Standard Vocabulary Research Articles

The Effects of a Science Vocabulary Intervention on Children’s Oral Language Development

This study investigated the effects of a shared reading and guided play vocabulary intervention on the science vocabulary acquisition of children who were at risk for oral language difficulties. Fifty-five children from five preschool programs participated in a 15-day science vocabulary intervention. Children randomly assigned to the treatment group (n = 29) received explicit science vocabulary instruction through shared readings of informational science books and follow-up scientific investigations. Children in the business-as-usual group (n = 26) received typical instruction from their classroom teacher. Vocabulary knowledge was measured at pre- and posttest using two standardized vocabulary assessments and one researcher-developed science vocabulary test. Results of the researcher-developed test showed that the treatment group significantly outperformed the BAU group on explicitly taught science vocabulary words (p < 0.001). No significant differences were found between the two groups on measures of general vocabulary knowledge. Limitations and practical implications of this study are discussed.

A Blueberry (Vaccinium L.) Crop Ontology to Enable Standardized Phenotyping for Blueberry Breeding and Research

Breeding programs around the world continually collect data on large numbers of individuals. To be able to combine data collected across regions, years, and experiments, research communities develop standard operating procedures for data collection and measurement. One such method is a crop ontology, or a standardized vocabulary for collecting data on commonly measured traits. The ontology is also computer readable to facilitate the use of data management systems such as databases. Blueberry breeders and researchers across the United States have come together to develop the first standardized crop ontology in blueberry (Vaccinium spp.). We provide an overview and report on the construction of the first blueberry crop ontology and the 178 traits and methods included within. Researchers of Vaccinium species—such as other blueberry species, cranberry, lingonberry, and bilberry—can use the described crop ontology to collect phenotypic data of greater quality and consistency, interoperability, and computer readability. Crop ontologies, as a shared data language, benefit the entire worldwide research community by enabling collaborative meta-analyses that can be used with genomic data for quantitative trait loci, genome-wide association studies, and genomic selection analysis.

Mapping UK Pain Datasets to the OMOP Common Data Model: Lessons Learned

ObjectiveChronic Pain is defined as pain lasting for three months or longer and a report by the British Pain Society in 2016 found that 28 million adults live with this condition. Chronic pain research is problematic as it is poorly represented in health data and often in silos. Alleviate is the Advanced Pain Discovery Platform (APDP) Data Hub which is removing barriers to access to data. ApproachThe aim for Alleviate is to transform pain data sets within the UK to be Findable, Accessible, Interoperable and Reusable (FAIR) and be more discoverable for researchers via Health Data Research UK’s Cohort Discovery Tool. The tool enables identification of potential cohorts in real-time from datasets Alleviate provides. The onboarding requires datasets to be transformed to the open and widely used Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). ResultsWe collaboratively developed CaRROT tools and used our mapping expertise to transform datasets to OMOP-CDM. The tools have improved efficient mapping of clinical and research data, covering ~10 million individuals’ records. Subsequently we learned valuable lessons on how to accurately and consistently map across data sets to support federated discovery. We also identified a lack of standard vocabulary representations for pain specific data. ConclusionsMapping data to OMOP is not straightforward. Attention to detail is required to ensure that consistent mapping decisions are made to maximise data reusability and interoperability. ImplicationsIn sharing the lessons learned of mapping datasets to OMOP we aim to help improve interoperability of disparate data sources.

Initiating a One Digital Health Unified Terminology (ODH-UT) to Facilitate Community Expansion.

One Digital Health (ODH) merges the Digital Health and One Health approaches to create a comprehensive framework for future health ecosystems. In this rapidly evolving field, a standardized vocabulary is not just a convenience, but a necessity to ensure efficient communication. This research proposes the development of a "One Digital Health-Unified Terminology" (ODH-UT) to facilitate communication among researchers and practitioners in Digital Health and One Health, addressing this crucial need.

Classifier Chains for LOINC Transcoding.

