Real-world Knowledge Research Articles

School Leaders, Teachers, and Counselors’ Role in College and Career Readiness for First-Generation Students and Underserved Students by Promoting the Academy Modeling Concept

Based on a grounded body of research, too many first-year freshmen students demonstrate not being ready to assume the responsibility of being academically ready for college and careers after leaving high school. Some of the readiness descriptions for college for first-year students are attitudinal and essential skills needed to participate proficiently in basic coursework in English composition and mathematics during the first year in college. A number of first-generation students do not demonstrate having the best skills needed to succeed in college and how to navigate ways in securing resources such as financial aid and other needed resources to be successful in college their first year especially. Too many students drop out of college during their first year (Choy, Horn, Nunez and Chen, 2000; Jackson, 2009). Research continues to show that there are some common indicators to observe by identifying students being at risk of dropping out of college during their first year. Is it important for all students and first-generation students to be prepared for college starting at an early age? Leaders, teachers, counselors and parents must ensure that all students are taking rigorous coursework and provide career awareness knowledge and opportunities to set the stage for college readiness and success. Leaders working collaboratively, especially with counselors for career information and teachers of mathematics and English composition are providing students with real-world skills and knowledge that will be needed in college are among the best practices to support student success as they leave high school and enter college. Students’ attitudinal behaviors, peers, and their families must be encouraged and given support in helping students to get to and succeed in college. At the secondary and post-secondary sites, it is essential for students to have a mentor, advisor, responsive curriculum, and appropriate resources to gain a greater chance for college success. This study looks at a variety of data to help communicate the meaning of college readiness based on four schools in two different states, first-generation students’ academic ability, social life, socioeconomic status, appropriate resources, and curriculum. Second, this study captures the role of school leaders, teachers, counselors, and parents who can all promote college readiness for students. This study is significant because it adds to the body of literature best practices on how to promote strategies that will effectively support college readiness for first-generation students using the academy modeling concept.

Metaphor and Conflict /Métaphore et conflit ed. by Paola Paissa et al.

Reviewed by: Metaphor and Conflict /Métaphore et conflit ed. by Paola Paissa et al. A. Kate Miller Paissa, Paola, Michelangelo Conoscenti, Ruggero Druette, and Martin Solly, eds. Metaphor and Conflict /Métaphore et conflit. Peter Lang, 2021. ISBN 978-3-0343-4068-7. Pp. 386. The contributions to this edited volume, penned by scholars from universities in France, Italy, Switzerland, and the United Kingdom, are written in English and French, reflecting their origins in a bilingual 2018 workshop of the same name held in Turin as part of a larger Italian Progetti di ricerca di interesse nazionale [research project of national interest] on metaphors. The introduction to the volume (which is the only chapter that appears in both English and French versions) presents the notion underlying the contributions and reflected in the title: The use of metaphor creates a "conflict" in that the literal interpretation of the words used are at odds with real-world facts and knowledge (for example, as two separate chapters note, in Emily Brontë's verse "And winter pours its grief in snow," grief is not a substance that can be poured and winter is not an entity that can carry out the act of pouring). The introduction and the first few theoretically framed chapters (discussing syntax and textual coherence, metaphor and metalanguage, and the argumentative functions and vulnerabilities of metaphors) may present difficulties for nonspecialists. However, subsequent chapters are more accessible, presenting a series of interesting case studies on the use of metaphor in political and media discourse (section 2), and in pedagogical materials and translated literature (section 3). The six chapters that comprise section 2 focus on the discourse surrounding significant recent political events such as Brexit and its aftermath, the 2017 U.S. presidential inauguration, and the Gilets jaunes protests in France. These chapters examine the use of verbal, visual, and multimodal metaphor through various corpora that include social media posts, political speeches, press articles, or editorial cartoons. These case studies offer fascinating analyses with broad appeal to anyone interested in language, politics, social media interaction, current events, satire, and beyond. It should be noted, however, that a widespread interest captured by analyses related to current events will almost certainly be tempered by the passage of time as such events are relegated to the annals of history. Section 3 includes only two chapters, which examine the use of metaphor in, respectively, a video series illustrating basic economic principles (Dr. CAC, which aired on France 5 from 2005-2011) and an Italian translation of Shakespeare's The Two Gentlemen of Verona. Again, these case studies present analyses that would be of interest to a wider range of readers beyond just those working in linguistics. Thus, overall, contributions to these latter sections could easily be read as stand-alone articles; the purported underlying theoretical framework of metaphor and conflict seems somewhat opaque and not essential to benefitting from the analyses presented in many of the volume's contributions. [End Page 249] A. Kate Miller Indiana University-Purdue University, Indianapolis Copyright © 2023 American Association of Teachers of French

