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The purpose of this study was to examine the relationship between early childhood teacher candidates' empathic tendencies and social problem-solving skills. A total of 187 teacher candidates studying in the department of Early Childhood Education at Duzce University participated in this study. The relational research model, a quantitative research method, was used. The data of this study were collected through a personal information form, the Empathic Tendency Scale, and the Social Problem-Solving Inventory (SPÇE-SF). The researchers analyzed the data using Pearson Correlation and one-way ANOVA with the SPSS program. According to the findings of the study, the empathic tendency levels and social problem-solving skills of the early childhood teacher candidates participating in the research are at moderate levels. In addition, there was a moderate positive relationship between participants' empathic tendencies and social problem-solving skills. Finally, it is seen that the empathic tendencies of the participants do not differ according to personal data such as gender, educational level, mother's and father's educational level, family type, family attitude, number of siblings, and birth order. The participants' social problems-solving skills differed only by gender and number of siblings.

The study investigates EFL (English as a Foreign Language) learners' opinions, attitudes and the challenges of incorporating AI-powered teaching and learning. It also examines how their ideas and attitudes are affected by demographic variables. 258 students were selected using a random sampling method from a population comprising students studying in different levels of programs at the College of Science and College of Business Administration, Prince Sattam bin Abdul-Aziz University, KSA. A questionnaire was self-developed using some modified items from prior studies as the study looks at how certain independent variables (e.g., study level, residential background and parents' educational level) affect the dependent variable (e.g., learners' opinions, attitudes and challenges for AI-powered learning and teaching). The quantitative approach (descriptive quantitative design) revealed that Saudi EFL students held a high level of positive opinions and attitudes towards AI-powered learning. However, the analysis found that many students thought implementing AI-powered learning was challenging. A one-way ANOVA showed no significant difference based on respondents' residential background and parental education. However, respondents differed significantly based on their level or year of study. The study findings will assist administrators and course teachers in using AI-powered technologies to overcome challenges and prepare students for achievement in the English language.

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The acquisition of multimodal magnetic resonance-based brain development data is central to the studys core protocol. However, application of Magnetic Resonance Imaging (MRI) methods in this population is complicated by technical challenges and difficulties of imaging in early life. Overcoming these challenges requires an innovative and harmonized approach, combining age-appropriate acquisition protocols together with specialized pediatric neuroimaging strategies. The HBCD MRI Working Group aimed to establish a core acquisition protocol for all 27 HBCD Study recruitment sites to measure brain structure, function, microstructure, and metabolites. Acquisition parameters of individual modalities have been matched across MRI scanner platforms for harmonized acquisitions and state-of-the-art technologies are employed to enable faster and motion-robust imaging. Here, we provide an overview of the HBCD MRI protocol, including decisions of individual modalities and preliminary data. The result will be an unparalleled resource for examining early neurodevelopment which enables the larger scientific community to assess normative trajectories from birth through childhood and to examine the genetic, biological, and environmental factors that help shape the developing brain.

A significant number of individuals with tuberous sclerosis complex (TSC) exhibit language difficulties. Here, we examined the language-related brain morphometry in 59 participants (7 participants with TSC and comorbid autism spectrum disorder (ASD) (TSC + ASD), 13 with TSC but no ASD (TSC-ASD), 10 with ASD-only (ASD), and 29 typically developing (TD) controls). A hemispheric asymmetry was noted in surface area and gray matter volume of several cortical language areas in TD, ASD, and TSC-ASD groups, but not in TSC + ASD group. TSC + ASD group demonstrated increased cortical thickness and curvature values in multiple language regions for both hemispheres, compared to other groups. After controlling for tuber load in the TSC groups, within-group differences stayed the same but the differences between TSC-ASD and TSC + ASD were no longer statistically significant. These preliminary findings suggest that comorbid ASD in TSC as well as tuber load in TSC is associated with changes in the morphometry of language regions. Future studies with larger sample sizes will be needed to confirm these findings.

