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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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SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models

arXiv

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arXiv 2025年

作者： Duc, Chuc Man Fukui, Hiromichi Department of Computer Science Faculty of Information Technology University of Engineering and Technology Vietnam National University Hanoi Viet Nam International Digital Earth Applied Science Research Center Chubu University Aichi Kasugai487-8501 Japan

Foundation models refer to deep learning models pretrained on large unlabeled datasets through self-supervised algorithms. In the Earth science and remote sensing communities, there is growing interest in transforming the use of Earth observation data, including satellite and aerial imagery, through foundation models. Various foundation models have been developed for remote sensing, such as those for multispectral, high-resolution, and hyperspectral images, and have demonstrated superior performance on various downstream tasks compared to traditional supervised models. These models are evolving rapidly, with capabilities to handle multispectral, multitemporal, and multisensor data. Most studies use masked autoencoders in combination with Vision Transformers (ViTs) as the backbone for pretraining. While the models showed promising performance, ViTs face challenges, such as quadratic computational scaling with input length, which may limit performance on multiband and multitemporal data with long sequences. This research aims to address these challenges by proposing SatMamba, a new pretraining framework that combines masked autoencoders with State Space Model, offering linear computational scaling. Experiments on high-resolution imagery across various downstream tasks show promising results, paving the way for more efficient foundation models and unlocking the full potential of Earth observation data. The source code is available in https://***/mdchuc/HRSFM. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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A hybrid healthy diet recommender system based on machine learning techniques

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computers in Biology and Medicine 2025年 184卷 109389-109389页

作者： Sweidan, Sara Askar, S.S. Abouhawwash, Mohamed Badr, Elsayed Department of Artificial Intelligence Faculty of Computer and Artificial Intelligence Benha University Benha Egypt Faculty of Computer Science and Engineering New Mansoura University New Mansoura Egypt Department of Statistics and Operations Research College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia College of Engineering Michigan State University East LansingMI48824 United States Department of Mathematics Faculty of Science Mansoura University Mansoura35516 Egypt Department of Scientific Computing Faculty of Computer and Artificial Intelligence Benha University Benha Egypt Department of Information Systems College of Information Technology Misr University for Science & Technology Giza Egypt

Obesity is a chronic disease correlated with numerous risk factors that not only negatively affect all body functions but also increase the chances of developing chronic diseases and the associated morbidity and mortality rates. This study proposes a novel system that bridges the gap between healthcare providers and patients by offering both parties some tools for navigating the intricacies of dietary planning. In this system, machine learning techniques are used to determine the required calories before starting an obesity treatment. A hybrid precision model with minimal parameters is also developed to estimate the appropriate number of calories for losing weight and to formulate a healthy diet plan. A real dataset of 15 anthropometric measurements is analyzed using SVR, LR, and DTR regression models, and all the data are preprocessed before analysis to enhance model performance. Results show that the required calories can be estimated with a high correlation (R = 0.985) from independent measurements. The proposed model also calculates the healthy daily percentages of fats, proteins, and carbohydrates based on a knowledge base of medical rules and functions, thus facilitating the sequential treatment of obese patients. In sum, this study applies different models to design a practical, cost-effective approach for accurately determining the required calories and formulating valuable diet plans for obesity treatment and management. © 2024 Elsevier Ltd

关键词： Calorie Machine learning Regression Obesity treatment Expert system

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Classification Models and Variable Selection for SME Credit Risk Assessment in Balance the Data Set

Classification Models and Variable Selection for SME Credit ...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Sornthun Intharasompong Anuchit Jitpattanakul Sakorn Mekruksavanich Piyada Wongwiwat Wikanda Phaphan Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Computer Engineering School of Information and Communication University of Phayao Thailand Faculty of Science and Technology Suansunandha Rajabhat University Bangkok Thailand Research Group in Statistical Learning and Inference

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Small and medium-sized enterprises (SMEs) are frequently considered high credit risk, making it difficult to obtain loans from banks. A viable substitute for assessing borrower creditworthiness more impartially is using machine learning-based credit risk assessment (CRA) algorithms. This study evaluated the performance of five machine learning techniques: decision tree, support vector machine, gradient boosting, K-nearest neighbor, and Naïve Bayes. We evaluated each model with K-fold cross-validation and utilized the Stepwise selection method for the variable selection. To address the underlying data imbalance, we balanced the dataset with the SMOTE technique, which showed that normal loans were more widespread than Non-Performing Loans (NPLs). After balancing the data, the Gradient Boosting model displayed the best accuracy, F-measure, and robustness without variable selection, making it the most effective model for predicting SME loans.

