Understanding and Enacting Culturally Sustaining Pedagogy Through Multicultural Children\u27s Literature: A Case Study of Preservice Teachers in Georgia

This study explored how preservice teachers understand culturally sustaining pedagogy (CSP) and used multicultural children’s literature (MCL) as a way of enacting CSP in their field placement experiences. This qualitative study utilized the theoretical framework of culturally sustaining pedagogy (Paris & Alim, 2017) and a single case study design (Stake, 1995; Yin, 2018) to explore five preservice teachers’ understanding and perceptions of CSP and its enactment in the classroom through MCL during their final student teaching semester in their hometowns. Data collected through lesson plans, literature lists, video recordings, and semi-structured interviews and then inductively coded through holistic coding and subcoding (Creswell & Poth, 2018; Saldaña, 2016). After pattern coding the subcodes and analyzing the data, the themes surrounding CSP showed that students believed CSP related to sociocultural and academic knowledge of students and the use of location and real life examples within the classroom. When analyzing MCL use as enactment of CSP, the themes of selecting MCL by content, academic abilities and student interests emerged. These findings all showed that participants needed greater instruction on the tenets of CSP in education preparation programs and modeling of practical teaching strategies, such as the use of MCL, to use in the classroom. The participants in this study were able to build connections with students, but unable to dig deeper into the cultural backgrounds, as they focused on student preferences and interests, and therefore were unable to select MCL as an enactment of CSP (Paris & Alim, 2017). The lack of knowledge of the foundational tenets of CSP and its enactment in the classroom were major findings from the data. Due to this, educator preparation programs need to provide stronger instruction around diversity, cultural and equity pedagogies to equip future teachers for the diverse student population they will teach through their content and methods courses

Building Classroom Community in Elementary Literacy Methods Courses

In this article, two elementary teacher educators provide a novel approach to building relationships and classroom community in their face to face elementary literacy methods courses. The authors present their classroom practice entitled “Tell Me Time” (TMT), mirroring morning meetings in K–12 settings, as the staple of building classroom community in their courses. They describe how TMT has been used to build student relationships with each other and with instructors through providing unique, personalized opportunities to socialize and connect through listening, speaking, reading, and writing. Threaded throughout, the authors detail specific feedback from students and highlight how TMT has helped students find common ground with one another and made them feel comfortable, cared for, and respected by their instructors. The authors describe the reasons behind implementing TMT, outline specific TMT activities and examples that have been most effective with their students, provide insight into how to integrate technology with TMT, delineate specific steps for successfully implementing TMT, and describe lessons learned from the implementation of TMT

Out with the Old, in with the New: Digital Interactive Journals in an Elementary Language Arts Methods Course

In this article, two elementary literacy teacher educators provide elementary language arts content and methods by implementing digital interactive journals in a face-to-face classroom setting. The authors present how this practice evolved from traditional interactive journals pre-COVID to an innovative and engaging learning experience post-COVID. Interactive journals provide students with opportunities to explore and apply content and methods, while also learning a teaching strategy that pre-service teachers can implement in their own elementary classrooms. The authors describe the design and development of digital interactive journals along with student work samples and instructional steps to transfer and implement this strategy across disciplines

A reluctant additive model framework for interpretable nonlinear individualized treatment rules

Individualized treatment rules (ITRs) for treatment recommendation is an important topic for precision medicine as not all beneficial treatments work well for all individuals. Interpretability is a desirable property of ITRs, as it helps practitioners make sense of treatment decisions, yet there is a need for ITRs to be flexible to effectively model complex biomedical data for treatment decision making. Many ITR approaches either focus on linear ITRs, which may perform poorly when true optimal ITRs are nonlinear, or black-box nonlinear ITRs, which may be hard to interpret and can be overly complex. This dilemma indicates a tension between interpretability and accuracy of treatment decisions. Here we propose an additive model-based nonlinear ITR learning method that balances interpretability and flexibility of the ITR. Our approach aims to strike this balance by allowing both linear and nonlinear terms of the covariates in the final ITR. Our approach is parsimonious in that the nonlinear term is included in the final ITR only when it substantially improves the ITR performance. To prevent overfitting, we combine cross-fitting and a specialized information criterion for model selection. Through extensive simulations, we show that our methods are data-adaptive to the degree of nonlinearity and can favorably balance ITR interpretability and flexibility. We further demonstrate the robust performance of our methods with an application to a cancer drug sensitive study

Effects of Fluorination on Fused Ring Electron Acceptor for Active Layer Morphology, Exciton Dissociation, and Charge Recombination in Organic Solar Cells

