ABSTRACT
Many students all over the world have faced some educational issues due to the Covid-19 epidemic. As a consequence, many educational institutes focused on shifting to an E-learning system. This paper introduces a design and implementation steps of a remotely controlled experiment representing a smart hydro energy storage and irrigation system with monitoring capability using photovoltaic power and the Internet of Things (IoT). The experiment is running within the newly proposed Laboratory Learning Management System (LLMS). The remotely controlled experiment is a smart hydro energy storage and irrigation system, where the stored water during the daytime is used at night for smart irrigation of three different types of plants based on the moisture and temperature, in addition to the amount of water that the user sets for every area. In this experiment, during the daytime, the utilities are feeding from the solar panel and battery, but at night, the utilities are feeding from the battery or the hydro turbine that converts the water potential energy to electric energy. The overall Experiment is controlled using IoT sensors and relays which are connected and driven by the parameters that the user sets and can be communicated with the system using the Internet which allows the system to be proactive and take the needed decision in the right time. The main contribution of this system's experiment is the pumping of underground water in irrigation using a renewable and clean energy source, in addition to controlling the systems using IoT through the proposed LLMS. © 2022 IEEE.
ABSTRACT
Due to the COVID-19 pandemic and the development of educational technology, e-learning has become essential in the educational process. However, the adoption of e-learning in sectors such as engineering, science, and technology faces a particular challenge as it needs a special Laboratory Learning Management System (LLMS) capable of supporting online lab activities through virtual and controlled remote labs. One of the most challenging tasks in designing such LLMS is how to assess a student's performance while an experiment is being conducted and how stuttering students can be automatically detected while experimenting and providing the appropriate assistance. For this, a generic technique based on Artificial Intelligence (AI) is proposed in this paper for assessing student performance while conducting online labs and implemented as a performance evaluation module in the LLMS. The performance evaluation module is designed to automatically detect the student performance during the experiment run time and triggers the LLMS virtual assistant service to provide struggling students with the appropriate help when they need it. Also, the proposed performance assessment technique is used during the lab exam sessions to support the automatic grading process conducted by the LLMS Auto-Grading Module. The proposed performance evaluation technique has been developed based on analyzing the student's mouse dynamics to work generally with any type of simulation or control software used by virtual or remote controlled laboratories;without the need for special interfacing. The study has been applied to a novel dataset built by the course instructors and students simulating a circuit on TinkerCad. Using mouse dynamics fetching, the system extracts features and evaluates them to determine if the student has built the experiment steps in the right way or not. A comparison study has been developed between different Machine Learning (ML) models and a number of performance metrics are calculated. The study confirmed that Artificial Neural Network (ANN) and Support Vector Machine (SVM) are the best models to be used for automatically evaluating student performance while conducting the online labs with a precision reaching up to 91%.