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In past work, time management interventions involving prompts, alerts, and planning tools have successfully nudged students in online courses, leading to higher engagement and improved performance. However, few studies have investigated the effectiveness of these interventions over time, understanding if the effectiveness maintains or changes based on dosage (i.e., how often an intervention is provided). In the current study, we conducted a randomized controlled trial to test if the effect of a time management intervention changes over repeated use. Students at an online computer science course were randomly assigned to receive interventions based on two schedules (i.e., high-dosage vs. low-dosage). We ran a two-way mixed ANOVA, comparing students' assignment start time and performance across several weeks. Unexpectedly, we did not find a significant main effect from the use of the intervention, nor was there an interaction effect between the use of the intervention and week of the course. 

Data-driven interatomic potentials (IPs) trained on large collections of first principles calculations are rapidly becoming essential tools in the fields of computational materials science and chemistry for performing atomic-scale simulations. Despite this, apart from a few notable exceptions, there is a distinct lack of well-organized, public datasets in common formats available for use with IP development. This deficiency precludes the research community from implementing widespread benchmarking, which is essential for gaining insight into model performance and transferability, and also limits the development of more general, or even universal, IPs. To address this issue, we introduce the ColabFit Exchange, the first database providing open access to a large collection of systematically organized datasets from multiple domains that is especially designed for IP development. The ColabFit Exchange is publicly available at https://colabfit.org, providing a web-based interface for exploring, downloading, and contributing datasets. Composed of data collected from the literature or provided by community researchers, the ColabFit Exchange currently (September 2023) consists of 139 datasets spanning nearly 70 000 unique chemistries, and is intended to continuously grow. In addition to outlining the software framework used for constructing and accessing the ColabFit Exchange, we also provide analyses of the data, quantifying the diversity of the database and proposing metrics for assessing the relative diversity of multiple datasets. Finally, we demonstrate an end-to-end IP development pipeline, utilizing datasets from the ColabFit Exchange, fitting tools from the KLIFF software package, and validation tests provided by the OpenKIM framework.

 

Massive Open Online Courses (MOOCs) have increased the accessibility of quality educational content to a broader audience across a global network. They provide access for students to material that would be difficult to obtain locally, and an abundance of data for educational researchers. Despite the international reach of MOOCs, however, the majority of MOOC research does not account for demographic differences relating to the learners' country of origin or cultural background, which have been shown to have implications on the robustness of predictive models and interventions. This paper presents an exploration into the role of nation-level metrics of culture, happiness, wealth, and size on the generalizability of completion prediction models across countries. The findings indicate that various dimensions of culture are predictive of cross-country model generalizability. Specifically, learners from indulgent, collectivist, uncertainty-accepting, or short-term oriented, countries produce more generalizable predictive models of learner completion. 

Grand Lake St. Marys (GLSM) is a popular recreational lake located in western Ohio, United States, generating nearly $150 million in annual revenue. However, recurring algal blooms dominated by Planktothrix agardhii , which can produce harmful microcystin toxins, have raised concerns about water safety and negatively impacted the local economy. Planktothrix agardhii is host to a number of parasites and pathogens, including an obligate fungal parasite in the Chytridiomycota (chytrids). In this study, we investigated the potential of these chytrid ( Rhizophydium sp.) to infect P. agardhii blooms in the environment by modifying certain environmental conditions thought to limit infection prevalence in the wild. With a focus on temperature and water mixing, mesocosms were designed to either increase or decrease water flow compared to the control (water outside the mesocosm). In the control and water circulation mesocosms, infections were found infrequently and were found on less than 0.75% of the Planktothrix population. On the other hand, by decreasing the water flow to stagnation, chytrid infections were more frequent (found in nearly 3x as many samples) and more prevalent, reaching a maximum infection rate of 4.12%. In addition, qPCR coupled with 16S–18S sequencing was utilized to confirm the genetic presence of both host and parasite, as well as to better understand the effect of water circulation on the community composition. Statistical analysis of the data confirmed that chytrid infection was dependent on water temperature, with infections predominantly occurring between 19°C and 23°C. Additionally, water turbulence can significantly reduce the infectivity of chytrids, as infections were mostly found in stagnant mesocosms. Further, decreasing the water circulation promoted the growth of the cyanobacterial population, while increasing water agitation promoted the growth of green algae (Chlorophyta). This study starts to explore the environmental factors that affect chytrid pathogenesis which can provide valuable insights into controlling measures to reduce the prevalence of harmful algal blooms and improve water quality in GLSM and similarly affected waterbodies. 

We investigate the distribution of the lithium abundances, A(Li), of metal-poor dwarf and subgiant stars within the limits 5500 K < Teff < 6700 K, −6.0 < [Fe/H] < −1.5, and log  g ≳ 3.5 (a superset of parameters first adopted by Spite and Spite), using literature data for some 200 stars. We address the problem of the several methods that yield Teff differences up to 350 K, and hence uncertainties of 0.3 dex in [Fe/H] and A(Li), by anchoring Teff to the infrared flux method. We seek to understand the behaviour of A(Li) as a function of [Fe/H] – small dispersion at highest [Fe/H], ‘meltdown’ at intermediate values (i.e. large spread in Li below the Spite Plateau), and extreme variations at lowest [Fe/H]. Decreasing A(Li) is accompanied by increasing dispersion. Insofar as [Fe/H] increases as the Universe ages, the behaviour of A(Li) reflects chaotic star formation involving destruction of primordial Li, which settles to the classic Spite Plateau, with A(Li) ∼ 2.3, by the time the Galactic halo reaches [Fe/H] ∼ −3.0. We consider three phases: (1) first star formation in C-rich environments ([C/Fe] > 2.3), with depleted Li; (2) silicates-dominated star formation and destruction of primordial Li during pre-main-sequence evolution; and (3) materials from these two phases co-existing and coalescing to form C-rich stars with A(Li) below the Spite Plateau, leading to a toy model with the potential to explain the ‘meltdown’. We comment on the results of Mucciarelli et al. on the Lower RGB, and the suggestion of Aguado et al. favouring a lower primordial lithium abundance than generally accepted.