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The increase in infections caused by multi-resistant Gram-negative bacteria, like Stenotrophomonas maltophilia , has become a growing health crisis worldwide. S. maltophilia poses a risk because of its tendency to opportunistically infecting patience for example through colonization of catheters in hospital environments using its intrinsic resistance against multiple antibiotics. Through the COVID-19 pandemic it gained more prominence by being a key pathogen in respiratory co-infections. This study will present a structural analysis of StmPr1, S. maltophilia ’s main virulence factor, an excreted serine protease. Our study outline structure and functional aspects of StmPr1, revealing a unique autoproteolytic activity resulting in a shortened version of the active enzyme. We also investigated the potential of two groups of peptide-based inhibitors, one being acetyl- and the other being boron-based inhibitors. The focus here lies on Bortezomib, a boron-based serine protease inhibitor, and its potential therapeutic use against S. maltophilia. We provide a structure-function analysis which includes X-ray crystallography data with resolutions ranging from 1.64 to 2.08 Å, molecular dynamic simulations and small-angle X-ray scattering (SAXS) experiments. These data provide a deeper understanding of StmPr1’s resilience and mechanisms, while highlighting the relevance of StmPr1’s C-terminal extension for correct folding and its stability. Moreover, it also shows that StmPr1 is promising target for further drug discovery investigations to identify compounds and drugs to treat S. maltophilia infections.

GLIMMPSE Version 3 is a free, web-based, open-source software tool, which calculates power and sample size for general linear mixed models with Gaussian errors. The software permits power calculations for clinical trials, randomized experiments, and observational studies with clustering, repeated measures, and both, and almost any testable hypothesis. The software has been supported by five United States National Institutes of Health (NIH) grants, is used for more than 14,000 power or sample size calculations per year, has been cited in almost 500 peer-reviewed manuscripts, and used to design more than 200 million dollars in NIH-funded studies. This release provides several new features. The back end has been refactored in Python. The interface has been simplified, requiring user decisions about only one topic per screen. A new menu improves specification of both between-participant and within-participant hypotheses. A recursive algorithm permits computing covariances for up to ten levels of clustering. An updated Monte Carlo simulation using five new examples with clustering, longitudinality, or both, shows accuracy of the power approximations to within 0.01. Five new examples demonstrate power or sample size calculations for 1) a cluster-randomized trial, 2) a longitudinal study with repeated measures, 3) a multilevel study with a multivariate outcome, 4) a multilevel and longitudinal study, and 5) a complex study with a subgroup factor, repeated measures, and intervention-by-location interaction.

The purpose of this research was to provide validity evidence for the Information Literacy Self-Efficacy Scale (ILSES), a widely used instrument that was constructed in 2006. The researchers were interested in investigating the validity of this instrument due to the evolution of the information environment that has taken place since the scale's original development, mostly as a result of the prominence of the internet. Data were collected from undergraduate students participating in a broader information literacy research study (n = 253). Data were subjected to descriptive analyses, internal consistency reliability, and a confirmatory factor analysis (CFA). After evaluating three different CFA models based on the ILSES' construction, the researchers determined that a four-factor model fit the data with the following latent constructs: 1) Initiating the search strategy, 2) Assessing and comprehending the information, 3) Interpreting, synthesizing, and using the information, and 4) Evaluating the product and process. A discussion of these findings is provided in light of the evolving information environments in which undergraduate students are expected to use information for their academic, personal, and professional lives.

Gamification research in educational settings has produced mixed results on student learning outcomes. Educational researchers and practitioners both struggle with identifying when, where, and how to use gamification design concepts. The present study provides findings from a meta-analysis that integrated the empirical, quantitative research on gamification in formal educational settings on student learning outcomes. This was achieved by examining the overall effect size, identifying which gamification design elements (e.g., badges) were used, and determining under what circumstances (e.g., engineering education) gamification works. The final corpus of data included 30 independent studies and associated effect sizes comparing gamification to non-gamification conditions while accounting for N  = 3083 participants. The overall effect size using a random-effects model is g  = .464 [.244 to .684] in favor of the gamification condition, which is a small to medium effect size. We examined 14 different gamification design elements (e.g., leaderboards) and showed that each leads to different effects on student learning outcomes. Further, the type of publication (e.g., journal article), student classification (e.g., undergraduate), and subject area (e.g., mathematics) are also investigated as moderators. We provide a discussion of our findings, some recommendations for future research, and some brief closing remarks.

