An investigation in the learning effects of integrated development projects. In two subsequent semesters the students were asked how they rated their competencies at the start of the project as well as at the end of it. The students voluntarily filled out a questionnaire. After the last questionnaire a number of students were also interviewed in order to learn more about their perceptions. It was a remarkable outcome of these interviews that a lot of students tended to give themselves lower ratings in the end if they met any difficulties in for instance communication or co-operation during the project. Then the questionnaire showed a decrease in the student's ratings, while anyone else would say the student did learn something after recognizing these difficulties. It required a different interpretation of the outcomes of the questionnaires. The investigation showed that co-operating in general and in multidisciplinary teams in particular, co-operating with companies and also working according to plans are the four objectives that are recognized mostly by the students. The factors that actually contribute to, or block, the learning effects remained unknown yet.
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An on-going investigation in the learning effects of IPD projects. In three subsequent semesters the students were asked how they rated their competencies at the start of the project as well as at the end of it. Also questionnaires were filled out and students were interviewed. A lot of students tended to give themselves lower ratings in the end than in the begin. It appeared that if they met any difficulties in for instance communication or co-operation during the project, that they interpreted this as a decrease in competencies. Finally the students were explicitly asked to mention an eventual increase in competencies and also a possible contribution for this effect. Only a few factors that actually contribute to the learning effects have been defined.
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The paper introduced an automatic score detection model using object detection techniques. The performance of sevenmodels belonging to two different architectural setups was compared. Models like YOLOv8n, YOLOv8s, YOLOv8m, RetinaNet-50, and RetinaNet-101 are single-shot detectors, while Faster RCNN-50 and Faster RCNN-101 belong to the two-shot detectors category. The dataset was manually captured from the shooting range and expanded by generating more versatile data using Python code. Before the dataset was trained to develop models, it was resized (640x640) and augmented using Roboflow API. The trained models were then assessed on the test dataset, and their performance was compared using matrices like mAP50, mAP50-90, precision, and recall. The results showed that YOLOv8 models can detect multiple objects with good confidence scores.
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