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Exploring students’ perceptions of the design and use of the Moodle learning management system

computers in Human Behavior Reports 2025年 18卷

作者： Al-Fraihat, Dimah Alshahrani, Abdulaziz M. Alzaidi, Maram Shaikh, Aijaz A. Al-Obeidallah, Mohammed Al-Okaily, Manaf Department of Software Engineering Faculty of Information Technology Isra University Amman 11622 Jordan Information Technology Department Faculty of Computer and Information Systems Islamic University of Madinah Saudi Arabia Department of Computer Science College of Computers and Information Technology Taif University Taif Saudi Arabia The Institute of Information and Computational Technologies Kazakhstan Department of Computer Science and Information Technology Abu Dhabi University Abu Dhabi United Arab Emirates School of Business Irbid Jadara University Jordan

Universities have been utilizing various synchronous and asynchronous methods embedded in digital learning tools to continue the teaching-learning process. Among these tools, known as learning management systems (LMSs), Moodle holds a distinct position due to its scalability, user-friendliness, and ease of use. However, despite its widespread adoption, there is a lack of focused research evaluating factors that influence Moodle adoption and usage in university environments, particularly in developed countries. This lack of investigation is critical because even minor usability shortcomings can lead to reduced student engagement, lower satisfaction, and hindered educational outcomes. Therefore, there is a pressing need to explore students' experiences and feedback regarding Moodle's design, layout, and overall usability to enhance its effectiveness and better align with contemporary educational demands. In this study, we collected students' insights regarding the design and layout features of the Moodle system and whether these features play a decisive role in the adoption or discontinuation of the Moodle LMS. An open-ended questionnaire was used to collect the opinions of 103 university students who were enrolled in several courses conducted online using Moodle as the primary LMS. Qualitative data analysis was conducted and seven major themes related to the factors influencing the success of the Moodle LMS were identified. Key findings reveal that usability issues, such as system features and content design quality, significantly hinder student experiences, while positive aspects, such as overall satisfaction and usefulness, highlight areas of strength. The implications for educators, system administrators, and policymakers are discussed, along with limitations and future research directions. This study not only addresses a critical gap in the literature but also offers practical insights for enhancing LMS effectiveness in e-learning environments. © 2025 The Authors

关键词： E-Learning success Higher education Information system success model (ISSM) Moodle learning management system (moodle LMS) Qualitative method Student perceptions

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Harnessing optimization with deep learning approach on intelligent transportation system for anomaly detection in pedestrian walkways

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Scientific Reports 2025年第1期15卷 1-28页

作者： Alotaibi, Sultan Refa Alrayes, Fatma S. Maashi, Mashael Maray, Mohammed Alliheedi, Mohammed A. Badawood, Donia Alotaibi, Moneerah Department of Computer Science College of Science and Humanities Dawadmi Shaqra University Shaqra Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University PO Box 84428 Riyadh 11671 Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University PO Box 103786 Riyadh 11543 Saudi Arabia Department of Information Systems College of Computer Science King Khalid University Abha Saudi Arabia Department of Computing Faculty of Computing and Information Al-Baha University Al Baha Saudi Arabia Department of Data Science School of Computers Umm Al-Qura University Makkah Saudi Arabia