关键词： Support vector machines Machine learning algorithms Input variables Predictive models Nearest neighbor methods Boosting Data models Robustness Bayes methods Risk management

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Early Detection of Childhood Malnutrition Using Survey Data and Machine Learning Approaches

Early Detection of Childhood Malnutrition Using Survey Data ...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Thoufiq Zumma Anikur Rahaman Nuzhat Noor Islam Prova Tasriful Haque Julias Caeser Joy Bose Ravina Akter Youki Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science Washington University Science and Technology New York USA Department of Seidenberg School of CSIS Pace University New York USA Master of Information Technology and Systems Victorian Institute of Technology Faculty of Artificial Intelligence and Science University of Wollongong Faculty of Engineering and IT University of Technology Sydney

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Children's physical, mental and emotional development depends heavily on sleep, with age-specific sleep needs fluctuating. Malnutrition may result from eating too little, absorbing nutrients poorly, being unwell, living in poverty or not knowing enough about nutrition. Food allergies and iron insufficiency are two examples of illnesses whose onsets might differ depending on environmental exposures and genetics. By providing proper diet, getting medical attention when necessary and encouraging healthy behaviors, parents play a crucial role in supporting their children's health. For children to grow and develop properly, they need meals high in fruits, vegetables, milk, protein, calcium, iron and fiber. Key advantages of a balanced diet include aiding bone formation, improving heart health and encouraging regular bowel motions and digestion. In general, children's general health and wellness depend on caregivers being aware of their dietary requirements and giving them the proper attention. The findings of the research imply that machine learning algorithms may be useful in understanding and predicting the prevalence of malnutrition in children. Furthermore, these algorithms could provide perceptive data that might help politicians create effective policies. In this research, we make use of five different machine learning algorithms. With a decision tree, the accuracy is 0.92%, followed by svm which provides 0.91%, KNN, which provides 0.84%, GBM classifier, which provides 0.79% and random forest, which provides 0.65%.

关键词： Support vector machines Deep learning Surveys Pediatrics Machine learning algorithms Predictive models Prediction algorithms Iron Decision trees Medical diagnostic imaging

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Climate change prediction in Saudi Arabia using a CNN GRU LSTM hybrid deep learning model in al Qassim region

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Scientific Reports 2025年第1期15卷 1-19页

作者： Elabd, Emad Hamouda, Hany Mohamed Ali, M. A. Mohamed Fouad, Yasser Department of Management Information Systems College of Business and Economics Qassim University Qassim Buraidah 51452 Saudi Arabia Department of Information Systems Faculty of Computers and Information Menoufia University Shebin El Kom Egypt Department of Mathematics Faculty of Science Qassim University Qassim Buraidah 51452 Saudi Arabia Department of Computer Science Faculty of Computers and Information Suez University P.O.Box:43221 Suez Egypt

Climate change, which causes long-term temperature and weather changes, threatens natural ecosystems and cities. It has worldwide economic consequences. Climate change trends up to 2050 are predicted using the hybrid model that consists of Convolutional Neural Network-Gated Recurrent Unit-Long Short-Term Memory (CNN-GRU-LSTM), a unique deep learning architecture. With a focus on Al-Qassim Region, Saudi Arabia, the model assesses temperature, air temperature dew point, visibility distance, and atmospheric sea-level pressure. We used Synthetic Minority Over-sampling Technique for Regression with Gaussian Noise (SMOGN) to reduce dataset imbalance. The CNN-GRU-LSTM model was compared to 5 classic regression models: DTR, RFR, ETR, BRR, and K-Nearest Neighbors. Five main measures were used to evaluate model performance: MSE, MAE, MedAE, RMSE, and R². After Min-Max normalization, the dataset was split into training (70%), validation (15%), and testing (15%) sets. The paper shows that the CNN-GRU-LSTM model beats standard regression methods in all four climatic scenarios, with R² values of 99.62%, 99.15%, 99.71%, and 99.60%. Deep learning predicts climate change well and can guide environmental policy and urban development decisions. © The Author(s) 2025.