Hate Speech Patterns in Social Media: A Methodological Framework and Fat Stigma Investigation Incorporating Sentiment Analysis, Topic Modelling and Discourse Analysis

Social media offers users an online platform to freely express themselves; however, when users post opinionated and offensive comments that target certain individuals or communities, this could instigate animosity towards them. Widespread condemnation of obesity (fatness) has led to much fat stigmatizing content being posted online. A methodological framework that uses a novel mixed-method approach for unearthing hate speech patterns from large text-based corpora gathered from social media is proposed. We explain the use of computer-mediated quantitative methods comprising natural language processing techniques such as sentiment analysis, emotion analysis and topic modelling, along with qualitative discourse analysis. Next, we have applied the framework to a corpus of texts on gendered and weight-based data that have been extracted from Twitter and Reddit. This assisted in the detection of different emotions being expressed, the composition of word frequency patterns and the broader fat-based themes underpinning the hateful content posted online. The framework has provided a synthesis of quantitative and qualitative methods that draw on social science and data mining techniques to build real-world knowledge in hate speech detection. Current information systems research is limited in its use of mixed analytic approaches for studying hate speech in social media. Our study therefore contributes to future research by establishing a roadmap for conducting mixed-method analyses for better comprehension and understanding of hate speech patterns.

Dose titration of osmotic release oral system methylphenidate in children and adolescents with attention-deficit hyperactivity disorder: a retrospective cohort study

BackgroundOsmotic release oral system methylphenidate (OROS-MPH) is one of the most commonly used medication for attention-deficit hyperactivity disorder (ADHD), however, real-world knowledge on OROS-MPH dose titration has been limited. This study aims to summarize and visualise the OROS-MPH titration patterns in children and adolescents with ADHD in the United States (US) and Japan.MethodsThis retrospective cohort study used the US IBM® MarketScan® Commercial Claims and Encounters database from 2000 to 2019 and the Japan Medical Data Centre database from 2008 to 2019. New OROS-MPH users with ADHD were identified and split into child (6 to < 13 years) and adolescent (13 to < 18 years) groups according to age at OROS-MPH initiation/reinitiation. Patient characteristics and OROS-MPH treatment patterns were described. OROS-MPH dose titration pathways were visualised by Sankey diagrams.ResultsWe included 98,973 children and 62,002 adolescents in the US cohort, and 4595 children and 1508 adolescents in the Japanese cohort. In Japanese cohort, 91.9% of children and 77.9% of adolescents initiated OROS-MPH at the lowest dose (18 mg/day), whereas US patients had a broader distribution of initial doses (e.g., 18–54 mg/day). The US patients had higher daily dose of OROS-MPH than Japanese patients. Overall, a minority (< 40%) of the OROS-MPH users underwent dose titration, and different titration patterns were observed between the US and Japanese patients.ConclusionsDifferent treatment and titration patterns of OROS-MPH were observed in the two countries. Additional real-world studies about clinical reasoning underlying dose selection are needed to support clinical decision-making.

