primary objective of data compression is to facilitate efficient data management and transmission to work within the constrained bandwidth, thereby ensuring that essential information is captured while optimizing the utilization of available resources. This paper introduces a neural video compression technique that achieves a high compression ratio for the SDO's image data collection. The proposed approach focuses on leveraging both temporal and spatial redundancies in the data, leading to a more efficient compression. In this work, we introduce an architecture based on the Transformer model, which is specifically designed to capture both local and global information from input images in an effective and efficient manner. Additionally, our network is equipped with an entropy model that can accurately model the probability distribution of the latent representations and improves the speed of the entropy decoding step. The entropy model leverages a channel-dependent approach and utilizes checkerboard-shaped local and global spatial contexts. By combining the Transformer-based video compression network with our entropy model, the proposed compression algorithm demonstrates superior performance over traditional video codecs like H.264 and H.265, as confirmed by our experimental results.

The Synthetic Corona Outflow Model (SynCOM), an empirical model, simulates the solar corona's dynamics to match high-resolution observations, providing a useful resource for testing velocity measurement algorithms. SynCOM generates synthetic images depicting radial variability in polarized brightness and includes stochastic elements for plasma outflows and instrumental noise. It employs a predefined flow velocity probability distribution and an adjustable signal-to-noise ratio to evaluate different data analysis methods for coronal flows. By adjusting parameters to match specific coronal and instrumental conditions, SynCOM offers a platform to assess these methods for determining coronal velocity and acceleration. Validating these measurements would help to understand solar wind origins and support missions such as the Polarimeter to Unify the Corona and Heliosphere (PUNCH). In this study, we demonstrate how SynCOM can be employed to assess the precision and performance of two different flow tracking methods. By providing a ground-truth based on observational data, we highlight the importance of SynCOM in confirming observational standards for detecting coronal flows., Comment: 18 pages, 10 figures

We have analyzed high-dispersion spectra in the Li 6708 Angstrom region for 167 stars within the anticenter cluster, NGC 2204. From 105 probable members, abundance analysis of 45 evolved stars produces [Fe/H] = -0.40 +/- 0.12, [Si/Fe] = 0.14 +/- 0.12, [Ca/Fe] = 0.29 +/- 0.07, and [Ni/Fe] = -0.12 +/- 0.10, where quoted errors are standard deviations. With E(B-V) = 0.07 and (m-M)_0 = 13.12, appropriate isochrones provide an excellent match from the main sequence through the tip of the giant branch for an age of 1.85 +/- 0.05 Gyr. Li spectrum synthesis produces A(Li) below 1.4 at the base of the red giant branch to a detectable value of -0.4 at the tip. Six probable AGB stars and all but one red clump star have only Li upper limits. A rapidly rotating red giant is identified as a possible Li-rich giant, assuming it is a red clump star. Main sequence turnoff stars have a well-defined A(Li) = 2.83 +/- 0.03 (sem) down to the Li-dip wall at the predicted mass of 1.29 solar masses. Despite the same isochronal age as the more metal-rich NGC 2506, the red giant luminosity distribution reflects a younger morphology similar to NGC 7789, possibly indicating a deeper metallicity impact on stellar structure and A(Li) than previously assumed. As in NGC 2506 and NGC 7789, the NGC 2204 turnoff exhibits a broad range of rotation speeds, making abundance estimation impossible for some stars. The cluster place within Galactic A(Li) evolution is discussed., Comment: Accepted for publication in the Astronomical Journal; 24 pages, 8 figures

Learning to program can be challenging, and providing high-quality and timely support at scale is hard. Generative AI and its products, like ChatGPT, can create a solution for most intro-level programming problems. However, students might use these tools to just generate code for them, resulting in reduced engagement and limited learning. In this paper, we present CodeTailor, a system that leverages a large language model (LLM) to provide personalized help to students while still encouraging cognitive engagement. CodeTailor provides a personalized Parsons puzzle to support struggling students. In a Parsons puzzle, students place mixed-up code blocks in the correct order to solve a problem. A technical evaluation with previous incorrect student code snippets demonstrated that CodeTailor could deliver high-quality (correct, personalized, and concise) Parsons puzzles based on their incorrect code. We conducted a within-subjects study with 18 novice programmers. Participants perceived CodeTailor as more engaging than just receiving an LLM-generated solution (the baseline condition). In addition, participants applied more supported elements from the scaffolded practice to the posttest when using CodeTailor than baseline. Overall, most participants preferred using CodeTailor versus just receiving the LLM-generated code for learning. Qualitative observations and interviews also provided evidence for the benefits of CodeTailor, including thinking more about solution construction, fostering continuity in learning, promoting reflection, and boosting confidence. We suggest future design ideas to facilitate active learning opportunities with generative AI techniques., Comment: Accepted to the Eleventh ACM Conference on Learning @ Scale (L@S 24) as a full paper

Novice programmers need to write basic code as part of the learning process, but they often face difficulties. To assist struggling students, we recently implemented personalized Parsons problems, which are code puzzles where students arrange blocks of code to solve them, as pop-up scaffolding. Students found them to be more engaging and preferred them for learning, instead of simply receiving the correct answer, such as the response they might get from generative AI tools like ChatGPT. However, a drawback of using Parsons problems as scaffolding is that students may be able to put the code blocks in the correct order without fully understanding the rationale of the correct solution. As a result, the learning benefits of scaffolding are compromised. Can we improve the understanding of personalized Parsons scaffolding by providing textual code explanations? In this poster, we propose a design that incorporates multiple levels of textual explanations for the Parsons problems. This design will be used for future technical evaluations and classroom experiments. These experiments will explore the effectiveness of adding textual explanations to Parsons problems to improve instructional benefits., Comment: Peer-Reviewed, Accepted for publication in Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 2 (SIGCSE 2024)