Extraction, Validation, And Evaluation Of Motivational Tactics Rules In A Web-Based Intelligent Tutoring System (WITS)

Kajian ini memberi tumpuan terhadap cara menlestarikan serta meningkatkan motivasi pelajar semasa proses pembelajaran dalam persekitaran Sistem Pentutoran Cerdas Berasaskan Web (Web-Based Intelligent Tutoring System, WITS) The current study focuses on finding a way to sustain or enhance the learners’ motivation during the learning process within a Web-Based Intelligent Tutoring System (WITS) environmen

Students' Performance Prediction Using Machine Learning Based on Generative Adversarial Network

5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023 -- 8 June 2023 through 10 June 2023 -- Istanbul -- 190025Predicting student performance is a crucial area of research in the field of education. To improve the accuracy and reliability of student performance prediction, machine learning (ML) techniques have been widely used. In this study, we propose a novel approach for predicting student performance using five ML techniques, which include data analysis, pre-processing techniques, and data augmentation using GAN. We evaluate the proposed approach using a real-world dataset of student academic records and compare the results to those obtained without data augmentation. Our findings demonstrate that data augmentation significantly improves the accuracy and reliability of student performance prediction. Specifically, the random forest classifier achieves the best accuracy of 99.8%. This research contributes to the field of education by providing a more comprehensive and accurate model for predicting student performance, which can support informed decision-making and improve educational outcomes. © 2023 IEEE

Detection of Chronic Diseases Based on the Principles of Deep and Machine Learning

4th International Scientific Conference of Alkafeel University, ISCKU 2022 -- 20 December 2022 through 21 December 2022 -- Al-Najaf Al-Ashraf -- 195756Continuing care is referred to as a chronic disease. The most widespread and expensive medical illnesses worldwide are chronic diseases. Chronic diseases can result in hospitalization, long-term impairment, worse quality of life, and even death. These conditions include cancer, diabetes, hypertension, stroke, heart disease, respiratory conditions, and kidney diseases. In reality, the greatest cause of mortality and disability worldwide is chronic illnesses. In this paper, we present deep-based and machine-based models to diagnose chronic diseases, this system includes several stages, namely the stage of data pre-processing and the stage of disease detection, which is carried out in two ways, the first depending on a deep Convolution Neural Network (CNN) and the second based on five machine learning algorithms: Stochastic Gradient Descent (SGD), Naïve Bayes (NB), K-Nearest Neighbor (KNN), Logistic Regression (LR), and Decision Tree (DT). The proposed model works on three data sets, namely (Pima Indians Diabetes Dataset, Cardiovascular Disease dataset, and UCI Heart Disease Data) to classify heart, diabetes, and kidney diseases. The experimental results proved the capability of the suggested system to classify the aforementioned diseases with an ideal accuracy of 100% using the CNN in the first model, and an accuracy of 94% in the second model using the SGD and LR algorithms. © 2023 American Institute of Physics Inc.. All rights reserved

AN INTELLIGENT TUTORING SYSTEM TO MAINTAIN THE STUDENTS' MOTIVATION

7th International Conference on Smart City Applications, SCA 2022 -- 19 October 2022 through 21 October 2022 -- Castelo Branco -- 185014Recently, many educational institutions around the world has transformed to online education specially during the COVID-19 pandemic. This fast and in many cases unplanned transformation leads to the needs for more researches to find solutions for the problems of this rapid transformation. As it's the best economic options during this pandemic, this study focused on creating a web-based (asynchronous system) intelligent tutoring system (ITS) to support the teachers in the C programming language course. Nonetheless, the suggested system takes into consideration one of the biggest challenges for asynchronous system which is how to maintain the students' motivation for the entire learning process. Therefore, the current study suggested the use of an interactive ITS as a solution for this challenge. The created system C-ITS used a set of motivational state rules and tactics to assess and maintain the motivation of the students. Finally, after using the system by the students and the teachers for two weeks, we conducted an evaluation study to evaluate the quality of the system design, the usability, the functionality, the compatibility. The result of the evaluation study showed that C-ITS system acceptable from both the students and the teachers. © 2022 International Society for Photogrammetry and Remote Sensing. All rights reserved