关键词： Climate change Climate change prediction CNN-GRU-LSTM Convolutional neural network Hybrid model Long short-term memory

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ESARSA-MRFO-FS: Optimizing Manta-ray Foraging Optimizer using Expected-SARSA reinforcement learning for features selection

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Knowledge-Based systems 2025年 321卷

作者： AbdulAzeem, Yousry Magdy Balaha, Hossam Bamaqa, Amna Badawy, Mahmoud Elhosseini, Mostafa A. Zewail City of Science and Technology October Gardens 6th of October City Giza12578 Egypt Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Computers and Control Systems Engineering Department Mansoura University Faculty of Engineering Mansoura35516 Egypt Department of Computer Science and Information Applied College Taibah University Madinah42353 Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia

Disease prediction with the help of computers has achieved significant progress in this area;however, it still requires a more accurate identification of each data feature. In the past few years, ML-based medical diagnoses have become increasingly dependent on the datasets rather than the algorithms. Consequently, the more features in a dataset, the more difficult the model training process will be. A feature selection (FS) strategy can be used to eliminate unwanted features before the training in order to improve the model performance. Due to the great increase in the number of features, FS causes a complex combinatorial problem, which is NP-hard. The primary goal of this research is to create a novel framework for improving the Manta-ray Foraging Optimizer (MRFO) algorithm's performance. By the method of exploration–exploitation toggle adjustments, this process becomes the most efficient one by using the Expected State–action-reward-state–action (ESARSA) reinforcement learning strategy. The study employed 12 datasets as well as two classifiers (K-Nearest-Neighbor and Decision-Tree), both with and without hyperparameter adjustment. The study compared the performances of three feature selection methods: no feature selection, the original MRFO algorithm, and the proposed ESARSAMRFO-FS algorithm. The results revealed that ESARSA-MRFO-FS is the most effective of the three algorithms with classifiers and optimization scenarios. Besides, experiments comparing MRFO and ESARSA-MRFO performance on 44 benchmarks are presented in the article. In five functions, ESARSA-MRFO outperforms MRFO. These results confirm that the proposed method is efficient for feature selection in medical diagnosis because of its accuracy and low processing costs. © 2025 Elsevier B.V.

关键词： Nearest neighbor search

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Brain Function Assessment of EEG in Mild Cognitive Impairment using Timelag Analysis

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and systems 2025年第4期145卷 467-473页

作者： Tanabe, Akifumi Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Graduate School of Information Science University of Hyogo 7-1-28 Minatojima Minami Chuo-ku Hyogo Kobe650-0047 Japan Department of Medical Engineering Aino University 4-5-4 Higashi-Ohda Ibaraki Osaka567-0012 Japan D3 Center The University of Osaka 2-8 Yamadaoka Osaka Suita565-0871 Japan D3 Center The University of Osaka 5-1 Mihogaoka Ibaraki Osaka567-0047 Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Software and Information Technology Aomori University 2-3-1 Shachihata Aomori-shi Aomori030-0943 Japan

This research aimed to reveal the information process in the brain of the patients who suffered from mild cognitive impairment (MCI) using the power spectra and timelag analysis in electroencephalogram (EEG). The patients with MCI have memory or thinking problems, and are at a great risk of developing Alzheimer's disease or a related dementia. Ten patients with MCI and 13 healthy adults (non-MCI) were examined. The EEGs (122.88 seconds/subject) during a state of relaxation with the eyes closed, were analyzed using the Fourier analysis (power spectrum) and the timelag analysis. The results showed that the mean value of the power spectra in MCI was significantly higher than the mean value in non-MCI in the theta band. The mean value of the absolute timelag values (ATL) in MCI was significantly lower than the mean value in non-MCI in the frontal and left temporal areas. Our findings suggest that the features from EEG of MCI were abstracted in the theta band, and the propagation of information related to memory in brain would becoming fast in MCI. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Electroencephalography

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MyanSpeech: Joint CTC-Attention and RNN Language Model for End-To-End Read Speech Recognition

MyanSpeech: Joint CTC-Attention and RNN Language Model for E...