Mapping clinical observations and medical test results into the standardized vocabulary LOINC is a prerequisite for exchanging clinical data between health information systems and ensuring efficient interoperability. We present a comparison of three approaches for LOINC transcoding applied to French data collected from real-world settings. These approaches include both a state-of-the-art language model approach and a classifier chains approach. Our study demonstrates that we successfully improve the performance of the baselines using the classifier chains approach and compete effectively with state-of-the-art language models. Our approach proves to be efficient, cost-effective despite reproducibility challenges and potential for future optimizations and dataset testing.

Kemampuan Membedakan Antara Kosakata Baku Dengan Kosakata Tidak Baku Dalam Karangan Bahasa Indonesia Siswa Kelas V SD Negeri 59 Noling Kabupaten Luwu

The study aims to describe the level of students' ability to distinguish between standard and non-standard vocabulary in the Indonesian language. The population in this study was a student of V grade SD State 59 Noling who totalled 35 people. Data collection is done through test techniques, while data analysis is done using percentage techniques. This is evident from the data analysis that showed that 77.1% of the students of the sample obtained a score of 6.5 above, whereas the students obtaining a score below 6.5 was 22.65%. On this basis, it is expected that the learning process in the classroom, the teacher intensively gives tasks or training to the students in particular in terms of the difference between standard and non-standard vocabulary so that in the end students can establish the distinction between the two.

The biomedical relationship corpus of the BioRED track at the BioCreative VIII challenge and workshop.

The automatic recognition of biomedical relationships is an important step in the semantic understanding of the information contained in the unstructured text of the published literature. The BioRED track at BioCreative VIII aimed to foster the development of such methods by providing the participants the BioRED-BC8 corpus, a collection of 1000 PubMed documents manually curated for diseases, gene/proteins, chemicals, cell lines, gene variants, and species, as well as pairwise relationships between them which are disease-gene, chemical-gene, disease-variant, gene-gene, chemical-disease, chemical-chemical, chemical-variant, and variant-variant. Furthermore, relationships are categorized into the following semantic categories: positive correlation, negative correlation, binding, conversion, drug interaction, comparison, cotreatment, and association. Unlike most of the previous publicly available corpora, all relationships are expressed at the document level as opposed to the sentence level, and as such, the entities are normalized to the corresponding concept identifiers of the standardized vocabularies, namely, diseases and chemicals are normalized to MeSH, genes (and proteins) to National Center for Biotechnology Information (NCBI) Gene, species to NCBI Taxonomy, cell lines to Cellosaurus, and gene/protein variants to Single Nucleotide Polymorphism Database. Finally, each annotated relationship is categorized as 'novel' depending on whether it is a novel finding or experimental verification in the publication it is expressed in. This distinction helps differentiate novel findings from other relationships in the same text that provides known facts and/or background knowledge. The BioRED-BC8 corpus uses the previous BioRED corpus of 600 PubMed articles as the training dataset and includes a set of newly published 400 articles to serve as the test data for the challenge. All test articles were manually annotated for the BioCreative VIII challenge by expert biocurators at the National Library of Medicine, using the original annotation guidelines, where each article is doubly annotated in a three-round annotation process until full agreement is reached between all curators. This manuscript details the characteristics of the BioRED-BC8 corpus as a critical resource for biomedical named entity recognition and relation extraction. Using this new resource, we have demonstrated advancements in biomedical text-mining algorithm development. Database URL: https://codalab.lisn.upsaclay.fr/competitions/16381.

Making Metadata Machine-Readable as the First Step to Providing Findable, Accessible, Interoperable, and Reusable Population Health Data: Framework Development and Implementation Study.

