Generic Laboratory Authoring Tool for Virtual and Remote Controlled Laboratories

Due to the impact of Covid-19, many students all over the world have faced some educational issues. Therefore, many educational institutes focused on shifting their learning process to E-learning system. To provide a complete E-learning system, the performing of virtual and remote Laboratory experiments is needed. In this paper, a generic and flexible online authoring tool for the Laboratory Learning System (LLS) is presented. The LLS system is a platform that provides teachers and students with a flexible environment for virtual and remote controlled labs using the proposed authoring tool. The heart of the LLS system is the authoring tool which facilities the ease and flexibility of designing various laboratory experiments which includes a number of pages, and each page has a number of steps with many draggable components. Furthermore, the proposed authoring tool is the first authoring tool that provides general and reusable virtual laboratory resource (VLR) for automatically managing laboratory software and hardware resources. To support the new VRL feature of the authoring tool, the LLS supports the ability to remotely control the laboratory equipment while performing laboratory experiments and also has the capability to run any type of simulation tool for virtually simulated labs. The proposed authoring tool is designed considering all the needed components with well-defined interfaces to achieve an effective and flexible Laboratory learning system. © 2022 IEEE.

A New Intelligent System for Evaluating and Assisting Students in Laboratory Learning Management System

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.

AI Virtual Assistant for Online Laboratory Experiments Based on Multi-threshold Technique and Genetic Algorithm for Analyzing the Student's Mouse Interaction Activities

There is a great interest in online learning systems, especially due to COVID-19 pandemic. However, there are a lot of limitations and challenges of online laboratory learning systems. This paper presents an efficient technique that provides an intelligent virtual tutor for online laboratory environment, as in engineering and science sectors. Based on the analysis of the student's mouse activities, the AI virtual Assistant or virtual tutor will automatically estimate the difficulties that the student stuck during conducting the steps of lab's experiment. Hence, the virtual tutor can assist the student, accordingly. The technique is based on multi-threshold that are used to discriminate different levels of difficulties. The values of these thresholds are estimated and optimized via the genetic algorithm. The experimental results show that discrimination between different student behaviors can be achieved accurately and efficiently. © 2022 IEEE.

Geofencing-based Congestion Control in Workplaces Environment using Sequential Pattern Mining

To control congestion in the workplace environment especially in crises like the COVID-19 pandemic, this requires careful control of highly crowded workplace locations. Therefore, innovative technologies, such as geofencing and sequential pattern mining can be used to estimate people movement pattern and combat the spread of COVID-19. In this paper, the workplace area is divided into a set of geofences by using geofencing technology. Then, the movement profiles of each user are estimated to control the possible congestion in the workplace's enviroment. To accomplish this, the user's historical geofence transitions are used to anticipate the next time the user will leave the current geofence. The Sequential Pattern Discovery using Equivalence classes (CM-SPADE), Succinct BWT-based Sequence prediction model (SuBSeq) and Compact Prediction Tree + (CPT+) algorithms are adopted to predict the user's next geofence. In the CM-SPADE algorithm, a vertical database is obtained from the available database and the frequent sequence is found based on relative support, confidence, and lift measures. Meanwhile, in the training phase of the SuBSeq algorithm, Ferragina and Manzini (FM)-index and Burrows-Wheeler Transform string are generated. Then, in the ready-to-predict phase, the next geofence is anticipated. The CPT+ algorithm is based on generating Prediction Tree (PT), Lookup Table (LT), and Inverted Index (IIdx) for the training data. Then, Frequent Subsequence Compression (FSC) and Simple Branches Compression (SBC) are used to reduce the size of the PT. In addition, the Prediction with improved Noise Reduction (PNR) method is utilized to reduce the execution time. The results show remarkable superiority for SuBSeq algorithm over CM-SPADE and CPT+ with the accuracy greater than 90% withh an average of 8 input geofences to predict the next geofence. © 2022 IEEE.

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

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.

Violation Detection Technique for COVID-19 Self-Isolation and Control Measures using Wireless and Geofencing Technologies

The spread of COVID-19 pandemic forced the governments to apply severe control measures such as lockdowns and quarantines. This lead to the need for innovative digital health technological solutions to increase the effectiveness of the necessary precautionary. In this regard, a smart geofencing solution is proposed in this paper to effectively support the implementation of COVID-19 self-isolation and control measures in different areas where each intended area is defined as a geofence with specific control actions. The proposed solution is based on a mobile client application that exploits specific wireless metrics for Wi-Fi and cellular networks to generate a digital signature for the defined geofence and detects any violation of its virtual borders. A detailed description along with a mathematical model of how the geofence digital signature can be created is presented. Then, a geofence matching criterion based on a weighted score is proposed to detect the geofence violation by comparing the periodically measured wireless metrics with the created geofence digital signature. The proposed solution can be used for detecting violation in different cases such as distributed home quarantine and controlled areas. The performance of the proposed solution is studied on different scenarios, and the results show the significance of the proposed solution compared to others. Moreover, the effect of the decision time window parameter on the performance of the proposed geofence matching criterion is illustrated. © 2022 IEEE.

Automatic Identification of Student's Cognitive Style from Online Laboratory Experimentation using Machine Learning Techniques

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.

Intelligent Virtual Tutor for Online Laboratory Experiments Based on Modelling the Student's Mouse Interaction Behavior

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.