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computer Applications (ICCA), IEEE Conference on

作者： Sann Su Su Yee Win Lai Lai Phyu Win Pa Pa Moe Myint Yain Sai Win Maw Nyein Chan Faculty of Computer Systems and Technologies Polytechnic University (Myeik) Myeik Myanmar Faculty of Information Science Polytechnic University (Myeik) Myeik Myanmar Faculty of Computing Naypyitaw State Polytechnic University Naypyitaw Myanmar Myanmar Language and Literature Improvement Project (MLLIP) Naypyitaw Myanmar

ISBN: (数字)9798331534592

ISBN: (纸本)9798331534608

This paper proposed MyanSpeech 1 , an end-to-end Automatic Speech Recognition (ASR) system designed for the Myanmar language, exploiting a joint CTC-Attention architecture for the acoustic model in combination with a Recurrent Neural Network Language Model (RNN-LM). The integration of CTC-Attention addresses the challenge of aligning audio with text in Myanmar, effectively combining the strengths of Connectionist Temporal Classification (CTC) for stable framewise alignment with Attention mechanisms for handling longer dependencies and variable input lengths. This dual approach enhances recognition accuracy, particularly for low-resource languages. The RNN-LM further supports the model by capturing language-specific grammar and contextual relationships. The dataset includes 36 hours, 33 minutes, and 51 seconds of read speech from 20 males and 18 females speakers, comprising 35,468 utterances of daily conversation from the Linguistic Committee (LC). Among various configurations, the best models achieved a word error rate (WER) of 7.093% and character error rate (CER) of 4.474% on a speaker-dependent (closed test), and a WER of 26.829% and CER of 17.597% on a speaker-independent (open test), respectively. These results highlight the effectiveness of integrating CTC-Attention and RNN-LM for Myanmar ASR, offering a reliable solution for speech recognition in low-resource language.

关键词： Training Analytical models Recurrent neural networks Accuracy Error analysis Computational modeling Linguistics Encoding Time measurement Context modeling

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Study of New Generation Optical Networks with Regards to Type of Optical Amplifiers Connected in the Network 23

Study of New Generation Optical Networks with Regards to Typ...

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23rd Slovak-Czech-Polish Optical Conference on Wave and Quantum Aspects of Contemporary Optics

作者： Látal, Jan Šiška, Petr Hejduk, Stanislav Seyringer, Dana Dohnal, Fadi Chovan, Jozef Uherek, František Štípal, Jiří Trubák, Kamil Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. listopadu 2172/15 Ostrava-Poruba708 00 Czech Republic Research Centre for Microtechnology Vorarlberg University of Applied Sciences Hochschulstraße 1 Dornbirn6850 Austria Slovak Centre of Scientific and Technical Information International Laser Centre Ilkovičova 3 Bratislava841 04 Slovakia Institute of Electronics and Photonics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovičova 3 Bratislava812 19 Slovakia

ISBN: (数字)9781510688094

ISBN: (纸本)9781510688087

Nowadays, there is strong demand for high-bitrate systems that can provide and distribute large bandwidth while assuring minimal error rate and high-quality communication with regards to the implementation of cloud centres, new 6G networks and smart applications. This all leads to continuous dynamic development in the field of xPON networks bringing new solutions for future optical networks. The presented work focuses on NG-PON2 networks, which are represented by Time and Wavelength Division Multiplexing Passive Optical Network (TWDM-PON) (G.989.1) or Super-PON (IEEE P802.3cs). The basis of the introduced topology is in combination of wave and time access through the TWDM-PON network. In our simulation, we were focusing on the utilization of eight wavelengths operating in the C and L band with amplifiers implementation, namely Erbium Doped Fiber Amplifier (EDFA) or Semiconductor Optical Amplifier (SOA). Regarding to used amplifier the ODN (Optical Distribution Network) reach of TWDM-PON were studied as well as the bitrate of 10 Gbps per wavelength in both upstream and downstream directions with the help of parameters such as bit error rate (BER), Q-factor. From the simulation results, it is shown that by implementing an EDFA amplifier, a longer range of up to 60 km and a lower BER than that of SOA can be achieved. The proposed TWDM-PON system provides a communication network for urban as well as rural areas with guaranteed high bit rates and range. © 2025 SPIE.

关键词： Erbium doped fiber amplifiers

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