This paper discusses the creation and implementation of an experiential learning assignment focused on the United Nation's Sustainable Development Goal (SDG) 12, which aims to ensure sustainable consumption and production patterns. Using a grounded theory approach that combines analyzing 90 senior-level marketing students' reflective essays alongside 63 pre- and post-assignment survey responses, we develop the "RESCUER" framework which combines active and passive learning elements. We demonstrate how active learning layered on top of passive methods can be an effective means to generate more responsible consumer behaviors within a complex food supply system. Students begin with passive learning components in the form of readings and lectures (labeled Resources), before Engaging with mindfulness in an active learning activity that involves the selection, purchase, and preparation of perishable food for a salad. The framework also includes the important effects of Social influence and its role in how Cognizance and Underlying problem salience are generated. Finally included are factors that Expedite the process of generating cognizance and problem salience such as the ready availability of relevant facilities (e.g., the existence of a garbage sorting system), which can enable more Responsible consumer behaviors.

Deep learning-based mammographic evaluations could noninvasively assess response to breast cancer chemoprevention. We evaluated change in a convolutional neural network-based breast cancer risk model applied to mammograms among women enrolled in SWOG S0812, which randomly assigned 208 premenopausal high-risk women to receive oral vitamin D3 20 000 IU weekly or placebo for 12 months. We applied the convolutional neural network model to mammograms collected at baseline (n = 109), 12 months (n = 97), and 24 months (n = 67) and compared changes in convolutional neural network-based risk score between treatment groups. Change in convolutional neural network-based risk score was not statistically significantly different between vitamin D and placebo groups at 12 months (0.005 vs 0.002, P = .875) or at 24 months (0.020 vs 0.001, P = .563). The findings are consistent with the primary analysis of S0812, which did not demonstrate statistically significant changes in mammographic density with vitamin D supplementation compared with placebo. There is an ongoing need to evaluate biomarkers of response to novel breast cancer chemopreventive agents.

Purpose: The effect of e-learning success relies on the learning management system and its effectiveness provided to the learners. As a result, higher education institutions (HEIs) are expanding using various e-learning platforms and focusing on system and information quality. This study adopts the ISS (information system success) model to assess students' perception of e-learning system success (e-LSS). Design/methodology/approach: A quantitative research approach was used to analyse 151 students' perceptions collected from HEIs in Oman. The survey instrument was built on prior research related to DeLone and McLean's ISS model, and expert opinion was involved for validation. The snowball sampling method was used to collect the data, and participants' anonymity and confidentiality were maintained as part of the ethical process. The reliability of data was tested using Cronbach's alpha analysis. A statistical tool like correlation was used to examine the relationship between the study variables (system quality, information quality, user satisfaction and e-LSS). Findings: This study's results revealed that students positively perceived system usage, and users' satisfaction with e-learning systems (e-LSs) was high. Moreover, the correlation results indicated that the system and information quality aspects of e-learning significantly influence e-LSS. Practical implications: The study results on students' perspective towards e-learning information systems can be advantageous to HEIs and various stakeholders like policymakers, and e-learning platforms. It may support and assist the HEIs and corporate firms in deciding on e-learning platforms for students and learners, respectively. Moreover, the consolidated findings will contribute to the existing literature on e-learning success factors from students' perspectives. Originality/value: This study examines the students' perception of e-LSS in Oman HEIs and advocates prospects for further in-depth study and analysis.

Infographics are visual storytelling techniques used to communicate complex information. However, infographics can be misleading if they are not created ethically. When universities teach how to create infographics, they often do so without emphasizing the ethical issues underlying infographics. To address this gap, we designed a study to educate statistics and data science students about the ethics of infographics by using Rest model's three stages: awareness, orientation, and intention. Students' awareness of the ethical issues underlying infographics was captured before and after sensitizing them to five ethical principles derived from the AI4People's framework applied to a data science context. The students were then exposed to scenarios with ethical dilemmas. Their identification of the ethical principles in these scenarios was measured. The results showed a significant increase in students' awareness of the ethical issues underpinning the interpretation of infographics, suggesting that ethical training of current users and future designers would be beneficial.