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
Purpose: Online education was an inevitable approach during the COVID-19 lockdown period. We aimed to determine the effect of online learning on assessment results in courses containing practical learning objectives delivered during the pandemic. Method: A retrospective analysis was performed on the assessment scores in courses conducted during the lockdown and on-campus years. Accumulative and objective structured practical examination scores were used as outcome measures for academic performance. Courses were categorized into pure practical, theory + practical, and pure theory. Results: This study showed that online education increased student scores as evidenced by a higher P-value in theoretical (<0.0001) than pure practical courses (0.033). Discussion.: High scores indicate the effectiveness of the implemented online learning and assessment approach. However, potential confounders, such as exam validity, reliability, and misconduct, require further investigation to ensure an optimum and legitimate learning experience in future unforeseen situations. In addition, learning gaps in complex and technical learning objectives (e.g., prepare, perform, and operate) were identified and integrated in the following academic year © 2023 Association of Medical Education in the Eastern Mediterranean Region (AMEEMR). This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Sponsored by King Saud bin Abdulaziz University for Health Sciences
ABSTRACT
To prevent the spread of the Covid-19 pandemic, governments have been forced to stop offering educational services directly on campus. Thus, education has moved towards a new path;homes have been transformed into online educational classes through Learning Management Systems (LMS). Despite the many advantages of LMS such as availability, accessibility, and usability, which helps to monitor student learning and manage synchronous and asynchronous learning tools, there are many challenges facing students of applied disciplines such as sciences, engineering, and technology. Among these challenges are the following: how can laboratory experiments be conducted from a distance? How can students' achievement be measured while conducting their scientific experiment tasks? The current study aimed to reach design criteria for a new system for managing a virtual learning laboratory system (LLS). The Delphi method was used to obtain the opinions of experts and those interested in the field of e-learning design. The responses of (31) experts were analyzed using NVivo software, then the results were analyzed using statistical methods to rank them according to importance through three rounds. The results revealed that the criteria for applying artificial intelligence mechanisms, content management systems through virtual machine, assessment, and accessibility through cloud computing are among the key criteria for designing LLS for science, engineering, and technology disciplines. © 2022 IEEE.
ABSTRACT
Due to the Covid-19 epidemic the need for digital E-learning systems become mandatory. Also, most sectors that faced a shortage in E-learning systems are performing laboratory experiments remotely. For this reason, this research paper focuses on providing a complete Laboratory Learning Management System (LLMS) with generic and intelligent performance evaluation for experiments. The new LLMS offers many services from intelligently and automatically doing performance assessments and assistance for the students while performing the experiments online. The new performance assessment module provides regular assessment for experimental steps added to it the intelligent automatic assessment that detects if the students performed the experiments correctly from their mouse dynamics using an AI algorithm. Moreover, the new LLMS uses an analytic module to provide the teachers with analyzed results and charts to describe the behavior of students in various performed experiments. Regarding, the new performance assistant module provides students with complete assistance by pressing the help button to trigger the virtual tutor to explain any experimental steps. Furthermore, it intelligently to collects the mouse dynamics of the student performing the experiments and uses AI algorithms to detect if students face difficulties and provide them with suitable help automatically. Moreover, it can open a chat session with a real teaching assistant or a classmate to help the students. Furthermore, the new performance assessment and assistant services are considered generic because they used the mouse dynamic behavior of students which is suitable for any type of software used in the laboratory, without the need for a special device or extra cost. © 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%.
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. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.