Teachers College
  • New York City, United States
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Context This study examines how urban American Indian high school students negotiate their civic identities within the settler colonial structures of urban American public schools. Research Question How do urban American Indian students negotiate civic identities in spaces where civic concepts are taught, such as American history classes in an urban public high school and a Native Youth Council (Native YC)? Research Design This critical participatory ethnographic study examines the negotiation of civic identity by 11 urban Indigenous students in social studies classes, a Native YC, and a school in Washington State, where the STI curriculum is taught. Safety zone theory and tribal critical race theory were used to understand students’ experiences and their stories from observations, participant interviews, and focus groups, which were employed as data. Conclusions/Recommendations The study found that the social studies classes and Native YC were zones of sovereignty (ZoS), forwarding survivance and self-determination for Native students. Students learned about the Indigenous civic constructs of sovereignty, self-determination, dual citizenship, tribal self-government, and federal Indian policy inside and outside of school, all of which supported Native students in civic identity development. Recommendations on teaching Indigenous civic constructs to all students as part of teaching for critical democracy in public schools as a component of social studies classes and extracurricular activities are discussed.
DOTA@Sludge@Chitosan was synthesized by a facile treatment using DOTA (1,4,7,10-tetraazacyclododecane-N,N′,N,N′-tetraacetic acid) to modify dry sludge and chitosan in an acidic solution. The performance of developed DOTA@Sludge@Chitosan was investigated for the adsorptive removal of Cr6+ and Pb2+ from water. Characterization studies showed that the materials possess a large surface area (52.009 m2/g), pore volume (0.069 cm3/g), and abundant functional groups of amino and hydroxyl. The prepared material showed a synergetic effect due to carboxylic acid and sludge, effectively removing Cr6+ and Pb2+. It reached 329.4 mg/g (Pb2+) and 273.3 mg/g (Cr6+) at 20 °C, much higher than commercial activated carbon. The regeneration of the adsorbent was tested for six adsorption and desorption cycles. The results demonstrate that the DOTA@Sludge@Chitosan adsorbent well-maintained high adsorption capacity attributed to its stability, making it a promising adsorbent for heavy metals removal from industrial effluent.
This paper proposes top-level dual-exploitation particle swarm optimization (TLDEPSO), which aims to use the evolutionary experience between particles better and enhance the convergence performance of the algorithm. In TLDEPSO, the population is divided into top-level particles and ordinary particles according to fitness, and each iteration is divided into two stages to be executed. For the first stage, a particle modification method based on gene editing technology is proposed and applied to top-level particles to improve the search direction of the population and explore the problem space better. For other ordinary particles in the population, the learning strategy of the canonical ring neighborhood topology PSO is used to update the velocity and the position to maintain the diversity of the population. For the second stage, a top-level neighborhood exploration mechanism is proposed for top-level particles to accelerate the algorithm’s convergence. In addition, an adaptive dynamic adjustment mechanism for the parameters of acceleration coefficient, inertia coefficient and the number of top-level particles is proposed to balance better the global exploration and local exploitation capabilities of the algorithm. On the latest CEC2022 test benchmark, comparison and statistical analysis with seven advanced PSO variants and three CEC competition top algorithms demonstrate TLDEPSO’s superior performance in solving functional problems with different fitness landscapes.
This study investigated rater confidence when rating airway invasion with the penetration-aspiration scale (PAS) on flexible endoscopic evaluations of swallowing (FEES), raters’ accuracy against a referent-standard, inter-rater reliability, and potential associations between clinician confidence, experience, and accuracy. Thirty-one clinicians who use FEES in their daily practice were asked to judge airway invasion with the PAS and to rate their confidence that their score was correct (0–100) for 40 video clips, five in each of the 8 PAS categories. We found that raters were most confident in rating PAS 1, 7, and 8. The average confidence score across all videos was 76/100. Confidence did not have a significant relationship with accuracy against the referent-standard. Accuracy was highest for PAS 1 (92%), followed by PAS 8 (80%), PAS 7 (77%), and PAS 4 (72%). Accuracy was below 60% for PAS 2, 3, 5, and 6, the lowest being for PAS 3 (49%). Mean accuracy for all ratings, compared to referent-standard ratings, was highest for the intermediate group (71%), followed by expert (68%) and novice (65%). In general, we found that certain PAS scores tend to be rated more accurately, and that participating SLPs had varied confidence in PAS ratings on FEES. Potential reasons for these findings as well as suggested next steps are discussed.