Metadata describe and provide context for other data, playing a pivotal role in enabling findability, accessibility, interoperability, and reusability (FAIR) data principles. By providing comprehensive and machine-readable descriptions of digital resources, metadata empower both machines and human users to seamlessly discover, access, integrate, and reuse data or content across diverse platforms and applications. However, the limited accessibility and machine-interpretability of existing metadata for population health data hinder effective data discovery and reuse. To address these challenges, we propose a comprehensive framework using standardized formats, vocabularies, and protocols to render population health data machine-readable, significantly enhancing their FAIRness and enabling seamless discovery, access, and integration across diverse platforms and research applications. The framework implements a 3-stage approach. The first stage is Data Documentation Initiative (DDI) integration, which involves leveraging the DDI Codebook metadata and documentation of detailed information for data and associated assets, while ensuring transparency and comprehensiveness. The second stage is Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardization. In this stage, the data are harmonized and standardized into the OMOP CDM, facilitating unified analysis across heterogeneous data sets. The third stage involves the integration of Schema.org and JavaScript Object Notation for Linked Data (JSON-LD), in which machine-readable metadata are generated using Schema.org entities and embedded within the data using JSON-LD, boosting discoverability and comprehension for both machines and human users. We demonstrated the implementation of these 3 stages using the Integrated Disease Surveillance and Response (IDSR) data from Malawi and Kenya. The implementation of our framework significantly enhanced the FAIRness of population health data, resulting in improved discoverability through seamless integration with platforms such as Google Dataset Search. The adoption of standardized formats and protocols streamlined data accessibility and integration across various research environments, fostering collaboration and knowledge sharing. Additionally, the use of machine-interpretable metadata empowered researchers to efficiently reuse data for targeted analyses and insights, thereby maximizing the overall value of population health resources. The JSON-LD codes are accessible via a GitHub repository and the HTML code integrated with JSON-LD is available on the Implementation Network for Sharing Population Information from Research Entities website. The adoption of machine-readable metadata standards is essential for ensuring the FAIRness of population health data. By embracing these standards, organizations can enhance diverse resource visibility, accessibility, and utility, leading to a broader impact, particularly in low- and middle-income countries. Machine-readable metadata can accelerate research, improve health care decision-making, and ultimately promote better health outcomes for populations worldwide.

Dual data mapping with fine-tuned large language models and asset administration shells toward interoperable knowledge representation

In the context of Industry 4.0, ensuring the compatibility of digital twins (DTs) with existing software systems in the manufacturing sector presents a significant challenge. The Asset Administration Shell (AAS), conceptualized as the standardized DT for an asset, offers a powerful framework that connects the DT with the established software infrastructure through interoperable knowledge representation. Although the IEC 63278 series specifies the AAS metamodel, it lacks a matching strategy for automating the mapping between proprietary data from existing software and AAS information models. Addressing this gap, we introduce a novel dual data mapping system (DDMS) that utilizes a fine-tuned open-source large language model (LLM) for entity matching. This system facilitates not only the mapping between existing software and AAS models but also between AAS models and standardized vocabulary dictionaries, thereby enhancing the model's semantic interoperability. A case study within the injection molding domain illustrates the practical application of DDMS for the automated creation of AAS instances, seamlessly integrating the manufacturer's existing data. Furthermore, we extensively investigate the potential of fine-tuning decode-only LLMs as generative classifiers and encoding-based classifiers for the entity matching task. To this end, we establish two AAS-specific datasets by collecting and compiling AAS-related resources. In addition, supplementary experiments are performed on general entity-matching benchmark datasets to ensure that our empirical conclusions and insights are generally applicable. The experiment results indicate that the fine-tuned generative LLM classifier achieves slightly better results, while the encoding-based classifier enables much faster inference. Furthermore, the fine-tuned LLM surpasses all state-of-the-art approaches for entity matching, including GPT-4 enhanced with in-context learning and chain of thoughts. This evidence highlights the effectiveness of the proposed DDMS in bridging the interoperability gap within DT applications, offering a scalable solution for the manufacturing industry.

Grammatical gender in spoken word recognition in school-age Spanish monolingual and Spanish-English bilingual children.

