This study analyze data from three national contexts in which teachers worked with the same teaching materials and inquiry classroom activities, investigating teachers’ use of strategies to promote interaction and scaffolding when participating in a professional development program. The data material is collected from three case studies from the Netherlands, Norway, and Sweden, respectively. Each case is from a teaching unit about green plants and seed sprouting. In one lesson in this unit, students were involved in planning an experiment with sprouting seeds, and this (similar) lesson was videotaped in three national settings. The main research question is, as follows: How do primary teachers use questions to scaffold conceptual understanding and language use in inquiry science activities? The data analysis shows that teachers ask different kind of questions such as open, closed, influencing and orienting questions. The open, orienting questions induce students to generate their own ideas, while closed orienting and influencing questions often scaffold language and content-specific meaning-making. However, both open, closed, orienting and influencing questions can scaffold student language and conceptual understanding. Often, teacher questions scaffold both language content-specific meaning-making at the same time. The study shows the subtle mechanisms through which teachers can use questions to scaffold student science literacy and thereby including them in classroom interaction.
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Introduction The CEFR offers a framework for language teaching, learning and assessment for L2 learners. Importantly, the CEFR draws on a learner’s communicative language competence rather than linguistic competence (e.g. vocabulary, grammar). As such, the implementation of the CEFR in our four years bachelor program Teacher of Sign Language of the Netherlands (NGT) caused a shift in didactic approach from grammar-based to communication-centered. It has been acknowledged that didactic approaches associated with the CEFR are scarcely documented (Figueras, 2012) and the effectiveness on learner outcomes have not been investigated systematically. Moreover, for many languages the levels of the CEFR are not supported by empirical evidence from L2 learner data (Hulstijn, 2007). Purpose We will i) describe our communication-centered approach in detail and iii) present some preliminary findings on the effectiveness of this approach on student’s outcomes. Method We followed four student cohorts longitudinally: students in the first cohort (n=14) were taught in a grammar-based curriculum, students in the second (n=6), third (n=9) and fourth (n=14) cohort in a communication-centered curriculum. Data involved production (interviews) videos that are transcribed using ELAN. Results Comparing students in their first and second year, results show that students who followed a communication-based curriculum show more grammatical variability as compared to students who followed a grammar-based curriculum. Conclusions Interestingly, the communication-centered approach stimulates the development of linguistic competence. We attempt to fit the empirical evidence of L2 learners within the CEFR-levels. References Figueras, N. (2012). The impact of the CEFR. ELT Journal, 66, 477 – 485. Hulstijn, J. (2007). The shaky ground beneath the CEFR: quantitave and qualitative dimensions of language proficiency. The Modern Language Journal, 91, 663 – 667.
Full text met HU account Although people all over the world learn sign languages as a second language (SL2), there is scant literature on sign language acquisition processes to guide professionals in the field. This study focuses on one of the modality-specific phenomena that SL2 learners with a spoken language background encounter that do not exist in their native language (L1): the use of space for grammatical reasons. We analyzed the sign language production data of two learners of Sign Language of the Netherlands (NGT) who we followed for four years. Data comprise interviews that were coded for use of space. Use of space was operationalized by measuring the number of occasions of pointing signs, agreement verbs, classifier verbs, and spatially modified signs from the nominal domain. In addition, we identified examples of typical L2 signing (e.g. errors of overgeneralization, omissions, et cetera). Data show that learners initially produce modified signs that have a gestural counterpart. It might be that they "borrow" signs from the gestural domain, or they produce these highly iconic structures because their gestural inventory has helped them to acquire these structures. Furthermore, the data show that particularly classifier verbs and agreement verbs within a constructed action sequence pose challenges for the learners, and we observed some general error patterns that have been found in L1-learners, such as stacking and reversing the movement path of agreement verbs
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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.
-Chatbots are being used at an increasing rate, for instance, for simple Q&A conversations, flight reservations, online shopping and news aggregation. However, users expect to be served as effective and reliable as they were with human-based systems and are unforgiving once the system fails to understand them, engage them or show them human empathy. This problem is more prominent when the technology is used in domains such as health care, where empathy and the ability to give emotional support are most essential during interaction with the person. Empathy, however, is a unique human skill, and conversational agents such as chatbots cannot yet express empathy in nuanced ways to account for its complex nature and quality. This project focuses on designing emotionally supportive conversational agents within the mental health domain. We take a user-centered co-creation approach to focus on the mental health problems of sexual assault victims. This group is chosen specifically, because of the high rate of the sexual assault incidents and its lifetime destructive effects on the victim and the fact that although early intervention and treatment is necessary to prevent future mental health problems, these incidents largely go unreported due to the stigma attached to sexual assault. On the other hand, research shows that people feel more comfortable talking to chatbots about intimate topics since they feel no fear of judgment. We think an emotionally supportive and empathic chatbot specifically designed to encourage self-disclosure among sexual assault victims could help those who remain silent in fear of negative evaluation and empower them to process their experience better and take the necessary steps towards treatment early on.
