Poster presented at the workshop Rural signing varieties: social dynamics and linguistic structure, results from the EuroBABEL project on 'Endangered Sign Languages in Village Communities', Leiden, 7th November 2012
Worldwide, pupils with migrant backgrounds do not participate in school STEM subjects as successfully as their peers. Migrant pupils’ subject-specific language proficiency lags behind, which hinders participation and learning. Primary teachers experience difficulty in teaching STEM as well as promoting required language development. This study investigates how a professional development program (PDP) focusing on inclusive STEM teaching can promote teacher learning of language-promoting strategies (promoting interaction, scaffolding language and using multilingual resources). Participants were five case study teachers in multilingual schools in the Netherlands (N = 2), Sweden (N = 1) and Norway (N = 2), who taught in primary classrooms with migrant pupils. The PDP focused on three STEM units (sound, maintenance, plant growth) and language-promoting strategies. To trace teachers’ learning, three interviews were conducted with each of the five teachers (one after each unit). The teachers also filled in digital logs (one after each unit). The interviews showed positive changes in teachers’ awareness, beliefs and attitudes towards language-supporting strategies. However, changes in practice and intentions for practice were reported to a lesser extent. This study shows that a PDP can be an effective starting point for teacher learning regarding inclusive STEM teaching. It also illuminates possible enablers (e.g., fostering language awareness) or hinderers (e.g., teachers’ limited STEM knowledge) to be considered in future PDP design.
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BACKGROUND: Atypical speech and language development is one of the most common developmental difficulties in young children. However, which clinical signs characterize atypical speech-language development at what age is not clear.AIM: To achieve a national and valid consensus on clinical signs and red flags (i.e. most urgent clinical signs) for atypical speech-language development in children from 1 to 6 years of age.METHODS & PROCEDURES: A two-round Delphi study in the Netherlands with a national expert panel (n = 24) of speech and language therapists was conducted. The panel members responded to web-based questionnaires addressing clinical signs. Consensus was defined as ≥ 70% of the experts agreeing on an issue.OUTCOMES & RESULTS: The first round resulted in a list of 161 characteristics of atypical speech and language development. The second round led to agreement on 124 clinical signs and 34 red flags.CONCLUSIONS & IMPLICATIONS: Dutch national consensus concerns 17-23 clinical signs per age year for the description of an atypical speech-language development in young children and three to 10 characteristics per age year being red flags for atypical speech-language development. This consensus contributes to early identification and diagnosis of children with atypical speech-language development, awareness and research.
Developing a framework that integrates Advanced Language Models into the qualitative research process.Qualitative research, vital for understanding complex phenomena, is often limited by labour-intensive data collection, transcription, and analysis processes. This hinders scalability, accessibility, and efficiency in both academic and industry contexts. As a result, insights are often delayed or incomplete, impacting decision-making, policy development, and innovation. The lack of tools to enhance accuracy and reduce human error exacerbates these challenges, particularly for projects requiring large datasets or quick iterations. Addressing these inefficiencies through AI-driven solutions like AIDA can empower researchers, enhance outcomes, and make qualitative research more inclusive, impactful, and efficient.The AIDA project enhances qualitative research by integrating AI technologies to streamline transcription, coding, and analysis processes. This innovation enables researchers to analyse larger datasets with greater efficiency and accuracy, providing faster and more comprehensive insights. By reducing manual effort and human error, AIDA empowers organisations to make informed decisions and implement evidence-based policies more effectively. Its scalability supports diverse societal and industry applications, from healthcare to market research, fostering innovation and addressing complex challenges. Ultimately, AIDA contributes to improving research quality, accessibility, and societal relevance, driving advancements across multiple sectors.
Aanleiding Nieuwsuitgeverijen bevinden zich in zwaar weer. Economische malaise en toegenomen concurrentie in het pluriforme medialandschap dwingen uitgeverijen om enerzijds kosten te besparen en tegelijkertijd te investeren in innovatie. De verdere automatisering van de nieuwsredactie vormt hierbij een uitdaging. Buiten de branche ontstaan technieken die uitgeverijen hierbij zouden kunnen gebruiken. Deze zijn nog niet 'vertaald' naar gebruiksvriendelijke systemen voor redactieprocessen. De deelnemers aan het project formuleren voor dit braakliggend terrein een praktijkgericht onderzoek. Doelstelling Dit onderzoek wil antwoord geven op de vraag: Hoe kunnen bewezen en nieuw te ontwikkelen technieken uit het domein van 'natural language processing' een bijdrage leveren aan de automatisering van een nieuwsredactie en het journalistieke product? 'Natural language processing' - het automatisch genereren van taal - is het onderwerp van het onderzoek. In het werkveld staat deze ontwikkeling bekend als 'automated journalism' of 'robotjournalistiek'. Het onderzoek richt zich enerzijds op ontwikkeling van algoritmes ('robots') en anderzijds op de impact van deze technologische ontwikkelingen op het nieuwsveld. De impact wordt onderzocht uit zowel het perspectief van de journalist als de nieuwsconsument. De projectdeelnemers ontwikkelen binnen dit onderzoek twee prototypes die samen het automated-journalismsysteem vormen. Dit systeem gaat tijdens en na het project gebruikt worden door onderzoekers, journalisten, docenten en studenten. Beoogde resultaten Het concrete resultaat van het project is een prototype van een geautomatiseerd redactiesysteem. Verder levert het project inzicht op in de verankering van dit soort systemen binnen een nieuwsredactie. Het onderzoek biedt een nieuw perspectief op de manier waarop de nieuwsconsument de ontwikkeling van 'automated journalism' in Nederland waardeert. Het projectteam deelt de onderzoekresultaten door middel van presentaties voor de uitgeverijbranche, presentaties op wetenschappelijke conferenties, publicaties in (vak)tijdschriften, reflectiebijeenkomsten met collega-opleidingen en een samenvattende white paper.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