Knowledge graph extension with a pre-trained language model via unified learning method

Knowledge graphs (KGs) are collections of real-world knowledge that is represented by a structured form of triples. Since they are manually built in their nascent stage, there is a common problem that some links (triples) are missing. Knowledge graph completion (KGC) aims to find those missing links and thereby complete the KGs. However, as knowledge increases through diverse sources, new entities have explosively emerged and they are needed to be connected to existing KGs. Thus, open-world KGC is targeted on extending KGs to those new entities. Dealing with those new entities is challenging because they do not have any connection with entities in the existing KGs. One way to handle the new ones is to embed them with their textual descriptions with pre-trained word embeddings and score them in the graph-vector space with the existing typical KGC models. These models have resulted in meaningful results but there is still a lack of studies on utilizing the latest neural networks, such as pre-trained language models which are known to be better at capturing contexts than pre-trained word embeddings. This paper proposes a novel model that effectively connects new entities and existing KGs through a pre-trained language model. To effectively handle the problem, we utilize two learning methods; one is the classification method of the masked language model (MLM) that predicts a word among a huge vocabulary set with a given context, and the other is multi-task learning based on the Multi-Task for Deep Neural Networks (MT-DNN). Based on the methods, the model first generates an embedding of a new entity using its textual description and then uses the embedding to find one of the existing entities from a KG where the new entity can be connected. The experimental results on three benchmark datasets, DBPedia50k, FB15k-237-OWE, and FB20k, show that the proposed model improves performances by 9.2%p, 4.4%p, and 11.1%p, respectively, and achieves new state-of-the-art performance for all datasets.

Opinion Mining Using Multi-Dimensional Analysis

This review addresses emerging literature in the field of Opinion Mining with a particular emphasis on user-generated textual content. The focus is on the various tasks involved in Opinion Mining, which satisfies the comprehension of the essential criteria and methodologies that should be considered prior to embraking on the task. The paper provides an in-depth analysis of benchmarked datasets, widely-used feature sets, algorithms, techniques, open-source tools, challenges, real-world applications, and insights into various dimensions of Opinion Mining. The findings have both theoretical and practical implications, as it highlights the significance of Opinion Mining of textual content in comprehensive society. This review covers both technical and real-world knowledge and offers a comprehensive understanding of available open-source tools that are used in real-time.

An attempt to establish real-world databases of poly(ADP-ribose) polymerase inhibitors for advanced or recurrent epithelial ovarian cancer: the Japanese Gynecologic Oncology Group.

The development of new treatments for gynecological malignancies has been conducted mainly through collaborative international phase III trials led by the United States and Europe. The survival outcomes of many gynecological malignancies have greatly improved as a result. Recent large-scale genome-wide association studies have revealed that drug efficacy and adverse event profiles are not always uniform. Thus, it is important to validate new treatment options in each country to safely and efficiently provide newly developed treatment options to patients with gynecological malignancies. The Japanese Gynecologic Oncology Group (JGOG) is conducting 5 cohort studies (JGOG 3026, 3027, 3028, 3030, and 3031) to establish real-world data (RWD) of poly(ADP-ribose) polymerase (PARP) inhibitor use in patients with advanced or recurrent epithelial ovarian cancer. The RWD constructed will be used to provide newly developed PARP inhibitors for women with advanced or recurrent ovarian cancer in a safer and more efficient manner as well as to develop further treatment options. In 2022, The JGOG, Korean Gynecologic Oncology Group, Chinese Gynecologic Cancer Society, and Taiwanese Gynecologic Oncology Group established the East Asian Gynecologic Oncology Trial Group to collaborate with East Asian countries in clinical research on gynecologic malignancies and disseminate new knowledge on gynecologic malignancies from Asia. The JGOG will conduct a collaborative integrated analysis of the RWD generated from Asian countries and disseminate real-world clinical knowledge regarding new treatment options that have been clinically implemented.