Background Satisfaction of fundamental needs is an important concept in sport, but currently there is no tool in Arabic to measure this construct. Basic needs are often linked to high rates of motivation and performance. It is necessary to develop tools to assess psychological needs in the sport context. Aim This study aimed to validate the Basic Needs Satisfaction in Sport Scale (BNSSS) in Arabic language across Tunisian athletes, and to test its psychometric properties (factorial structure, internal reliability, construct validity, and sensitivity). Methods Athletes in various sports participated in this study (370 men, 146 women; mean age 18.35) and voluntarily completed the Arabic version of the BNSSS-20. Both exploratory (EFA, N = 294; males: 68%; females: 32%; [14–18] = 182; [19–28] = 112) and confirmatory (CFA; N = 222; males: 76.6%; females: 23.4%; [14–18] = 103; [19–28] = 119) factor analyses were examined. Results Results from the EFA suggest that the BNSSS scale reflects the theoretical model well, with good internal consistency for all factors. All 20 items of BNSSS revealed excellent reliability (McDonald’s omega = 0.773, Cronbach’s α = 0.886, Gutmann’s λ6 = 0.970) and good temporal stability (ICC = 0.84, 95% CI = 0.55–0.93) over a 4-week period. Likewise, the CFA fit indices were excellent. Conclusion The BNSSS presented excellent fit to the theoretical model for all indices, confirming the factorial structure and providing validity of the instrument for Tunisian athletes.
Live-streaming commerce has become increasingly prevalent in recent years and has significantly impacted consumer purchasing behavior. This study aims to assess the impact of live-streaming environmental features, presence, and perceived trust on consumer purchase intention, building upon the stimulus-organism-response (SOR) model. The authors obtained 392 valid responses from a survey, which were analyzed using PLS-SEM to yield statistical insights. The study found that live-streaming environmental features directly impact presence, perceived trust, and purchase intention while also indirectly affecting purchase intention through perceived trust. Additionally, perceived trust directly affects purchase intention, and presence indirectly influences purchase intention through perceived trust. This research contributes to the existing literature on consumers’ purchase intention in live-streaming commerce. Additionally, this study expands the comprehension of how environmental cues during live streaming affect purchase intention by examining the mediating roles of presence and perceived trust. As a result, this research provides a reference for enhancing the marketing effectiveness of live-streaming commerce.
Transformative assessment is a classroom assessment aimed at changing both how teachers teach and students learn a lesson. Nowadays, this kind of assessment needs to be practiced to encourage teachers to be creative and flexible when designing their assessments and for students to be reflective and take responsibility for their learning. Hence, this qualitative study aimed to examine mathematics teachers’ practices of transformative assessment and the associated challenges. Data collected from eight teachers using semi-structured interviews and lesson observations were analysed thematically. It was found that teachers did, in fact, not provide evidence of practicing transformative assessment in their teaching. There was an attempt to align assessment to learning outcomes even though the assessment practice utilized remained traditional. Teachers’ assessment practices focused on fast-learners while leaving behind the majority, lacking balance and equity. Teacher, student, and school-related factors were the main challenges facing the teachers during assessment practices. The study presents possible strategies by which transformative assessment practices in mathematics teaching can be developed, implemented, and sustained to improve students’ learning.
