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Efficient Collaborative and Cooperative Laboratory Experimentation System for Online Engineering, Science and Technology Education

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Artificial Intelligence and Online Engineering (REV 2022)

Abstract

The presence of the COVID-19 pandemic forced the educational systems all over the world to shift their activities to be hold remotely using online learning systems. Creating an efficient remote learning system that facilitate the transition to e-learning and distance education has become a must, especially in practical sectors such as Engineering, Science and Technology that require laboratory-demanded courses. Focusing only on the individual-based experiment where a single user can access and conduct the experiment, dismissing the structure of group-based laboratory experiment, can’t reflect comprehension construction as in the real on-site laboratories. In this paper, a group-based online learning system is proposed to provide a collaborative and cooperative virtual learning environment for laboratory experimentation taking into consideration different aspects that may impact the interactions between students. We divided the whole group-based laboratory experimentation platform process into four main parts: experiment creation using integrated authoring tool, experiment configuration and scheduling, monitored run-time process, and pre/post session configuration. We also proposed a runtime experiment student’s web-based graphical user interface that represents developed features that successfully achieve flexible, scalable and reusable system with the aim of maintaining satisfactory and effective user experience.

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Acknowledgment

This research is a part of research project supported by the Academy of Scientific Research and Technology (ASRT) through Egypt RDI/P4/1/20-21 program (2021–2023).

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Correspondence to Ahmed M. Abd El-Haleem .

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Abd El-Haleem, A.M., Elmosilhy, N.A., Salama, A.I., Eman, S.E.D., Elmesalawy, M.M. (2023). Efficient Collaborative and Cooperative Laboratory Experimentation System for Online Engineering, Science and Technology Education. In: Auer, M.E., El-Seoud, S.A., Karam, O.H. (eds) Artificial Intelligence and Online Engineering. REV 2022. Lecture Notes in Networks and Systems, vol 524. Springer, Cham. https://doi.org/10.1007/978-3-031-17091-1_22

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