Refocusing Civic Education: Developing the Skills Young People Need to Engage in Democracy

Schools have traditionally taken a “just-the-facts-ma’am” approach to civic education, focusing on governmental structures and political systems. We argue that preparing young people to engage with democracy requires far more than rote memorization of facts and figures. Schools should be laboratories of democracy, where young people’s civic intentions are converted into civic behaviors. We argue that to realize that transformation, educators must impart real-world knowledge, practical skills, and nurturing abilities that are not captured by standardized tests of academic achievement: namely, the interpersonal and intrapersonal abilities conducive to civic mindedness. We discuss what these oft-labeled “noncognitive” skills are and how they are measured, review the evidence that shows how they foster democratic participation, articulate a vision for how civics can help develop students’ noncognitive skills, and lay out a research agenda for scholars seeking to teach young people the skills requisite to actively participate in democracy.

The Challenges of Transition to Practice Expressed Through the Lived Experience of New-to-Practice Nurses

Background: The nurse’s transition into practice is challenging. The need to facilitate a safe transition into practice and retain nurses is crucial in today’s healthcare. Objective and Design: The qualitative study aim was to describe the lived experiences of new nurses’ safe transition into practice and their perceptions of functioning as safe practitioners. Method: This descriptive, phenomenological arm of a larger, mixed methods study (Safety Transition Education to Practice study) interviewed 11 nurses with varied clinical backgrounds regarding their first six months as licensed nurses. Semistructured, one-to-one interviews with nurses representing geographical locations across the nation were conducted. Colaizzi’s method of analysis was used to extract themes. Results: The data revealed five themes with subthemes. The most universal theme was lack of practical knowledge (skills dexterity, real-world knowledge), followed by impostor syndrome, safety culture (unsafe environment, lack of supplies, lack of mentoring/management), internalized fear, and seeking the sage. Conclusion: The experiences support the understanding that transition to practice is overwhelming and uncertain. Academic faculty and practice nurses’ cooperative efforts can aid novice nurses in safe transition to practice through academic curriculum enhancement, preceptors, and nurse residency programs working toward narrowing the academic practice gap.

L1-Korean speakers’ definiteness processing in L2 English: A visual world paradigm eye tracking study

AbstractThis study investigated whether L1-Korean L2-English speakers could use (in)definiteness to predict unmentioned (non-)unique referents. In previous studies on the topic, methodological considerations made it difficult to separate the effects of grammar from the effects of non-linguistic knowledge. We used a visual world paradigm eye-tracking task to resolve such issues and designed stimuli that focused only on (in)definiteness. Participants’ eye movements were recorded as they heard “Click on the/a blue circle.” L1 and advanced L2 speakers used definiteness information to predict unique referents before the critical noun (circle) was heard while indefinite articles were not utilized to the same extent. Intermediate L2 speakers relied heavily on color words, not articles, to locate a referent. The results imply that predicting a referent solely based on definiteness (without real-world knowledge) requires substantial advancement in L2 development, and indefinite articles do not predict non-unique referents as clearly as definite articles predict unique referents.

Unsupervised real-world knowledge extraction via disentangled variational autoencoders for photon diagnostics

We present real-world data processing on measured electron time-of-flight data via neural networks. Specifically, the use of disentangled variational autoencoders on data from a diagnostic instrument for online wavelength monitoring at the free electron laser FLASH in Hamburg. Without a-priori knowledge the network is able to find representations of single-shot FEL spectra, which have a low signal-to-noise ratio. This reveals, in a directly human-interpretable way, crucial information about the photon properties. The central photon energy and the intensity as well as very detector-specific features are identified. The network is also capable of data cleaning, i.e. denoising, as well as the removal of artefacts. In the reconstruction, this allows for identification of signatures with very low intensity which are hardly recognisable in the raw data. In this particular case, the network enhances the quality of the diagnostic analysis at FLASH. However, this unsupervised method also has the potential to improve the analysis of other similar types of spectroscopy data.