We examined the role of morphological processing in the reading of inflections and derivations in Arabic, a morphologically-rich language, among 228 first-graders and 230 second-graders. All words were morphologically complex, with differences in number of morphemes and morphological transparency. Inflections consisted of three morphemes, with high transparency of the root morpheme, while derivations consisted of two morphemes with lower transparency of the root. Results indicated that, despite their matching in frequency and syllabic length, reading performances of derivations was better than those of inflections. That is, three-morphemic highly transparent inflections were read slower and involved more errors than bi-morphemic less transparent derivations. These differences in reading performance between inflectional and derivational words might suggest that Arab-speaking novice readers use a morphological decomposition process that is reflected in reading accuracy and fluency. The results highlight the important role morphology has in reading, even at a young age, along with reading acquisition.
It aims to improve the efficiency of information collection and extraction in the current intelligent transportation system, and accurately mine the vehicle trajectory data By using Artificial Intelligence (AI) and Deep Learning methods, the trajectory data generated during vehicle driving are deeply mined and analyzed, and the characteristics of driving behavior of vehicle drivers are modeled and analyzed in detail. Then, a method of mining driving behavior characteristics based on Convolutional Neural Network (CNN) and vehicle trajectory is proposed. Based on the mathematical principle of wavelet packet and Least Square Support Vector Machine (LSSVM), a combined model of trajectory mining is constructed and applied to the short-term prediction of traffic flow. The traffic flow of Binjiang Road and Renmin Road in Guangzhou, Guangdong Province from August 19 to August 21, 2021 is predicted to verify the accuracy of the trajectory mining combined model. The results show that the combination model of data mining has good fitting effect, and the average accuracy is above 0.8. Besides, the effectiveness of the Deep Learning model in driver behavior classification is verified. The accuracy of the classification model is 75.2% for trajectory, and that is 76.8% for driver behavior characteristics. It is of great significance to effectively utilize the knowledge data in Intelligent Transportation System (ITS) and extract valuable information from it, which has certain reference value for the subsequent refined prediction of vehicle behavior.
This paper analyzes the technical and educational aspects of English teaching informatization, explores the impact of university English teaching reform on traditional English teaching in the informatization era, and designs an information technology-assisted precise English teaching model in three stages: before, during and after class. To realize accurate teaching, the FCE_DFM teaching resource recommendation model is constructed by the fuzzy C-mean clustering method in combination with the recommendation model based on self-encoder and FM. The RMSE and MAE values of this paper were reduced by about 0.12 and 0.2, respectively, compared with traditional recommended teaching, the attendance rate was increased by 22%, and about 2.0 points improved the emotional attitude toward learning through English teaching reform practice.
This paper first investigates English teaching using an AI speech recognition platform. In the feature value analysis module, using the hidden Markov model is beneficial to improve the accuracy of MFCC features for parameter speech recognition. Therefore, the hidden Markov model is proposed for English audio recognition. Secondly, according to the recognition platform pronunciation error detection, the speech pronunciation scoring algorithm is proposed, and an example analysis of English blended teaching based on AI speech recognition is conducted. The results show that the blended teaching mode is 0.33 points higher than the traditional teaching mode in the entrance examination, 2.96 points higher in the total score of Grade 4, 2.10 points higher in listening, 0.57 points higher in reading, and 0.31 points higher in writing, and the effect of blended teaching is positively correlated with the students’ entrance English level. This study proposes a blended English teaching strategy that utilizes a speech recognition platform to achieve innovation and diversification of English teaching efforts.