Anomaly Detection (AD) in pedestrian walkways is significant in urban safety and security methods. It is generally employed for perceiving unusual or abnormal situations, behaviours, or actions in regions devoted to pedestrian traffic, like pedestrian bridges, sidewalks, or crosswalks. The main aim is to enhance security, efficiency, and safety in the urban environment by classifying changes and observing the activities of pedestrians from recognized models. This type of AD usually has sensors, surveillance cameras, and advanced software methods. By applying innovative computer vision (CV) and machine learning (ML) methods, this approach always examines the pedestrian area to detect potential anomalies and attacks. Deep learning (DL) aided AD in pedestrian walkways, which displays a new and very effective method to progress safety and security in urban environments. Therefore, this study designs a novel Harnessing Optimization with a DL Approach on an Intelligent Transportation System with Anomaly Detection in Pedestrian Walkways (HODLAITS-ADPW) method. The presented HODLAITS-ADPW method utilizes the CV and DL process to detect pedestrians accurately. To attain this, the HODLAITS-ADPW method performs image pre-processing using Median Filtering to enrich the quality of input frames. Next, the HODLAITS-ADPW method utilizes an enhanced YOLOv7 object detector for the object’s detection in the frame. Besides, the Chameleon Swarm Algorithm is employed for the optimum hyperparameter tuning method. The HODLAITS-ADPW method uses an Attention Pyramid Convolutional Neural Network model for anomaly recognition. The experimental values of the HODLAITS-ADPW technique were certified on the pedestrian dataset, and the outcomes can be examined in numerous aspects. The empirical findings reported better outcomes for the HODLAITS-ADPW technique when compared to other models. © The Author(s) 2025.

关键词： Chameleon swarm algorithm Intelligent transportation system Median filtering Pedestrian walkway YOLOv7

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Emergency Vehicle Navigation in Connected Autonomous systems Using Enhanced Traffic Management System

引用

IEEE Internet of Things Journal 2025年

作者： Raja, Gunasekaran Anbalagan, Sudha Gurumoorthy, Sugeerthi Jegathesan, Darshini Girivel, Niveditha Subramanian Sundaram, Varsha Mani Shanmuga Khowaja, Sunder Ali Dev, Kapal Anna University NGNLab Department of Computer Technology MIT Campus Chennai600044 India Vellore Institute of Technology Chennai Centre for Smart Grid Technologies School of Computer Science and Engineering Chennai600127 India Citicorp Services Private Ltd Software Development Chennai600113 India Dublin City University School of Computing Faculty of Engineering and Computing Glasnevin Campus DublinD09 V209 Ireland Munster Technological University Department of Computer Science Bishopstown CorkT12 P928 Ireland University of Johannesburg Department of Institute of Intelligent Systems Johannesburg2006 South Africa Chitkara University Institute of Engineering and Technology Centre for Research Impact Outcome Chitkara University Punjab Rajpura140401 India

Autonomous Vehicle (AV) usage has become predominant in the rapidly evolving landscape of urban transportation. Integrating AVs and non-AVs in the existing traffic infrastructure has significantly increased the complexity of traffic patterns. This research work primes the enhanced Traffic Management System (e-TMS), a solution implemented to expedite Emergency Vehicle (EV) travel in the context of Connected AVs (CAVs) and ensured secured communication employing Public Key Infrastructure (PKI) among the Internet of Vehicles (IoV) framework. When an EV is detected, the IoV system verifies the EV's signal using PKI, ensuring its authenticity and integrity by encrypting the communication between the EV and Road Side Units (RSUs). Further, the e-TMS activates the platooning process where CAVs in the EV's lane shift to the adjacent lanes and dynamically form platoons to create a dedicated lane for the EV. To further optimize the platooning process, an Adaptive Smart Leader Selection (ASLS) algorithm is employed to select a leader vehicle spontaneously among the AVs based on the proximity to the EV, communication reliability, and lane position. Radio Detection and Ranging devices aid this platooning process by providing distance and target velocity, which are needed to maintain the required distance between the AVs and ensure safe platoon formation. The e-TMS enhances EV response times and overall traffic flow efficiency and resulted in 27.8% increase in mean speed compared to the traditional methods. © 2014 IEEE.