A study on impact of conceptual and practical based learning on employability

The impact of practical learning on employability is significant. There is no age, time or effort limit to learn because it is what as a person important to survive and thrive in this world. Although learning through theory is one side of the mirror, leveraging the knowledge in real world is what matters most. As employability is the primary goal, the process of learning leading up to that is what supports the goal. To assist the above statements this study refers the teaching strategies and learning models states that learning and employability are correlated for both learners and their peers. Practical-based learning should be implemented in the current scenario. It must be started from the preparatory level to make it a habit. The main aim of education is “learning” which must not miss out from the education system as learning has a direct impact on employability. The study reveals that a variety of techniques can be implemented by peers and the participation of students in the process could be a productive way to collaborate, hence, this kind of engagement would be a superior course of action for students to learn practically and can increase the scope of employability. The relevant articles' conclusions were discussed for better understanding of perceptions, multiple learning models and their importance, and significance are acknowledged to comprehend the impact of conceptual learning on Employability. The methods such as Practical Based Learning provide students’ exposure to real-life problems that they can solve by adopting the learning methods.

Identifying opportunities for hepatic encephalopathy self-management: A mixed methods systematic review and synthesis.

Hepatic encephalopathy (HE) in cirrhosis is an extremely challenging complication for patients and care partners. To identify potentially modifiable factors to enhance HE self-management strategies, we conducted a synthesis of quantitative and qualitative research about real-world HE behaviours, knowledge, and experiences. Using the EPPI-Centre's mixed methods synthesis procedure, a systematic literature search in five databases was completed; methods of selected articles underwent critical appraisal followed by descriptive analysis and coded line-by-line of content. Through refutational translation, the findings from the quantitative and qualitative syntheses were juxtaposed to highlight congruencies, incongruencies, or gaps. These findings informed generation of cross-analytical themes that were transformed into action statements. The quantitative narrative review of synthesis (n = 17) generated four themes (patients had low awareness of HE and low treatment adherence rates, physicians had a non-uniform approach to non-pharmaceutical therapies). Meta-aggregation of qualitative data from six articles yielded three themes (patients and care partners had low levels of HE awareness, were unfamiliar with HE self-management, and were adherent to treatments). Comparison of findings revealed three congruencies, two gaps, and one incongruency. The combined synthesis yielded two self-management themes: universal patient-oriented cirrhosis HE education and ensuring each health care encounter systematically addresses HE to guarantee health care is continuously modified to meet their needs. By drawing on elements of Bloom's Taxonomy and distributed knowledge networks, deliberate patient-oriented HE messaging at all health care encounters is greatly needed to improve health outcomes and reduce care burdens related to HE.

Learning neighborhood-based embedding sequence for link prediction in temporal knowledge graphs

Many real-world knowledge graphs are complex and keep evolving over time. Inferring missing facts in temporal knowledge graphs is a fundamental and challenging task. Previous studies focus on link prediction in static knowledge graphs which hardly extracts the temporal features effectively. In this paper, we propose a novel deep learning model, namely KBGAT-BiLSTM, which is capable of solving long-term predict problems and is suitable for temporal knowledge graph with complex structures. First, we adapt the Graph Attention Network (GAT) to learn the structural features of knowledge graph. Then we utilize the Bidirectional Long Short-Term Memory Networks (BiLSTM) to learn the temporal features and obtain the low-dimensional embeddings of entities and relations. Finally, we employ a scoring function for link prediction in temporal knowledge graphs. Through extensive experiments on YAGO, WIKI, and ICEWS18 datasets, we demonstrate the effectiveness of our model, compare the performance of our model with several different state-of-the-art methods and further analyze the properties of the proposed method.