This study examined grammatical gender processing in school-aged children with varying levels of cumulative English exposure. Children participated in a visual world paradigm with a four-picture display where they heard a gendered article followed by a target noun and were in the context where all images were the same gender (same gender), where all of the distractor images were the opposite gender than the target noun (different gender), and where all of the distractor images were the opposite gender, but there was a mismatch in the gendered article and target noun pair. We investigated 51 children (aged 5;0-10;0) who were exposed to Spanish since infancy but varied in their amount of cumulative English exposure. In addition to the visual word paradigm, all children completed an article-noun naming task, a grammaticality judgment task, and standardized vocabulary tests. Parents reported on their child's cumulative English language exposure and current English language use. To investigate the time course of lexical facilitation effects, looks to the target were analyzed with a cluster-based permutation test. The results revealed that all children used gender in a facilitatory way (during the noun region), and comprehension was significantly inhibited when the article-noun pairing was ungrammatical rather than grammatical. Compared to children with less cumulative English exposure, children with more cumulative English exposure looked at the target noun significantly less often overall, and compared to younger children, older children looked at the target noun significantly more often overall. Additionally, children with lower cumulative English exposure looked at target nouns more in the different-gender condition than the same-gender condition for masculine items more than feminine items.

IMPatienT: An Integrated Web Application to Digitize, Process and Explore Multimodal PATIENt daTa.

Medical acts, such as imaging, lead to the production of various medical text reports that describe the relevant findings. This induces multimodality in patient data by combining image data with free-text and consequently, multimodal data have become central to drive research and improve diagnoses. However, the exploitation of patient data is problematic as the ecosystem of analysis tools is fragmented according to the type of data (images, text, genetics), the task (processing, exploration) and domain of interest (clinical phenotype, histology). To address the challenges, we developed IMPatienT (Integrated digital Multimodal PATIENt daTa), a simple, flexible and open-source web application to digitize, process and explore multimodal patient data. IMPatienT has a modular architecture allowing to: (i) create a standard vocabulary for a domain, (ii) digitize and process free-text data, (iii) annotate images and perform image segmentation, (iv) generate a visualization dashboard and provide diagnosis decision support. To demonstrate the advantages of IMPatienT, we present a use case on a corpus of 40 simulated muscle biopsy reports of congenital myopathy patients. As IMPatienT provides users with the ability to design their own vocabulary, it can be adapted to any research domain and can be used as a patient registry for exploratory data analysis. A demo instance of the application is available at https://impatient.lbgi.fr/.

Cross-modal digit span and vocabulary proficiency in deaf or hard-of-hearing children.

This study aims to examine the relationship between vocabulary proficiency and short-term memory capacity in deaf or hard-of-hearing (DHH) children. We test the hypothesis that the relationship between vocabulary skills and digit span performance could be strengthened when the digit span task encompasses cross-modal integration processes. A group of DHH children performed two types of auditory digit span tasks. Furthermore, they participated in a standardized vocabulary proficiency test, comprising two subtests: Receptive Vocabulary and Expressive Vocabulary. The verbal digit span served as a significant predictor of Expressive Vocabulary among the DHH children. Simultaneously, the auditory-pointing digit span accounted for a substantial portion of performance variation in both Receptive and Expressive Vocabulary. After considering the impact of the duration of auditory-verbal intervention through regression models, likelihood ratio tests demonstrated that the auditory-pointing digit span persisted as a significant determinant of both receptive and expressive vocabulary skills. A positive influence of the intervention was also confirmed by the present results. This study provides evidence that memory span and the ability to integrate cross-modal information could serve as significant cognitive correlates of vocabulary proficiency in DHH children.