关键词： Emergency vehicles

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DESIGN OF MECHANISMS FOR ENSURING THE EXECUTION OF TASKS IN PROJECT PLANNING

arXiv

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arXiv 2025年

作者： Mulesa, Oksana Horvat, Petro Radivilova, Tamara Sabadosh, Volodymyr Baranovskyi, Oleksii Duran, Sergii Department of Software Systems Uzhhorod National University Narodna sq. 3 Uzhhorod88000 Ukraine Department of Computer Systems and Networks Uzhhorod National University Narodna sq. 3 Uzhhorod88000 Ukraine V. V. Popovskyy Department of Infocommunication Engineering Kharkiv National University of Radio Electronics Nauky ave. 14 Kharkiv61166 Ukraine Intellias Company Kapushanska str. 177 А Uzhhorod88000 Ukraine Department of Computer Science Blekinge Institute of Technology Blekinge Karlskrona37179 Sweden

This paper reports an analysis of aspects of the project planning stage. The object of research is the decision-making processes that take place at this stage. This work considers the problem of building a hierarchy of tasks, their distribution among performers, taking into account restrictions on financial costs and duration of project implementation. Verbal and mathematical models of the task of constructing a hierarchy of tasks and other tasks that take place at the stage of project planning were constructed. Such indicators of the project implementation process efficiency were introduced as the time, cost, and cost-time efficiency. In order to be able to apply these criteria, the tasks of estimating the minimum value of the duration of the project and its minimum required cost were considered. Appropriate methods have been developed to solve them. The developed iterative method for assessing the minimum duration of project implementation is based on taking into account the possibility of simultaneous execution of various tasks. The method of estimating the minimum cost of the project is to build and solve the problem of Boolean programming. The values obtained as a result of solving these problems form an «ideal point», approaching which is enabled by the developed iterative method of constructing a hierarchy of tasks based on the method of sequential concessions. This method makes it possible to devise options for management decisions to obtain valid solutions to the problem. According to them, the decision maker can introduce a concession on the value of one or both components of the «ideal point» or change the input data to the task. The models and methods built can be used when planning projects in education, science, production, *** Codes 90B50 Copyright © 2025, The Authors. All rights reserved.

关键词： Efficiency

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Lab Color Model and Contrast Modification as a Tool for Improving the Quality of Medical Images

Lab Color Model and Contrast Modification as a Tool for Impr...

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Amer Abu-Jassar Mohammad Hamdan Kawther Hamash Sardor Boboyorov Vyacheslav Lyashenko Department of Computer Science College of Information Technology Amman Arab University Amman Jordan College of software engineering Amman Arab University Amman Jordan WellStar school of nursing Kennesaw state university Georgia USA Tashkent Medical Academy Termiz branch Termiz Uzbekistan Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv Ukraine

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper addresses the challenge of enhancing the quality of medical data interpretation, specifically using color microscopic medical images for effective visualization and further analysis. The method employed involves increasing contrast by adjusting the brightness intensity values within the Lab color model, which also considers the reference white point for horizontal light. To determine the parameters for the contrasting procedure, two strategies for selecting gamma correction of the input image are explored, based on the dynamics of the resulting image quality for various no-reference quality metrics. By analyzing these dynamics, the choice of correction conditions for the contrasting procedure can be justified, rather than relying on static metrics. The paper investigates the application of these proposed strategies on different types of images, presents a formal generalization of the strategies for selecting correction parameters, and offers practical recommendations for their use.

关键词： Measurement Image quality Visualization Image color analysis Microscopy Computational modeling Data visualization Quality assessment Computational intelligence Biomedical imaging

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Dynamic IoT Protocol Adaptation for Smart Home systems with Diverse Device Ecosystems

Dynamic IoT Protocol Adaptation for Smart Home Systems with ...

引用

International Conference on Trends in Electronics and Informatics (ICEI)

作者： P. Devisri Bhavana Jamalpur Stanly Felix Christian Raj Keerthika Velusamy Ashok Kumar V B. Jegajothi CSE Ramco Institute of Technology Rajapalayam Tamil Nadu Department of CS & AI SR University Warangal Software Systems Coimbatore Institute of Technology peelamedu Civil Aerodrome post Coimbatore 14 Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu SRS Tech Solutions Chennai

