This small-scale observational study explores how Dutch bilingual education history teachers (BHTs) focus on the L2 component in their CLIL-lessons. We observed and rated eight BHTs on five language teaching categories. Results show that Dutch BHTs focus more strongly on using the L2 to teach subject content and that they tend to be less engaged in teaching specific second language topics, such as focus on form or language learning strategies. Further results and suggestions for improving the BHTs’ L2 focus are discussed together with a plea for a CLIL definition that is more in line with the everyday reality of the CLIL classroom.
This study investigates subject teachers’ practical knowledge and teaching behaviour regarding integrated language teaching in the context of vocational education. The emphasis was on the nature of teachers’ subject-specific language awareness and how they enact this awareness in their teaching practice. For this purpose, teachers in vocational education were interviewed and observed while teaching. The results reveal that teachers differ in their subject-specific language awareness. Some teachers are unaware of the relation between language and learning, while others are aware of this relation and feel responsible towards their students’ language proficiency. Teachers who feel this responsibility stimulate students’ active language use and use more advanced interaction strategies to promote students’ higher cognitive thinking. The results of this study indicate that raising subject teachers’ language awareness needs to be part of activities for teacher professional development.
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The current study investigated how individual differences among children affect the added value of social robots for teaching second language (L2) vocabulary to young children. Specifically, we investigated the moderating role of three individual child characteristics deemed relevant for language learning: first language (L1) vocabulary knowledge, phonological memory, and selective attention. We expected children low in these abilities to particularly benefit from being assisted by a robot in a vocabulary training. An L2 English vocabulary training intervention consisting of seven sessions was administered to 193 monolingual Dutch five-year-old children over a three- to four-week period. Children were randomly assigned to one of three experimental conditions: 1) a tablet only, 2) a tablet and a robot that used deictic (pointing) gestures (the no-iconic-gestures condition), or 3) a tablet and a robot that used both deictic and iconic gestures (i.e., gestures depicting the target word; the iconic-gestures condition). There also was a control condition in which children did not receive a vocabulary training, but played dancing games with the robot. L2 word knowledge was measured directly after the training and two to four weeks later. In these post-tests, children in the experimental conditions outperformed children in the control condition on word knowledge, but there were no differences between the three experimental conditions. Several moderation effects were found. The robot's presence particularly benefited children with larger L1 vocabularies or poorer phonological memory, while children with smaller L1 vocabularies or better phonological memory performed better in the tablet-only condition. Children with larger L1 vocabularies and better phonological memory performed better in the no-iconic-gestures condition than in the iconic-gestures condition, while children with better selective attention performed better in the iconic-gestures condition than the no-iconic-gestures condition. Together, the results showed that the effects of the robot and its gestures differ across children, which should be taken into account when designing and evaluating robot-assisted L2 teaching interventions.
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.
Taal speelt een essentiële rol in het leren, ook in zaakvakken zoals wereldoriëntatie of geschiedenis. Soms worden deze vakken in het Engels gegeven (CLIL). We hebben lesstrategieën en lesmaterialen ontworpen om het leren in CLIL-contexten te bevorderen.Doel Nederlandse basisscholen onderwijzen (zaak)vakken steeds vaker in het Engels. Dit wordt Content and Language Integrated Learning (CLIL) genoemd. Zeker wanneer Engels de voertaal is in de klas, moeten leerkrachten zich bewust zijn van de verschillende rollen die taal speelt in het leerproces. Wij hebben daarom samen met vijf basisscholen strategieën en materialen voor CLIL-lessen ontworpen met een effectieve focus op taal. Op deze manier werkten we aan de professionele ontwikkeling van de deelnemende scholen. Met het project wilden we ook bijdragen aan bredere praktijk- en kennisontwikkeling. Resultaten Het onderzoeks- en professionaliseringsproject heeft inzichten opgeleverd over manieren waarop leraren gelijktijdig het leren van een nieuwe taal (Engels) en nieuwe inhoud kunnen stimuleren. Deze inzichten zijn ook relevant voor andere meertalige onderwijscontexten. Vier factoren bleken van grote waarde voor de professionele- en praktijkontwikkeling van de vijf consortiumscholen: • tegemoetkoming aan contextspecifieke factoren • kennis nemen en toepassen van theorie • ruimte voor reflectie • uitwisseling en samenwerking met collega’s Het project leverde ook een aantal praktijkproducten op. Deze kunt u hier downloaden (pdf): Looptijd 01 september 2018 - 30 juni 2021 Aanpak Dit is een ontwerpgericht project waarin we samen met de betrokken scholen lesstrategieën en lesmaterialen ontwierpen, implementeerden en evalueerden. Catherine van Beuningen was vanuit het lectoraat Meertaligheid en Onderwijs hoofdonderzoeker in dit project. Momenteel is zij als hoofddocent Talenonderwijs en Meertaligheid verbonden aan de Hogeschool van Amsterdam.
