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This qualitative case study explores the leadership strategies of three high school principals to promote equity and inclusivity amid multifaceted challenges in the diverse schooling contexts of Nepal. By shedding light on equitable school leadership practices within a complex web of long-held socio-economic and structural disparities, the findings reveal persistent educational inequalities stemming from caste discrimination, gender biases, economic gaps, and social prejudices. Leadership emerges as crucial in addressing these disparities, with empowering strategies showing promise in bridging educational divides. Policymakers, educators, and leaders can benefit from these insights in fostering equitable educational environments. As Nepal addresses historical inequities, the study advocates for systemic change and social justice in education, aiming to create a more inclusive future for Nepali students. [Note: The page range (268-294) shown on the PDF is incorrect. The correct page range is 268-307.]

Multimodal learning analytics (MMLA) research has shown the feasibility of building automated models of collaboration quality using artificial intelligence (AI) techniques (e.g., supervised machine learning (ML)), thus enabling the development of monitoring and guiding tools for computer-supported collaborative learning (CSCL). However, the practical applicability and performance of these automated models in authentic settings remains largely an under-researched area. In such settings, the quality of data features or attributes is often affected by noise, which is referred to as attribute noise. This paper undertakes a systematic exploration of the impact of attribute noise on the performance of different collaboration-quality estimation models. Moreover, we also perform a comparative analysis of different ML algorithms in terms of their capability of dealing with attribute noise. We employ four ML algorithms that have often been used for collaboration-quality estimation tasks due to their high performance: random forest, naive Bayes, decision tree, and AdaBoost. Our results show that random forest and decision tree outperformed other algorithms for collaboration-quality estimation tasks in the presence of attribute noise. The study contributes to the MMLA (and learning analytics (LA) in general) and CSCL fields by illustrating how attribute noise impacts collaboration-quality model performance and which ML algorithms seem to be more robust to noise and thus more likely to perform well in authentic settings. Our research outcomes offer guidance to fellow researchers and developers of (MM)LA systems employing AI techniques with multimodal data to model collaboration-related constructs in authentic classroom settings.

Medical school admissions is a contentious and high stakes selection activity. Many assessment approaches are available to support selection; but how are decisions about building, monitoring, and adapting admissions systems made? What shapes the processes and practices that underpin selection decisions? We explore how these decisions are made across several Canadian medical schools, and how values shape the creation, monitoring, and adaptation of admissions systems. Using phenomenography (a qualitative method suited to examining variability), the authors analyzed interviews with 10 current or previous heads of admissions from 10 different undergraduate medical education programs in Canada. Interviews were conducted in English and French, and data was collected between 2016 and 2017 (therefore participants no longer hold these roles). Data was coded and analyzed iteratively, focusing on identifying underlying values, and exploring how these values shape admissions practices and considerations for validity. Eight different intersecting values were identified. Of these, four were shared across all participants: critically questioning the process and tools, aiming for equity, striving for better, and embracing the challenges of change. The expression of these values depended on different contextual variables (e.g., geographic location, access to expertise, resource availability), and values shaped how admissions systems were built, enacted, and monitored for quality. Ultimately, values shaped: (1) admissions practices resulting in different candidates being offered admission, and (2) how arguments supporting score interpretation are built (i.e., validity). This study documents various values that influence admissions processes, practices, and quality monitoring. The values that shape what is assessed, how it is assessed, and how fair and defensible practices are conceptualized have significant impact, ultimately determining who is selected. These values--whether implicit or explicit--result in intended and unintended consequences in selection processes. However, these values are rarely explicitly examined and questioned, leaving it uncertain as to which consequences are the intended outcomes of deliberately chosen values, and which are unintended consequences of implicitly held values of admissions systems and their actors.

Background: India has a significant prevalence of people with intellectual disabilities. Despite their higher prevalence, they receive poor support. Therefore, this review aims to explore the experiences of family carers in providing care for children with intellectual disabilities in India. Methods: A qualitative evidence synthesis was undertaken, searching databases such as MEDLINE, CINAHL, Web of Science, and PsycInfo up to October 2023. Grey literature was also searched for unpublished studies, with two reviewers assessing methodological quality. Eleven eligible studies, mostly qualitative in design, were included in the review. The data synthesis followed a thematic approach. Results: The synthesis found five themes representing family carers' experiences and perspectives. These were 'resilience and acceptance', 'parental response', 'care dynamic', 'preparing for transition to adulthood' and 'parental advocacy'. Conclusion: Family carers hold diverse views, while almost all consider providing care complex and challenging, with few positive experiences.

The advent of the digital age represents a transformative era in which technology, primarily social media platforms, has become an integral part of the daily lives of individuals worldwide. Students are the most prolific users of social media, utilizing these platforms for a variety of purposes, including communication, information sharing, entertainment, and social networking. This study evaluated the connection between student social media use and academic performance through family bonding and collaborative learning. This research also explores how mental wellbeing moderates the link between students' family bonding, collaborative learning, and academic performance. This research article analyzes a sample of 330 university students from the public and private sectors and tests the proposed hypothesized relationships. The study used the Partial Least Squares Structural Equation Modelling (PLS-SEM) methodological approach for evaluating proposed parameters. The findings indicated that social media use positively correlated with students' academic performance. Second, family bonding and collaborative learning significantly moderated the association between students' academic performance and social media use. Finally, mental well-being significantly moderated the connection between students' collaborative learning, family bonding, and academic performance. This study's findings contribute to the knowledge of global education with valuable insights into students' psychological well-being and academic performance. In theory, the current research advances the scientific understanding of education by assessing social media usage's effects on students' academic performance and psychological well-being.

Context.-The Nottingham Grading System (NGS) developed by Elston and Ellis is used to grade invasive breast cancer (IBC). Glandular (acinar)/tubule formation is a component of NGS. Objective.-To investigate the ability of pathologists to identify individual structures that should be classified as glandular (acinar)/tubule formation. Design.-A total of 58 hematoxylin-eosin photographic images of IBC with 1 structure circled were classified as tubules (41 cases) or nontubules (17 cases) by Professor Ellis. Images were sent as a PowerPoint (Microsoft) file to breast pathologists, who were provided with the World Health Organization definition of a tubule and asked to determine if a circled structure represented a tubule. Results.-Among 35 pathologists, the [kappa] statistic for assessing agreement in evaluating the 58 images was 0.324 (95% CI, 0.314-0.335). The median concordance rate between a participating pathologist and Professor Ellis was 94.1% for evaluating 17 nontubule cases and 53.7% for 41 tubule cases. A total of 41% of the tubule cases were classified correctly by less than 50% of pathologists. Structures classified as tubules by Professor Ellis but often not recognized as tubules by pathologists included glands with complex architecture, mucinous carcinoma, and the "inverted tubule" pattern of micropapillary carcinoma. A total of 80% of participants reported that they did not have clarity on what represented a tubule. Conclusions.-We identified structures that should be included as tubules but that were not readily identified by pathologists. Greater concordance for identification of tubules might be obtained by providing more detailed images and descriptions of the types of structures included as tubules. (Arch Pathol Lab Med. 2024;148:1119-1125; doi: 10.5858/arpa.2023-0163-OA), Histologic grading of invasive breast carcinoma (IBC) represents a morphologic assessment of the degree of tumor differentiation and is routinely reported in the pathologic assessment of IBC. Histologic grade is [...]