As a result of the changing notions of work schools are increasingly acknowledging that they have a strong responsibility to guide students not only in their academic growth, but also in their career development. This paper presents the result of a study about effects of teachers training on career dialogue promoting career competency development in students. For the quantitative part of the study, a quasi experimental research design is used to measure effects among 2500 students. Video-recordings of conversations are used for qualitative research. The results show only when the off-the-job training is followed by on-the-job coaching, the professionalizing proves to be effective on student level: students notice that the guidance conversations are more appreciative, reflective and activating and are about self image development, work and career actions. Also the observation on guidance conversations show that the conversations are more career related.
As a result of the changing notions of work schools are increasingly acknowledging that they have a strong responsibility to guide students not only in their academic growth, but also in their career development. This paper presents the result of a study about effects of teachers training on career dialogue promoting career competency development in students. For the quantitative part of the study, a quasi experimental research design is used to measure effects among 2500 students. Video-recordings of conversations are used for qualitative research. The results show only when the off-the-job training is followed by on-the-job coaching, the professionalizing proves to be effective on student level: students notice that the guidance conversations are more appreciative, reflective and activating and are about self image development, work and career actions. Also the observation on guidance conversations show that the conversations are more career related.
Gepubliceerd in Mikroniek, nr. 6 2018 In manufacturing environments where collaborative robots are employed, conventional computer vision algorithms have trouble in the robust localisation and detection of products due to changing illumination conditions and shadows caused by a human sharing the workspace with the robotic system. In order to enhance the robustness of vision applications, machine learning with neural networks is explored. The performance of machine-learning algorithms versus conventional computer vision algorithms is studied by observing a generic user scenario for the manufacturing process: the assembly of a product by localisation, identification and manipulation of building blocks.
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