Fluorination is one of the effective approaches to alter the organic semiconductor properties that impact the performance of the organic solar cells (OSCs). Positive effects of fluorination are also revealed in the application of fused ring electron acceptors (FREAs). However, in comparison with the efforts allocated to the material designs and power conversion efficiency enhancement, understanding on the excitons and charge carriers' behaviors in high-performing OSCs containing FREAs is limited. Herein, the impact of fluorine substituents on the active layer morphology, and therefore exciton dissociation, charge separation, and charge carriers' recombination processes are examined by fabricating OSCs with PTO2 as the donor and two FREAs, O-IDTT-IC and its fluorinated analogue O-IDTT-4FIC, as the acceptors. With the presence of O-IDTT-4FIC in the devices, it is found that the excitons dissociate more efficiently, and the activation energy required to split the excitons to free charge carriers is much lower; the charge carriers live longer and suffer less extent of trap-assisted recombination; the trap density is 1 order of magnitude lower than that of the nonfluorinated counterpart. Overall, these findings provide information about the complex impacts of FREA fluorination on efficiently performed OSCs

The Periotest Method: Implant-Supported Framework Precision of Fit Evaluation

: In this study, the Periotest instrument was used to measure the precision of fit between cast high noble-metal frameworks and the supporting implants in a patient-simulation model. Three framework conditions and three implant-location variables were used to evaluate the rigidity of the assembly as measured by the Periotest method. The framework variables were (1) one-piece castings (OPC); (2) sectioned-soldered inaccurate castings (SSIC); and (3) sectioned-soldered accurate castings (SSAC). The implant-location variables were right anterior (RA), center (C), and left anterior (LA). Materials and Methods : The patient simulation model used consisted of three self-tapping BrÅnemark implants in a reasonable arch curvature in bovine bone. Three working casts were fabricated from the patient-simulation model using polyvinyl siloxane and tapered impression copings. From the working casts, three sets of three frameworks were fabricated as OPCs, SSICs, and SSACs using type 3 high noble alloy. The SSICs were fabricated with a quantitative misfit of 101.6 Μm at the facial surface, between the abutment-to-gold cylinder interface at the C implant location. Periotest value (PTV) measurements were made at the midfacial surface of the frameworks directly above each abutment-to-gold cylinder interface. Three measurements were made for each test condition. The data were analyzed to compare framework condition(s) and implant location(s) using ANOVA and Fisher's Protected Least Significant Difference Comparison Test. Results : The ANOVA showed that significant differences exist between the mean PTV data for framework condition and for implant location (p < .01). Significant differences were shown between the mean PTV data for the SSAC assemblies and the OPC and SSIC assemblies. The SSICs displayed a more positive (+) mean PTV than the OPCs. The OPC assemblies had a more positive mean PTV than the SSAC assemblies. The mean PTV data for the SSAC assemblies had a significantly different PTV (p < .01) than the other two framework condition assemblies. The OPC and the SSIC assemblies had PTVs that were not significantly different. The C implant location was significantly different from the RA and the LA implant locations (p < .01). The RA and the LA implant locations were not significantly different from each other. The C implant location always demonstrated the most positive mean PTV regardless of the framework condition being tested. Conclusions : The Periotest instrument quantified differences in the precision of fit between three framework conditions. The SSAC assemblies were significantly more rigid than the OPC and SSIC assemblies. The OPC and SSIC assemblies' mean PTVs were not significantly different. The mean PTVs for the C implant location and the RA and LA implant locations were significantly different (p < .01). The mean PTVs of the RA and LA implant locations were not significantly different. The implant-location PTVs followed the same rank order for all three framework conditions. The procedures used to fabricate a more precise fit between the framework and the supporting implants is influenced by the skill of the clinician and technician.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75096/1/j.1532-849X.1996.tb00298.x.pd

Advocacy for women migrant workers in Malaysia through an Intersectionality lens

Analysing labour migration through the lens of intersectionality provides valuable insights into the complex identities of women migrant workers and the multiple discriminations they struggle with. Intersectionality advocates argue that only through adopting such an approach can women’s multiple discriminations be challenged. Drawing on a case study of seven NGOs and one focus group of women migrant workers in a non-western context, Malaysia, we explore how advocacy organisations understand, interpret and adopt an intersectional approach in advancing the rights of its women migrant workers. We show that there are challenges, specific to the local context, which reduces the likelihood of organisations doing so. The Malaysian experience likely has significance for similar advocacy in other Asian countries

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients’ predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types