Bilingual education has become popular in many countries in the last two decades. It is generally acknowledged that learning a second language (L2) through subject content has a positive impact on students’ L2 learning, but there is less agreement on whether this also applies to learning subject content knowledge in and through L2. This cross-sectional study compared Dutch pre-university mainstream and bilingual education students in grades 7 and 9 on a history knowledge test, taking into consideration the language of instruction and testing. Students were also tested on their motivation to learn and affinity with history, because of the alleged higher motivation bilingual education students bring to the classroom. Multilevel analyses showed that bilingual education students in grade 7 lagged behind in the English part of the test but performed at the same level in the Dutch part. 9th bilingual education graders on the other hand performed significantly better on the knowledge test than 9th mainstream graders in both L2 and L1, thus providing evidence for the non-detrimental effect of bilingual education on the acquisition of subject content knowledge.
Mastering academic language (AL) by elementary school students is important for achieving school success. The extent to which teachers play a role in stimulating students’ AL development may differ. Two types of AL stimulating behavior are distinguished: aimed at students’ understanding and at triggering students’ production of AL. As mathematics requires abstract language use, AL occurs frequently. The instructional methods teachers use during mathematics instruction may offer different opportunities for AL stimulating behavior. In our first study, based on expert opinions, instructional methods were categorized according to opportunities they offer for stimulating students’ AL development. In the second study, video-observations of mathematics instruction of elementary school teachers were analyzed with respect to AL stimulating behavior and instructional methods used. Results showed that actual AL stimulating behavior of teachers corresponds to the expert opinions, except for behavior shown during task evaluation. Teachers differ in time and frequency of their use of instructional methods and therefore in opportunities for stimulating AL development. Four teaching profiles, reflecting different AL stimulating potential, were constructed: ‘teacher talking’, ‘balanced use of methods’, ‘getting students at work’ and ‘interactive teaching’. Teachers showed more types of behavior aimed at students’ AL understanding than at production.
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Within the Netherlands, Content and Language Integrated Learning (CLIL) in foreign language teaching can be considered a sibling of 'Language Oriented Content Teaching' (LOCT), a pedagogy in mainstream classes with second language learners of Dutch, where Dutch is used as language of instruction. This article characterizes two decades of research on LOCT through Dutch in multilingual schools and discusses its relevance for CLIL development.
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.
