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
The great transformation of e-learning during the Covid-19 pandemic has led to the emergence of new learning tools and virtual learning environments through Internet. Despite many advantages of e-Learning courseware systems such as availability, flexibility and accessibility, students of some sectors such as engineering, science and technology are facing several limitations in conducting their practical works remotely through online platform for laboratory experiments. The specific objective of this study was to come up with an updated success criteria and list of requirements that should be considered for developing a sustainable artificial intelligence-based online laboratory courseware system. Data for this study were collected using online questionnaire distributed to a group of e-Learning experts. NVivo software was used to analyze experts' comments on how to construct the online laboratory courseware systems. This research revealed 16 basic design and development criteria for the online laboratory courseware system, which are distributed into eight sub-branches and organized into four primary aspects. These findings suggest that in general 30 accurate indicators for the design of an effective laboratory learning system (LLS) for engineering, science and technology sectors dealing with content management, assessment, accessibility and usability as well as the adoption of artificial intelligence techniques. © 2022 IEEE.
ABSTRACT
Online learning has emerged as powerful learning methods for the transformation from traditional education to open learning through smart learning platforms due to Covid-19 pandemic. Despite its effectiveness, many studies have indicated the necessity of linking online learning methods with the cognitive learning styles of students. The level of students always improves if the teaching methods and educational interventions are appropriate to the cognitive style of each student individually. Currently, psychological measures are used to assess students' cognitive styles, but about the application in virtual environment, the matter becomes complicated. The main goal of this study is to provide an efficient solution based on machine learning techniques to automatically identify the students' cognitive styles by analyzing their mouse interaction behaviors while carrying out online laboratory experiments. This will help in the design of an effective online laboratory experimentation system that is able to individualize the experiment instructions and feedback according to the identified cognitive style of each student. The results reveal that the KNN and SVM classifiers have a good accuracy in predicting most cognitive learning styles. In comparison to KNN, the enlarged studies ensemble the KNN, linear regression, neural network, and SVM reveal a 13% increase in overall total RMS error. We believe that this finding will enable educators and policy makers to predict distinct cognitive types in the assessment of students when they interact with online experiments. We believe that integrating deep learning algorithms with a greater emphasis on mouse location traces will improve the accuracy of our classifiers' predictions.
ABSTRACT
COVID-19 pandemic has led to a great interest in online learning systems. However, the lack of suitable online laboratory learning systems has posed a particular challenge for sectors that need laboratory experimentation activities as in engineering and science domains. This paper presents a simple but efficient technique for providing intelligent virtual tutor that can assist students in online laboratory experimentation environment. The proposed technique is based on analyzing and modelling the student's mouse interaction behavior for identifying the difficulties that the student faced during conducting the lab's experiment, and hence providing the suitable assistance. The different levels of difficulties will be detected using the trajectory of mouse movement activities. The obtained results verify accurate and very fast operation for identifying the student's difficulties.
ABSTRACT
The worldwide outbreak due to COVID-19 pandemic has led to a great interest in e-learning. However, the lack of suitable online laboratory management systems has posed a particular challenge for sectors that need laboratory activities such as engineering, science and technology. In this paper, the requirements and design for a flexible AI-based laboratory learning system (LLS) that can support online laboratory experimentations are presented. The elicitation of the LLS design requirements is decided based on a conducted survey for a set of LLS features. The LLS is designed with the flexibility to support various types of online experimentations such as virtual or remote controlled experiments using either desktop or web applications. The virtualization technique is used to manage the laboratory resources and allow multiple users to access the LLS. Moreover, the proposed LLS introduces the use of AI techniques to provide efficient virtual lab assistant and adaptive assessment process. © 2021 IEEE.