10 research outputs found

    An open learning environment for the diagnosis, assistance and evaluation of students based on artificial intelligence

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    The personalized diagnosis, assistance and evaluation of students in open learning environments can be a challenging task, especially in cases that the processes need to be taking place in real-time, classroom conditions. This paper describes the design of an open learning environment under development, designed to monitor the comprehension of students, assess their prior knowledge, build individual learner profiles, provide personalized assistance and, finally, evaluate their performance by using artificial intelligence. A trial test has been performed, with the participation of 20 students, which displayed promising results

    Analytical Estimation of the Electrostatic Field in Cylinder-Plane and Cylinder-Cylinder Electrode Configurations

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    This work presents analytical formulas for the estimation of the electrostatic field in cylinder-plane and cylinder-cylinder electrode configurations. Assuming a predefined potential difference between the electrodes and given their geometrical characteristics, these could be useful for the solution of numerous problems involving such electrode sets. Moreover, the voltage distribution around the electrodes is defined by providing equations either for the equipotentials at a given voltage ratio, or the exact estimation of the potential at any point in the surrounding space. Simplified expressions for critical engineering parameters such as the peak electric field and the field enhancement factor are also given

    An Advanced eLearning Environment Developed for Engineering Learners

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    Monitoring and evaluating engineering learners through computer-based laboratory exercises is a difficult task, especially under classroom conditions. A complete diagnosis requires the capability to assess both the competence of the learner to use the scientific software and the understanding of the theoretical principles. This monitoring and evaluation needs to be continuous, unobtrusive and personalized in order to be effective. This study presents the results of the pilot application of an eLearning environment developed specifically with engineering learners in mind. As its name suggests, the Learner Diagnosis, Assistance, and Evaluation System based on Artificial Intelligence (StuDiAsE) is an Open Learning Environment that can perform unattended diagnostic, evaluation and feedback tasks based on both quantitative and qualitative parameters. The base architecture of the system, the user interface and its effect on the performance of postgraduate engineering learners are being presented

    Evaluation of an intelligent open learning system for engineering education

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    In computer-assisted education, the continuous monitoring and assessment of the learner is crucial for the delivery of personalized education to be effective. In this paper, we present a pilot application of the Student Diagnosis, Assistance, Evaluation System based on Artificial Intelligence (StuDiAsE), an open learning system for unattended student diagnosis, assistance and evaluation based on artificial intelligence. The system demonstrated in this paper has been designed with engineering students in mind and is capable of monitoring their comprehension, assessing their prior knowledge, building individual learner profiles, providing personalized assistance and, finally, evaluating a learner's performance both quantitatively and qualitatively by means of artificial intelligence techniques. The architecture and user interface of the system are being exhibited, the results and feedback received from a pilot application of the system within a theoretical engineering course are being demonstrated and the outcomes are being discussed

    Star-delta switches evaluation for use in grid-connected wind farm installations

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    Electrical generators are designed to perform best under permanent rotation velocity and fixed loads conditions. However, such ideal conditions are not practically feasible during the operation of real wind turbines. Generally, the voltage output of electrical generators can be regulated without redesigning the electrical or/and mechanical parts constituting such a system, by simply changing the connection of the generator to the grid from Star to Delta or by using combined windings. The present work attempts to investigate the behavior of grid-connected wind turbines with Star-Delta, Delta, and Star connection switches in a variety of simulation scenarios, by taking into consideration the influence of both internal and external factors such as the inertia factor and the wind speed
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