Based on the achievement goal theory, this experimental study explored the influence of predictive and descriptive learning analytics dashboards on graduate students’ motivation and statistics anxiety in an online graduate-level statistics course. Participants were randomly assigned into one of three groups: (a) predictive dashboard, (b) descriptive dashboard, or (c) control (i.e., no dashboard). Measures of motivation and statistical anxiety were collected in the beginning and the end of the semester via the Motivated Strategies for Learning Questionnaire and Statistical Anxiety Rating Scale. Individual semi-structured interviews were used to understand learners’ perceptions of the course and whether the use of the dashboards influenced the meaning of their learning experiences. Results indicate that, compared to the control group, the predictive dashboard significantly reduced learners’ interpretation anxiety and had an effect on intrinsic goal orientation that depended on learners’ lower or higher initial levels of intrinsic goal orientation. In comparison to the control group, both predictive and descriptive dashboards reduced worth of anxiety (negative attitudes towards statistics) for learners who started the course with higher levels of worth anxiety. Thematic analysis revealed that learners who adopted a more performance-avoidance goal orientation approach demonstrated higher levels of anxiety regardless of the dashboard used.

The purpose of this study was to examine the effects of gamification used in formal educational settings on student affective and behavioral outcomes. Using systematic procedures to identify and screen the manuscripts across 18 academic databases, we identified 13 studies with behavioral outcomes and 19 studies with affective outcomes employing gamification in educational settings. These manuscripts accounted for a total of N = 1974 (n = 987 in gamification, n = 987 in control) participants in the affective model, and N = 1596 (n = 760 in gamification, n = 836 in control) in the behavioral model. Employing random-effects models, we calculated two statistically significant medium overall effect sizes for affective outcomes at g = .574 [.384, .764] and for behavioral outcomes at g = .740 [.465, 1.014]. We also examined 14 different gamification design elements (e.g., leaderboards, badges, etc.) as moderators to pinpoint the conditions in which gamification may be effective. Additionally, we examined contextual elements as moderators, including the discipline, student level, and publication source. Publication bias was not identified as a threat to either the affective or behavioral model. We also provide a discussion of our findings, limitations, and suggestions for future research.

The explosion of the Internet and prominence of social media platforms have created a dynamic online information ecosystem. The roles of traditional gatekeepers of credible information (e.g., news organizations) have been diminished, and individuals are often on their own in evaluating an abundance of information online coming from more sources than ever before. As a result, misinformation can easily spread in the online environment. The spread of misinformation can lead to individuals making decisions that are not in the best interests of themselves or the public, and can impact society through increasing cynicism, apathy, and extremism. To combat misinformation, individuals need to develop sufficient skills in online credibility assessment - the ability to assess the credibility of online information and resources. Assessing the credibility of information, however, has been shown to be a difficult and complicated task in the online environment. This dissertation includes three interrelated studies to address the problem of the spread of misinformation in the complex online environment. These studies address this problem through a focus on a metacognitive approach to developing credibility assessment skills in individuals. First, a systematic review of literature was conducted on the intersection of metacognition and credibility assessment. This study conceptualized the roles that metacognition can play in credibility assessment and discussed how metacognition has been incorporated into credibility assessment instruction in the past. The second study documented all stages of the design and development process involved in creating an online, multimedia instructional module that applies a metacognitive approach to foster credibility assessment skills in undergraduate learners. Analysis techniques, instructional design models and frameworks, and evaluation strategies that were integral to the design process are discussed. Finally, an experimental study evaluated this online module and explored the influence of metacognitive prompting on undergraduate learners' credibility assessment abilities, behaviors, and perceptions. Metacognitive prompting was not found to lead to significant differences between groups, but analysis of quantitative and qualitative data provided valuable insights regarding the metacognitive approach to credibility assessment instruction and its impact on performance and behaviors. Findings for each study, key takeaways, and implications on research and practice are discussed.