Caching Strategies for the Metaverse: Taxonomy, Open Challenges, and Future Research Directions

The metaverse, which is considered to be the next evolutionary stage of the Internet, has captured the attention of both academia and industry. Its primary goal is to establish a shared 3D virtual space that interconnects all virtual worlds through the Internet. In this shared space, users are represented as digital avatars, enabling them to communicate, interact with each other, and engage with the virtual environment as if they were in the physical world. However, realizing the full potential of the metaverse poses significant challenges, such as the requirement for higher throughput compared to current social VR platforms and the need to minimize latency to just a few milliseconds to uphold a truly immersive user experience. Caching is a critical aspect of optimizing data access on the current Internet, and it is equally crucial for addressing similar challenges in Web 3.0 and the metaverse. This paper explores different caching strategies suggested to address these challenges on the current Internet and assesses their potential relevance to the metaverse. Caching strategies are categorized into three groups: web caching, mobile caching, and Internet of Things (IoT) caching. Recent solu-tions are then examined to determine their relevance to the metaverse. Finally, the paper discusses open research challenges and potential future research directions in this domain. © 2024 by the authors of this article.National Natural Science Foundation of China, NSFC; National Research Foundation of Korea, NR

TOWARDS WEBCAM-BASED FACE DIRECTION TRACKING TO DETECT LEARNERS' ATTENTION WITHIN ASYNCHRONOUS E-LEARNING ENVIRONMENT

Abstract. Recently, as a consequence of COVID-19 pandemic, the delivery of education at most of the educational institutions depended mainly on e-learning. So, the researchers give more attention for both synchronous and asynchronous e-learning. Although from an economical perspective, asynchronous e-learning seems to be the best e-learning option for institutions, still one of the biggest challenges is how to keep learners motivated for the entire learning process. One of important motivational factors that drives the success of the learning process is the learner attention. Therefore, to retain the learners' attention during the asynchronous e-learning process, we need first to detect their loss of attention. Accordingly, more studies started to focus on detecting learners’ attention. However, those studies can't be widely used for attention detection within asynchronous e-learning environments, as the used approaches tend to be inaccurate, and complex for the design and maintain. In contrast, in this study, we explore the possibility to find a simple way that can be widely used to detect learners' attention within the asynchronous e-learning environments. Therefore, we used webcams which are available in almost every laptop, and computer vision tools to detect learners' attention by tracking their faces. Thereafter, we evaluated the accuracy of our suggested method, the result of this evaluation showed that our method is efficient. </jats:p

Caching Strategies for the Metaverse: Taxonomy, Open Research Challenges, and Future Directions

The metaverse, which is considered to be the next evolutionary stage of the Internet, has captured the attention of both academia and industry. Its primary goal is to establish a shared 3D virtual space that interconnects all virtual worlds through the Internet. In this shared space, users are represented as digital avatars, enabling them to communicate, interact with each other, and engage with the virtual environment as if they were in the physical world. However, realizing the full potential of the metaverse poses significant challenges, such as the requirement for higher throughput compared to current social VR platforms and the need to minimize latency to just a few milliseconds to uphold a truly immersive user experience. Caching is a critical aspect of optimizing data access on the current Internet, and it is equally crucial for addressing similar challenges in Web 3.0 and the metaverse. This paper explores different caching strategies suggested to address these challenges on the current Internet and assesses their potential relevance to the metaverse. Caching strategies are categorized into three groups: web caching, mobile caching, and Internet of Things (IoT) caching. Recent solutions are then examined to determine their relevance to the metaverse. Finally, the paper discusses open research challenges and potential future research directions in this domain