This special issue of Teachers College Record—“Minding the Gap in Education Discourse: Equity, Inclusion, and Belonging in Independent and International Schools”—aims to bring attention to independent and international private schools through the lenses of equity, inclusion, and belonging. In this issue, scholars and practitioners address gaps in education and education leadership discourse regarding considerations of equity, inclusion, and belonging. Historically, education discourse regarding equity, inclusion, and belonging has skewed largely toward public and charter education. While, according to the National Center for Educational Statistics (NCES), just 9% of PK-12 students nationally are enrolled in independent schools, independent schools nationally have much to contribute to the critical conversation concerning equity across the educational ecosystem. When we expand our attention to include international private schools, the numbers and scale shift dramatically. According to ISC Research, international private schools serve 6.74 million students. When the growing engagement of international private schools in the work of equity, inclusion, and belonging is considered, even more questions and possibilities emerge for the examining, understanding, and acting in research-informed ways on behalf of equity, inclusion, and belonging in education writ large. This special issue insists on the importance of considering schools as national and global systems of learning and socialization, and independent and international schools in particular as important players in the ecosystem of education in the United States and globally. The works in this issue tune us to the complex, multileveled ecosystem that we refer to as PK–12 schooling and to the importance of noticing and acting at all levels of that ecosystem to ensure research-informed thinking, decision-making, action, and impact.
Cyber threats are clearly understood across the security landscape using honeypot technologies across industrial cyber-physical systems (ICPS). Specifically, Distributed Denial of Service (DDoS) and Man in the Middle (MITM) attacks are the significant malicious threats in ICPS. This paper’s anti-honeypot-enabled attack detection system for ICPS is developed using the Stakerlberg dynamic game (SDG) theory and Reinforcement learning (RL) models. The interactions between the ICPS defender and the attackers are captured through BSDG model. RL state and rewards functions exhibit various possible ICPS defenses and offensive attackers. It will capture the attack sequences in the ICPS and identify the attackers efficiently. The simulation and numerical evaluation of two malicious attacks DDoS and MITM, using the proposed strategy, is efficient in detecting malicious activities. This model obtained improved detection rate, time, and accuracy by comparing existing approaches.
In an increasingly individualistic society in which the economic forecast has been uncertain for the past several years, independent schools have struggled to understand donors’ motivations for giving. In addition, schools continually examine the way their annual giving campaigns articulate how donors’ gifts align with the schools’ missions and future strategic goals. This case study aims to form an understanding of giving practices for independent schools. Because annual giving is central to the financial sustainability and success of a school, this study’s aim is to fully examine the extent to which school constituents (parents, grandparents, alumni, faculty members, parents of alumni, administration, and students) are involved in the annual giving process and aware of its impact. There is a distinct disparity in the outcome of fundraising efforts of long-standing independent schools and institutions with existing endowments and newer independent schools. The question becomes, How do the latter schools better educate their constituents about the importance of the annual fund? Using qualitative data elicited from survey and interview methods, this study examines the motivating factors that have a significant impact on annual giving practices.
The electrocatalytic hydrogen evolution reaction (HER) is an ideal method for hydrogen production. Transition metal complex electrocatalysts exhibit poor HER activity due to excessive or weak adsorption of H during the electrochemical reduction of water to molecular hydrogen in acidic environments. Developing specific functional complex materials as desired catalysts is challenging. Here, an electrochemical surface restructuring strategy of polyoxometalate (POM)-modified Ag materials toward the HER with a dramatically decreased overpotential under acidic aqueous conditions is established. We prepared two POM [SiW12O40]4- (SiW12)/[P2W18O62]6- (P2W18)-based Ag-2,2'-biimidazole (H2biim) inorganic-organic hybrid compounds (1 and 2) via the hydrothermal method and these two compounds undergo an electrochemical restructuring process in 0.5 M H2SO4 during the HER, in which Ag nanoparticles are in situ formed with the basic structures of SiW12 and P2W18 being maintained. The activated catalysts (1-AC-RDE and 2-AC-RDE) exhibit good electrocatalytic activity for the HER with good long-term stability, and the required overpotentials at a current density of 10 mA cm-2 are 112 mV (1-AC-RDE) and 91 mV (2-AC-RDE) with Tafel slopes of 77 mV dec-1 and 65 mV dec-1, respectively. The excellent electron-proton storage and transferability of SiW12 and P2W18 may provide a solution for the insufficient capture of H by Ag, leading to an effective self-optimizing behavior and superior acidic HER activity.