41 Content Analysis of the Recommendations from Project Echo Ontario Autism

Abstract Background The rising prevalence of autism spectrum disorder (ASD) diagnoses has caused an increased number of community practitioners to care for this population. However, community practitioners report a lack of knowledge and confidence in treating these patients, resulting in unmet healthcare needs. The Extension of Community Healthcare Outcomes (ECHO) Autism model aims to address this through case-based and didactic learning to help guide community practitioners in providing comprehensive, best-practice care for ASD screening, diagnosis, and management of co-occurring conditions. Each ECHO session involves a case presentation followed by a list of recommendations generated by community participants and an interdisciplinary ‘hub’ team. While ECHO Autism has been shown to improve physicians' abilities to care for children with ASD in their practices, recommendations stemming from ECHO cases have yet to be characterized and may help guide future care. Objectives To quantify and characterize the common categories within ECHO Autism Ontario case recommendations. Design/Methods A content analysis of 422 recommendations from 61 ECHO cases was conducted to identify categories of recommendations and their frequencies. Three researchers independently coded recommendations from five ECHO cases, from which an original coding guide was developed. The researchers then independently coded the remaining cases and met regularly with the ECHO lead to modify and consolidate the codes and coding guide. From there, categories and sub-categories from the various codes were identified. Finally, the frequencies of each code and category were calculated. Results Fifty-seven codes were included in the final coding guide and grouped into eight broad categories. Categories included: 1) diagnosis; 2) concurrent mental and physical health conditions; 3) referrals to allied health providers and other specialists; 4) accessing community resources, such as parent and sibling support groups; 5) providing education and guidance to physicians, patients, and families; 6) management strategies such as nutrition, physical activity, and social skills; and 7) patient and family-centered care. A COVID-19 category was added, as many of the later recommendations were adapted to online service delivery. An analysis of the frequency of codes found that 1,384 total in-text codes were distributed amongst the various categories. The three highest frequencies of categories were providing general guidance and education (22%), accessing resources (16%), and referrals (15%). Conclusion This is the first time recommendations from ECHO Autism have been characterized and quantified. Our results, particularly the most common category of providing general guidance and education about ASD, show there is still important work to be done with educating clinicians and families about aspects of ASD. Furthermore, findings from this study should inform Pediatrics residency programs about real-world knowledge gaps in ASD care, and may help create more tailored ASD training programs and educational materials.

Automated conversion of engineering rules: Towards flexible manufacturing collaboration

Rapid on-demand manufacturing resource sharing within and between factories are critical to achieving responsive autonomous manufacturing collaborations towards mass personalization. To this end, cloud manufacturing technologies allow resource owners/service providers to virtualize and encapsulate their resources as services accessible over the Internet. Decision-making in cloud manufacturing needs to utilize real-world engineering knowledge from different parties. Many existing systems have adopted the semantic web-based decision-making framework, in which engineering knowledge is modeled using structured syntax. However, manually converting engineering rules to semantic rules is time-consuming and error prone. This research proposes a machine learning model, based on the Transformer model, that uses neural machine translation techniques to convert engineering knowledge expressed in natural language to structured semantic rules directly. The model is implemented using neural network. The model is first trained using typical sentences that are used for describing engineering knowledge. From these sample sentences, the model learns the patterns and the meaning of the sentences. This allows the model to identify the service providers, resource users, and the resources described in the sentences. As a result, the corresponding semantic rules can be constructed. Compared with previous approaches, the proposed scheme not only improves the conversion accuracy but also reduces the amount of required human interaction, simplifying the system and its use.

This Better Be Interesting: A Speaker's Decision to Speak Cues Listeners to Expect Informative Content.

In anticipating upcoming content, comprehenders are known to rely on real-world knowledge. This knowledge can be deployed directly in favor of upcoming content about typical situations (implying a transparent mapping between the world and what speakers say about the world). Such knowledge can also be used to estimate the likelihood of speech, whereby atypical situations are the ones newsworthy enough to merit reporting (i.e., a nontransparent mapping in which improbable situations yield likely utterances). We report four forced-choice studies (three preregistered) testing this distinction between situation knowledge and speech production likelihood. Comprehenders are shown to anticipate situation-atypical meanings more when guessing content (a) that a speaker announces (rather than thinks), (b) that is said out of the blue (rather than produced when prompted), and (c) that is addressed to a large audience (rather than a single listener). The findings contrast with prior work that emphasizes a comprehension bias in favor of typicality, and they highlight the need for comprehension models that incorporate expectations for informativity (as one of a set of inferred speaker goals) alongside expectations for content plausibility.