Aim/Purpose: The purpose of this study was to examine the impact of the flipped classroom model on learner achievement and satisfaction for undergraduate learners Background: The context for this research on the flipped classroom was an introductory technology literacy course at a public, research university. Methodology: This study employed a quasi-experimental pre-test/post-test design consisting of two groups: the treatment condition (sections in which instructors implemented the flipped classroom model for the module that was the focus of the study) and control condition (sections in which instructors lectured in the face-to-face meeting, then learners completed the practice online as homework). Learners in each group received their form of instruction and completed the same instructional activities, tests, and surveys. These data were analyzed using descriptive statistics, Analysis of Variance (ANOVA), and Analysis of Covariance (ANACOVA) models. Contribution: This research adds to a growing base of literature on the flipped classroom, a special instantiation of blended learning. Findings: Results indicated that there was no significant difference between the two groups in terms of learner achievement. In terms of learner satisfaction, however, there was a significant difference in which participants favored the control condition. Recommendations for Practitioners: Practitioners should select situations that are appropriate for the flipped classroom context. This should be reflected in the implementation of curriculum that would benefit from the affordances of this approach. Recommendation for Researchers: Researchers should seek to replicate this study in longer durations and using rigorously developed and validated measures. Impact on Society: This study suggests the flipped classroom may not be perceived by learners in an introductory technology literacy course as beneficial to their satisfaction. Future Research: Future research should seek to replicate studies in technology literacy courses to identify the optimal learning situations for the blended learning environment.

The purpose of this design and development case is to document the design, development, and evaluation of a statistics course tailored for educational technology doctoral students in an online program. The course is intended to prepare online doctoral students to both consume and produce quantitative research in the field of educational technology using relevant and authentic learning experiences. This paper first outlined the course features used to engage students in the learning materials. Twenty doctoral students enrolled in this online statistics course provided relevant data on three occasions (pre-, mid-, and post-surveys) during a 16-week academic semester. Drawing from both quantitative and qualitative data sources, this paper demonstrates that the use of authentic and relevant datasets based on peer-reviewed publications from the field of educational technology coupled with engaging practice assignments, designed for students to exercise the use of various statistical techniques using SPSS, was a successful strategy. Students found learning about analysis of covariance (ANCOVA) along with factorial and repeated measures analysis of variance (ANOVA) in the same module overwhelming. Results showed that students’ level of statistics anxiety significantly decreased in three key areas. The discussion provides interpretations of the findings as well as our lessons learned.

Early-generation in-situ dust detectors in near-Earth space have reported the occurrence of clusters of sub-micron dust particles that seemed unrelated to human spaceflight activities. In particular, data from the impact ionization detector onboard the HEOS-2 satellite indicate that such swarms of particles occur throughout the Earth's magnetosphere up to altitudes of 60,000 km -- far beyond regions typically used by spacecraft. Further account of high-altitude clusters has since been given by the GEO-deployed GORID detector, however, explanations for the latter have so far only been sought in GEO spaceflight activity. This perspective piece reviews dust cluster detections in near-Earth space, emphasizing the natural swarm creation mechanism conjectured to explain the HEOS-2 data -- that is, the electrostatic disruption of meteoroids. Highlighting this mechanism offers a novel viewpoint on more recent near-Earth dust measurements. We further show that the impact clusters observed by both HEOS-2 and GORID are correlated with increased geomagnetic activity. This consistent correlation supports the notion that both sets of observations stem from the same underlying phenomenon and aligns with the hypothesis of the electrostatic breakup origin. We conclude that the nature of these peculiar swarms remains highly uncertain, advocating for their concerted investigation by forthcoming dust science endeavours, such as the JAXA/DLR DESTINY+ mission.