This paper achieves high-quality sharing of preschool educational resources among learners based on a matrix model. Through the shared resource matrix analysis method, the symmetry of the matrix about this educational resource is judged so that the resource can transmit information implicitly among all subjects. The difference between noise and preschool education resource data points is enhanced using the K-nearest neighbor search algorithm. Combined with the ant colony particle swarm optimization algorithm, the sharing sequence and time of the optimal preschool education resources are calculated, and a preschool education resource-sharing platform is constructed. To verify the applicability of the constructed platform, the simulation analysis results show that the resource search accuracy of the platform constructed in this paper reaches 94.51% on average, and the compatibility performance index is as high as 7.287vw, which is 3.25vw and 3.421vw higher compared to other platforms, respectively. The results show that the matrix model can eliminate resource heterogeneity and provide convenient and efficient interactive technical support for constructing a preschool education resource-sharing platform.
It is unclear whether doctoral nursing students are using nursing theory to guide their research. This descriptive, exploratory study involved a review of 747 doctoral papers to determine whether nursing students are using nursing or non-nursing theory to guide their research. The findings revealed that although 86.9% of doctoral students used theory, just 31.7% used nursing-specific theory to guide their dissertation study or capstone project. The disproportionate relationship between the use of nursing and non-nursing theory at the doctoral level poses both challenges and opportunities.
It is a generic paradigm to treat all samples equally in 3D object detection. Although some works focus on discriminating samples in the training process of object detectors, the issue of whether a sample detects its target GT (Ground Truth) during training process has never been studied. In this work, we first point out that discriminating the samples that detect their target GT and the samples that don’t detect their target GT is beneficial to improve the performance measured in terms of mAP (mean Average Precision). Then we propose a novel approach name as DW (Detected Weight). The proposed approach dynamically calculates and assigns different weights to detected and undetected samples, which suppresses the former and promotes the latter. The approach is simple, low-calculation and can be integrated with available weight approaches. Further, it can be applied to almost 3D detectors, even 2D detectors because it is nothing to do with network structures. We evaluate the proposed approach with six state-of-the-art 3D detectors on two datasets. The experiment results show that the proposed approach improves mAP significantly.
Purpose The purpose of this study was to explore how decision making and informal and incidental learning (IIL) emerged in the clinical learning environment (CLE) during the height of the Covid‐19 pandemic. The authors’ specific interest was to better understand the IIL that took place among frontline physicians who had to navigate a CLE replete with uncertainty and complexity with the future goal of creating experiences for medical students that would simulate IIL and use uncertainty as a catalyst for learning. Method Using a modified constructivist, grounded theory approach, we describe physicians’ IIL while working during times of heightened uncertainty. Using the critical incident technique, we conducted 45‐min virtual interviews with seven emergency department (ED) and five intensive care unit (ICU) physicians, who worked during the height of the pandemic. The authors transcribed and restoried each interview before applying inductive, comparative analysis to identify patterns, assertions, and organizing themes. Results Findings showed that the burden of decision making for physicians was influenced by the physical, emotional, relational, and situational context of the CLE. The themes that emerged for decision making and IIL were interdependent. Prominent among the patterns for decision making were ways to simplify the problem by applying prior knowledge, using pattern recognition, and cross‐checking with team members. Patterns for IIL emerged through trial and error, which included thoughtful experimentation, consulting alternative sources of information, accumulating knowledge, and “poking at the periphery” of clinical practice. Conclusions Complexity and uncertainty are rife in clinical practice and this study made visible decision‐making patterns and IIL approaches that can be built into formal curricula. Making implicit uncertainty explicit by recognizing it, naming it, and practicing navigating it may better prepare learners for the uncertainty posed by the clinical practice environment.
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3,065 members
Luc Paquette
  • Department of Human Development
George A Bonanno
  • Department of Counseling and Clinical Psychology
Nathan Ryan Holbert
  • Department of Mathematics, Science and Technology
Richard A. Magill
  • Department of Biobehavioural Sciences
New York City, United States