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

The swift proliferation of IoT devices in smart home ecosystems has resulted in notable interoperability issues due to the presence of numerous conflicting communication protocols, such as Zigbee, Z-Wave, MQTT, CoAP, Bluetooth, and Wi-Fi. Current solutions frequently depend on static protocol mapping, which proves inadequate for the dynamic nature of smart home environments where network conditions and device needs are in constant flux. In this paper, we introduce the Dynamic IoT Protocol Adaptation Framework (DIP A), which merges Reinforcement Learning (RL) with Multi-Armed Bandit (MAB) optimization to facilitate real-time, adaptive protocol selection. DIPA leverages edge computing for predictive decision-making and incorporates Graph Neural Networks (GNN) to assess protocol compatibility. A middleware-based protocol translation layer guarantees seamless integration across varied IoT systems. We assess the framework using a sandbox populated with actual IoT data to evaluate performance concerning adaptability, latency, energy consumption, and scalability. The findings indicate that DIP A diminishes latency by 35% and enhances energy efficiency by 28% when compared to static adaptation approaches. The proposed framework augments interoperability, optimizes resource management, and fosters flexible communication within smart home environments.

关键词： Protocols Translation Scalability Smart homes Reinforcement learning Graph neural networks Internet of Things Interoperability Optimization Electronics packaging

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DECODING EEG SPEECH PERCEPTION WITH TRANSFORMERS AND VAE-BASED DATA AUGMENTATION

arXiv

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arXiv 2025年

作者： Chen, Terrance Yu-Hao Chen, Yulin Soederhaell, Pontus Agrawal, Sadrishya Shapovalenko, Kateryna Computer Science Department Carnegie Mellon University PittsburghPA15213 United States Information Networking Institute Carnegie Mellon University PittsburghPA15213 United States Computational Finance Carnegie Mellon University PittsburghPA15213 United States Software and Societal Systems Department Carnegie Mellon University PittsburghPA15213 United States Language Technologies Institute Carnegie Mellon University PittsburghPA15213 United States

Decoding speech from non-invasive brain signals, such as electroencephalography (EEG), has the potential to advance brain-computer interfaces (BCIs), with applications in silent communication and assistive technologies for individuals with speech impairments. However, EEG-based speech decoding faces major challenges, such as noisy data, limited datasets, and poor performance on complex tasks like speech perception. This study attempts to address these challenges by employing variational autoencoders (VAEs) for EEG data augmentation to improve data quality and applying a state-of-the-art (SOTA) sequence-to-sequence deep learning architecture, originally successful in electromyography (EMG) tasks, to EEG-based speech decoding. Additionally, we adapt this architecture for word classification tasks. Using the Brennan dataset, which contains EEG recordings of subjects listening to narrated speech, we preprocess the data and evaluate both classification and sequence-to-sequence models for EEG-to-words/sentences tasks. Our experiments show that VAEs have the potential to reconstruct artificial EEG data for augmentation. Meanwhile, our sequence-to-sequence model achieves more promising performance in generating sentences compared to our classification model, though both remain challenging tasks. These findings lay the groundwork for future research on EEG speech perception decoding, with possible extensions to speech production tasks such as silent or imagined *** Codes 68T07, 92C55 Copyright © 2025, The Authors. All rights reserved.

关键词： Brain computer interface

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Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading

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IEEE Transactions on Mobile Computing 2025年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J. P. C. University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Zhengzhou University School of Electrical and Information Engineering Henan International Joint Laboratory of Intelligent Health Information System National Telemedicine Center National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Nottingham Trent University Department of Engineering NottinghamNG1 4FQ United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Nanjing University of Information Science and Technology School of Software Nanjing210044 China Amazonas State University Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC system. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU's utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO algorithm, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 86.88%, increasing the EU utility function value by 0.13%, and decreasing the variance of results by 96.78%. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