Representation learning of knowledge graphs with the interaction between entity types and relations

Representation learning of knowledge graphs is the task of representing entities and relations of real-world knowledge as low-dimensional vectors. Some previous works have demonstrated that entity types are beneficial for knowledge representation, but these methods rely too much on prior knowledge or do not have pluggability. To address these limitations, we propose a new method named TRPE which can better explore the entity type information for knowledge graph embedding. We suggest that entities should have multiple representations and each entity type should play a different role in different relations. To better characterize the interaction between entity types and relations, we build the type-relation pairs for each entity and gain each type-relation pair feature through the interaction between type-specific features and relation-specific features. These type-relation pair features are assigned different weights which are aggregated to serve as the transformation of entity embeddings. After that, we divide each entity embedding into some sub-embeddings where each sub-embedding does its own transformation. At the same time, our model is a pluggable module that can be attached to other models. We integrate our model into three baseline models for evaluation on FB15k and FB15k + datasets. The experiment results show that our model brings significant improvement to baseline models. On CoDEx datasets with more noisy entity type data, our model still achieves good results compared with other models, which also demonstrates the generalization ability of our model.

Analysis Based on Social Media using Multi Modal Algorithm

Propose system tend to concentrate on analysis sentiment of text from social media, aim of system is to find whether piece of text is positive or negative. The goal of Sentiment Analysis is to harness this data in order to obtain important information regarding public opinion that would help make smarter business decisions, political campaigns and better product consumption. Sentiment Analysis focuses on identifying whether a given piece of text is subjective or objective and if it is subjective, then whether it is negative or positive. Sentiment analysis deals with the computational treatment of opinion, sentiment, and subjectivity of texts. Moreover, we tend to conjointly develop economical illation methodology for parameter estimation of sup- ported folded Gibbs sampling. We tend to judge SJASM extensively on real-world review knowledge, and experimental results demonstrate that the planned model outperforms seven well-established base- line strategies for sentiment analysis tasks.

Upholding Multilingual Learners’ Civil Rights under ESSA: State Education Agency Leaders and the Contextual Factors Shaping Their Work

Purpose: Alongside the inequities surfaced by COVID-19, a confluence of demographic and policy-related shifts over the last few decades has elevated the education of multilingual learners (MLs) in state education agency (SEA) leaders’ work. This study employs an ecological perspective to examine how SEA leaders promote social justice policy implementation in their efforts to uphold MLs’ civil rights under the Every Student Succeeds Act (ESSA). Research Methods: Given its embeddedness in a research-practice partnership (RPP) that emphasizes integration of researchers’ expertise with SEA leaders’ real-world knowledge, the study employs a participatory research approach. Drawing on 23 semistructured interviews with SEA leaders and 20 hours of observations during RPP meetings, the authors engaged in an iterative analysis process involving coding, memoing, and extensive member checking. Findings: Findings show the dual roles that leaders played to uphold the spirit of civil rights law across local education agencies (LEAs) and within SEAs, and they highlight the structural, cultural, and political dynamics that operated to enable or constrain this work. The study also reveals that cross-state collaboration may be a powerful mechanism for upholding ML civil rights in ways that go beyond compliance toward transformation and social justice. Implications: Although ESSA expanded the role of the SEA in policy implementation, little attention has been given to supporting state-level organizational development. Our findings suggest the need to allocate resources to support SEA capacity development. Such support is ever more critical as state systems work to develop coherent and aligned supports in response to COVID-19-related disparities.