Prenatal maternal Inflammation, childhood cognition and adolescent depressive symptoms

BackgroundAccumulating evidence indicates that higher prenatal maternal inflammation is associated with increased depression risk in adolescent and adult-aged offspring. Prenatal maternal inflammation (PNMI) may increase the likelihood for offspring to have lower cognitive performance, which, in turn, may heighten risk for depression onset. Therefore, this study explored the potential mediating role of childhood cognitive performance in the relationship between PNMI and adolescent depressive symptoms in offspring. MethodsParticipants included 696 mother-offspring dyads from the Child Health and Development Studies (CHDS) cohort. Biomarkers of maternal inflammation [interleukin (IL)-6, IL-8, IL-1 receptor antagonist (IL-1RA) and soluble TNF receptor-II (sTNF-RII)] were assayed from first (T1) and second trimester (T2) sera. Childhood (ages 9–11) cognitive performance was assessed via standardized Peabody Picture Vocabulary Test (PPVT), a measure of receptive vocabulary correlated with general intelligence. Adolescent (ages 15–17) depressive symptoms were assessed via self-report. ResultsThere were no significant associations between T1 biomarkers and childhood PPVT or adolescent depressive symptoms. Higher T2 IL1-RA was directly associated with lower childhood PPVT (b = -0.21, SE = 0.08, t = -2.55, p = 0.01), but not with adolescent depressive symptoms. T2 IL-6 was not directly associated with childhood PPVT, but higher T2 IL-6 was directly associated at borderline significance with greater depressive symptoms in adolescence (b = 0.05, SE = 0.03, t = 1.96, p = 0.05). Lower childhood PPVT predicted significantly higher adolescent depressive symptoms (b = -0.07, SE = 0.02, t = -2.99, p < 0.01). There was a significant indirect effect of T2 IL-1RA on adolescent depressive symptoms via childhood PPVT (b = 0.03, 95 % CI = 0.002–0.03) indicating a partially mediated effect. No significant associations were found with T2 sTNF-RII nor IL-8. ConclusionsLower childhood cognitive performance, such as that indicated by a lower PPVT score, represents a potential mechanism through which prenatal maternal inflammation contributes to adolescent depression risk in offspring.

Peningkatan Kemampuan Siswa dalam Membedakan Bahasa Baku dan Bahasa Tidak Baku Berdasarkan EYD

The ability to differentiate standard language from non-standard language is an important aspect in learning Indonesian. This research aims to examine the effectiveness of using Enhanced Spelling (EYD) in improving students' ability to differentiate between standard and non-standard language. The method used is a literature study by examining in depth the EYD guidelines published by the Language Development and Development Agency, as well as analyzing scientific journals related to the application of EYD in learning. The results of the study show that the EYD guidelines provide a list of standard and non-standard words arranged alphabetically as well as clear and systematic rules for writing standard language. Several previous studies revealed that intensive use of word lists and rules can increase students' mastery of standard vocabulary and understanding of the differences between standard and non-standard language. However, the obstacle in implementing EYD in the field is the teacher's lack of understanding of the existing rules. Therefore, intensive training and socialization is needed for teachers so that the use of EYD can be optimal in improving students' ability to differentiate between standard and non-standard languages. This research concludes that the EYD guide is a very effective source for improving these abilities by providing a clear list of words and standard language rules as a guide for teachers and students.

Evaluation of shoot-growth variation in diverse carrot (Daucus carota L.) germplasm for genetic improvement of stand establishment.

Carrot (Daucus carota L.) is a high value, nutritious, and colorful crop, but delivering carrots from seed to table can be a struggle for carrot growers. Weed competitive ability is a critical trait for crop success that carrot and its apiaceous relatives often lack owing to their characteristic slow shoot growth and erratic seedling emergence, even among genetically uniform lines. This study is the first field-based, multi-year experiment to evaluate shoot-growth trait variation over a 100-day growing season in a carrot diversity panel (N=695) that includes genetically diverse carrot accessions from the United States Department of Agriculture National Plant Germplasm System. We report phenotypic variability for shoot-growth characteristics, the first broad-sense heritability estimates for seedling emergence (0.68 < H2 < 0.80) and early-season canopy coverage ( 0.61 < H2 < 0.65), and consistent broad-sense heritability for late-season canopy height (0.76 < H2 < 0.82), indicating quantitative inheritance and potential for improvement through plant breeding. Strong correlation between emergence and canopy coverage (0.62 < r < 0.72) suggests that improvement of seedling emergence has great potential to increase yield and weed competitive ability. Accessions with high emergence and vigorous canopy growth are of immediate use to breeders targeting stand establishment, weed-tolerance, or weed-suppressant carrots, which is of particular advantage to the organic carrot production sector, reducing the costs and labor associated with herbicide application and weeding. We developed a standardized vocabulary and protocol to describe shoot-growth and facilitate collaboration and communication across carrot research groups. Our study facilitates identification and utilization of carrot genetic resources, conservation of agrobiodiversity, and development of breeding stocks for weed-competitive ability, with the long-term goal of delivering improved carrot cultivars to breeders, growers, and consumers. Accession selection can be further optimized for efficient breeding by combining shoot growth data with phenological data in this study's companion paper to identify ideotypes based on global market needs.