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MedCoDi-M: A Multi-Prompt Foundation Model for Multimodal Medical Data Generation

arXiv

引用

arXiv 2025年

作者： Molino, Daniele Feola, Francesco Di Faiella, Eliodoro Fazzini, Deborah Santucci, Domiziana Shen, Linlin Guarrasi, Valerio Soda, Paolo Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Roma Italy Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden Department of Diagnostic Imaging and Stereotactic Radiosurgey Centro Diagnostico Italiano S.p.A. Milano Italy Department of Radiology and Interventional Radiology Fondazione Policlinico Universitario Campus Bio-Medico Rome Italy Research Unit of Radiology and Interventional Radiology Department of Medicine and Surgery Università Campus Bio-Medico di Roma Rome Italy College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Artificial Intelligence is revolutionizing medical practice, enhancing diagnostic accuracy and healthcare delivery. However, its adaptation in medical settings still faces significant challenges, related to data availability and privacy constraints. Synthetic data has emerged as a promising solution to mitigate these issues, addressing data scarcity while preserving privacy. Recently, Latent Diffusion Models have emerged as a powerful tool for generating high-quality synthetic data. Meanwhile, the integration of different modalities has gained interest, emphasizing the need of models capable of handle multimodal medical data. Existing approaches struggle to integrate complementary information and lack the ability to generate modalities simultaneously. To address this challenge, we present MedCoDi-M, a 6.77-billion-parameter model, designed for multimodal medical data generation, that, following Foundation Model paradigm, exploits contrastive learning and large quantity of data to build a shared latent space which capture the relationships between different data modalities. Further, we introduce the Multi-Prompt training technique, which significantly boosts MedCoDi-M’s generation under different settings. We extensively validate MedCoDi-M: first we benchmark it against five competitors on the MIMIC-CXR dataset, a state-of-the-art dataset for Chest X-ray and radiological report generation. Secondly, we perform a Visual Turing Test with expert radiologists to assess the realism and clinical relevance of the generated data, ensuring alignment with real-world scenarios. Finally, we assess the utility of MedCoDi-M in addressing key challenges in the medical field, such as anonymization, data scarcity and imbalance learning. The results are promising, demonstrating the applicability of MedCoDi-M in medical contexts. Project page is at https://***/MedCoDi-M/. © 2025, CC BY-NC-ND.

关键词： Contrastive Learning

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Business Process Remaining Time Prediction Based on Incremental Event Logs

引用

IEEE Transactions on Services Computing 2025年

作者： Guo, Na Liu, Cong Mo, Qi Cao, Jian Ouyang, Chun Lu, Xixi Zeng, Qingtian Shandong University of Technology School of Electrical and Electronic Engineering Zibo255000 China Nova University of Lisbon NOVA Information Management School Lisbon1070-312 Portugal Shandong University of Technology School of Computer Science and Technology Zibo255000 China Yunnan University School of Software Kunming650091 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200030 China Queensland University of Technology School of Information Systems BrisbaneQLD4000 Australia Utrecht University Department of Information and Computing Sciences CS Utrecht3584 Netherlands Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266590 China

Predictive Process Monitoring (PPM) aims to predict the future state of running process instances to enable timely interventions to mitigate potential risks. As one of the most fundamental tasks in PPM, process remaining time prediction focuses on preventing timeout occurrences. While various deep learning-based approaches have been developed for this purpose, they often rely on pre-established static prediction models and struggle to maintain accurate predictions when the process undergoes dynamic changes, such as an expanding sales channels. To tackle this challenge, this paper proposes an incremental process remaining time prediction framework by continuously updating the prediction model based on an incremental event log. Specifically, a feature selection strategy is first introduced to extract effective features from event logs. Leveraging effective features can significantly improve the prediction quality by capturing the changes in process information. Then, three incremental log-based updating mechanisms, including period-based updating, quantity-based updating, and concept-drift-based updating, along with a reconstruction strategy, are proposed to dynamically adjust the prediction model in response to business changes. Finally, LSTM, Transformer, and Auto-encoder models are adapted and integrated into the proposed framework. The approach has been implemented and publicly released. Experimental evaluation using nine real-life event logs demonstrate that the proposed framework and its three instantiations (i.e., LSTM-based, Transformer-based, and Auto-encoder-based ones) outperform state-of-the-art techniques in terms of prediction accuracy. © 2008-2012 IEEE.

关键词： incremental event log Predictive process monitoring process mining remaining time prediction

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