The OMOP common data model in Australian primary care data: Building a quality research ready harmonised dataset.

The use of routinely collected health data for secondary research purposes is increasingly recognised as a methodology that advances medical research, improves patient outcomes, and guides policy. This secondary data, as found in electronic medical records (EMRs), can be optimised through conversion into a uniform data structure to enable analysis alongside other comparable health metric datasets. This can be achieved with the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM), which employs a standardised vocabulary to facilitate systematic analysis across various observational databases. The concept behind the OMOP-CDM is the conversion of data into a common format through the harmonisation of terminologies, vocabularies, and coding schemes within a unique repository. The OMOP model enhances research capacity through the development of shared analytic and prediction techniques; pharmacovigilance for the active surveillance of drug safety; and 'validation' analyses across multiple institutions across Australia, the United States, Europe, and the Asia Pacific. In this research, we aim to investigate the use of the open-source OMOP-CDM in the PATRON primary care data repository. We used standard structured query language (SQL) to construct, extract, transform, and load scripts to convert the data to the OMOP-CDM. The process of mapping distinct free-text terms extracted from various EMRs presented a substantial challenge, as many terms could not be automatically matched to standard vocabularies through direct text comparison. This resulted in a number of terms that required manual assignment. To address this issue, we implemented a strategy where our clinical mappers were instructed to focus only on terms that appeared with sufficient frequency. We established a specific threshold value for each domain, ensuring that more than 95% of all records were linked to an approved vocabulary like SNOMED once appropriate mapping was completed. To assess the data quality of the resultant OMOP dataset we utilised the OHDSI Data Quality Dashboard (DQD) to evaluate the plausibility, conformity, and comprehensiveness of the data in the PATRON repository according to the Kahn framework. Across three primary care EMR systems we converted data on 2.03 million active patients to version 5.4 of the OMOP common data model. The DQD assessment involved a total of 3,570 individual evaluations. Each evaluation compared the outcome against a predefined threshold. A 'FAIL' occurred when the percentage of non-compliant rows exceeded the specified threshold value. In this assessment of the primary care OMOP database described here, we achieved an overall pass rate of 97%. The OMOP CDM's widespread international use, support, and training provides a well-established pathway for data standardisation in collaborative research. Its compatibility allows the sharing of analysis packages across local and international research groups, which facilitates rapid and reproducible data comparisons. A suite of open-source tools, including the OHDSI Data Quality Dashboard (Version 1.4.1), supports the model. Its simplicity and standards-based approach facilitates adoption and integration into existing data processes.

Retrieval Practice and Word Learning by Children With Developmental Language Disorder: Does Expanding Retrieval Provide Additional Benefit?

The word learning of preschool-age children with developmental language disorder (DLD) is improved when spaced retrieval practice is incorporated into the learning sessions. In this preregistered study, we compared two types of spacing-an expanding retrieval practice schedule and an equally spaced schedule-to determine if one of these approaches yields better word learning outcomes for the children. Fourteen children with DLD aged 4-5 years and 14 same-age children with typical language development (TD) learned eight novel nouns over two sessions. Spacing for half of the novel words was expanded gradually during learning; for the remaining novel words, greater spacing remained at the same level throughout learning. Immediately after the second session and 1 week later, the children's recall of the words was tested. The children with TD recalled more novel words than the children with DLD, although this difference could be accounted for by differences in the children's standardized receptive vocabulary test scores. The two groups were similar in their ability to retain the words over 1 week. Initially, the shorter spacing in the expanding schedule resulted in greater retrieval success than the corresponding (longer spaced) retrieval trials in the equally spaced schedule. These early shorter spaced trials also seemed to benefit retrieval of the trials with greater spacing that immediately followed. However, as the learning period progressed, the accuracy levels for the two conditions converged and were likewise similar during final testing. We need a greater understanding of how and when short spacing can be helpful to children's word learning, with the recognition that early gains might give a misleading picture of the benefits that short spacing can provide to longer term retention. https://doi.org/10.23641/asha.25537696.

Effectiveness of Poster Presentation Method for Vocabulary Enhancement of International Information Technology University Students

This paper investigates the impact of employing poster-making activities on the vocabulary proficiency of intermediate-level International Information Technology Students. The study focuses on a sample of four groups, each comprising 15 students, randomly assigned to either the treatment or control condition. The treatment group engages in structured poster-making exercises centered around IT-related terminology, while the control group receives conventional vocabulary instruction through traditional methods. Pre- and post-assessments, consisting of standardized vocabulary tests, will be administered to all four groups to measure vocabulary acquisition. Statistical analysis will be employed to compare the pre- and post-test scores between the treatment and control groups, while controlling for any initial differences in vocabulary proficiency. This article strictly adheres to a quantitative methodology, focusing on objective measurements of vocabulary gains. The study aims to provide empirical evidence regarding the effectiveness of poster-making activities as a supplementary tool for vocabulary enhancement in the context of Information Technology education. The findings of this study will contribute to the growing body of literature on innovative language learning strategies tailored to specialized fields like information technology. Additionally, the results will offer practical insights for educators and curriculum developers seeking evidence-based approaches to address the unique language learning needs of students in technical disciplines.

Some good news for the school: the levelling effect of the EFL class in FL vocabulary production and FL categorization of autochthonous learners and heritage speaker learners

Abstract Several researchers have drawn attention to the educational challenge posed by multilingual classes where students from different linguistic backgrounds coexist. Current institutional initiatives in Europe, but also elsewhere, advocate for the maintenance and promotion of multilingual and multicultural diversity within both monolingual and multilingual classrooms. In this study located in Spain, we were interested in exploring the English as a Foreign Language (EFL) vocabulary production and FL categorization of a group of heritage bilinguals in comparison with their mainstream monolingual peers. Results from a categorization task revealed that on most measures taken, heritage bilinguals, despite being a more heterogenous group, performed similarly in terms of vocabulary production in general, and as compared against the framework of a standard pre-B1 Cambridge vocabulary list, FL categorization, and typicality structures. The levelling effect of the EFL class is claimed to be accountable for this lack of differences. Furthermore, we dare speculate the generalization of CLIL could play in favour of more inclusive classes where immigrant learners are not left behind and can realise their potential to the fullest.

Conflict Avoidance in Social Navigation—a Survey

A major goal in robotics is to enable intelligent mobile robots to operate smoothly in shared human-robot environments. One of the most fundamental capabilities in service of this goal is competent navigation in this “social” context. As a result, there has been a recent surge of research on social navigation; and especially as it relates to the handling of conflicts between agents during social navigation. These developments introduce a variety of models and algorithms, however as this research area is inherently interdisciplinary, many of the relevant papers are not comparable and there is no shared standard vocabulary. This survey aims at bridging this gap by introducing such a common language, using it to survey existing work, and highlighting open problems. It starts by defining the boundaries of this survey to a limited, yet highly common type of social navigation—conflict avoidance. Within this proposed scope, this survey introduces a detailed taxonomy of the conflict avoidance components. This survey then maps existing work into this taxonomy, while discussing papers using its framing. Finally, this article proposes some future research directions and open problems that are currently on the frontier of social navigation to aid ongoing and future research.