Learning System Research Articles

Implementation of a Personalized Adaptive Mobile Learning System

A notable obstacle in the field of education is the restricted flexibility of traditional teaching approaches. These approaches frequently take a consistent stance, disregarding the wide range of learning preferences that pupils possess. This leads to a decrease in student motivation and engagement, which in turn produces below-average learning outcomes. This research focuses on creating an adaptive learning system that classifies learners using the Felder-Silverman model in order to overcome these problems. After then, this system creates customized recommendations based on user choices in an effort to improve learning results. In order to keep enhancing the system's efficacy, the study have also included a feedback mechanism and performance evaluation.

E-learning application in immersive music entertainment teaching system based on genetic network algorithm

The problem of traditional music teaching is that students' interest and participation in learning are not high, which leads to the decline of teaching effect. Therefore, this study proposes an e-learning approach based on genetic network algorithms, aiming to enhance students' learning experience and engagement through an immersive music entertainment teaching system. The research has established an immersive music entertainment teaching system, which combines virtual reality technology to provide an immersive music learning environment. A genetic network algorithm is then used to optimize the e-learning modules in the system to improve the personalization and interactivity of the learning process. By collecting students' learning data and feedback information and using it as input to genetic network algorithms to update the parameters and structures in the learning module. The results show that e-learning based on genetic network algorithm has achieved remarkable results in an immersive music entertainment teaching system. When using the system for music learning, students showed higher enthusiasm and investment in learning, and achieved better academic performance. Students' experience and satisfaction with the system were also improved.

10.55041/ISJEM01502

The proposed solution for detecting false news involves a combination of Natural Language Processing (NLP) techniques, Reinforcement Learning (RL), and blockchain technology. The process begins with the collection of a comprehensive dataset of news articles and their associated metadata, followed by NLP-based pre-processing to clean and tokenize the text. Relevant features, such as word frequencies and readability, are then extracted and used to train an RL agent. The agent is trained to distinguish between true and false news using a reward and punishment system for learning. Once trained, the RL agent can classify new articles as true or false based on their extracted features. Although the potential role of blockchain technology is mentioned, further elaboration is required. This innovative approach is aimed at combating the dissemination of false information and misinformation in the digital news. Keywords: Natural Language Processing (NLP), Block chain, Fake Media

OF-DFN: Optical flow prediction network for different perspective image fusion

Currently, non-decision-level image fusion algorithms require extremely high registration precision of the images to be fused. In the face of different perspective image fusion scenarios, traditional feature registration algorithms and learning-based methods have poor robustness and are unsuitable for large image differences because of the Registration-Fusion separation. In addition, the lack of relevant datasets also hinders the development of different perspective image fusion methods. Given the above problems, we collect 5000 sets of different perspective RGB-MONO datasets in multiple scenes for raw data support. We present an end-to-end learned system for fusing two different perspective photographs into a chosen target view. The cascaded feature extraction based on encoder–decoder structure enables learning optical flow at different feature levels systematically. Then the optical flow module enables the image to be continuously registered and optimized during the fusion process, thus avoiding the deviations introduced by non-end-to-end algorithms. Extensive quantitative and qualitative experiments demonstrate that our proposed system can effectively fuse images from different perspectives in our self-built dataset. Compared with non-end-to-end fusion, our method provides superior performance in several fusion evaluation indicators.

A perspective on automated rapid eye movement sleep assessment.

Rapid eye movement sleep is associated with distinct changes in various biomedical signals that can be easily captured during sleep, lending themselves to automated sleep staging using machine learning systems. Here, we provide a perspective on the critical characteristics of biomedical signals associated with rapid eye movement sleep and how they can be exploited for automated sleep assessment. We summarise key historical developments in automated sleep staging systems, having now achieved classification accuracy on par with human expert scorers and their role in the clinical setting. We also discuss rapid eye movement sleep assessment with consumer sleep trackers and its potential for unprecedented sleep assessment on a global scale. We conclude by providing a future outlook of computerised rapid eye movement sleep assessment and the role AI systems may play.

Design of an English vocabulary e-learning recommendation system based on word bag model and recurrent neural network algorithm

This article takes learning English vocabulary as the foundation, constructs precise classification strategies, and designs a recommendation system for English vocabulary learning. This article constructs a recommendation system for English vocabulary learning based on the word bag calculation model and recursive neural network calculation method, which has great significance, and also conducts in-depth research on this system. Thus, a classification calculation method based on the word bag calculation model was proposed. The calculation method in this paper is different from traditional calculation methods, and the classification accuracy in this paper is higher. Meanwhile, recurrent neural networks themselves have one or more feedback loops that can feed back information from the neural network to other neural networks, resulting in recurrent networks with different structures. After a series of research results, it has been found that the SIFT method takes longer to extract than the SURF method, and the number of extracted features is relatively large. Therefore, the recommendation system for English vocabulary learning proposed in this article can be improved according to the different needs of users, in order to better meet their individual requirements.

The Analysis Of Bela H. Banathy’s Model And Its Relevance To Islamic Education Learning In Schools

Learning Islamic religious education is an essential part of achieving national education goals. Meanwhile, choosing a learning model is important in achieving success in Islamic Religious Education. This research aims to reveal how applying the Bela H. Banathy learning model in Islamic religious education can improve the quality of learning. This qualitative research uses a literature study approach. The data analysis method uses descriptive analysis methods to reveal the application of the learning model developed in 1968 by Bela H. Banathy in learning Islamic religious education in schools. The research results show that the Bela H. Banathy learning system is relevant to learning Islamic religious education. This model uses an approach oriented towards student learning outcomes by developing the concept of a spiral or learning cycle. Implementation of this model is carried out through six stages, namely: (1) formulating goals, (2) developing tests, (3) analyzing learning activities, (4) designing a teaching system, (5) carrying out test development activities, (6) and making improvements based on Banathy's evaluation results. The Banathy model has advantages in formulating goals according to student abilities and a structured and measurable learning system. This model can provide meaningful learning experiences, improve critical thinking skills and creativity, and use a holistic learning approach. On the other hand, weaknesses include the need for more steps in designing learning and the tendency to ignore previously learned knowledge. Keywords: Learning Model, Bela H. Banathy, Islamic Religious Education

Prototype for Digitizing the Sato Kewan Fairy Tale as A Javanese Language Education Media

This research aims to develop and validate the digitalization product of the Sato Kewan fairy tale as a Javanese language educational medium for elementary school students. This research uses the development method (Research and Development) with the ADDIE model to design a learning system. This development model goes through five steps: Analysis, Design, Development, Implementation, and Evaluation. The validation questionnaire for linguists and media experts is a research instrument. Data analysis techniques include qualitative and quantitative descriptive analysis. Based on data from validity results by linguists and media experts on the stop motion animation video of Sato Kewan's fairy tale, the media is a suitable criterion for use. For the language aspect, the language expert scored 75%, and the content aspect was 80%. The visual aspect got 75%, the audio aspect 83%, and 83% effectiveness.

Школа Д.Б.Эльконина—В.В.Давыдова: от истории к перспективам

<p>The article is an introdution to the project “A Model of a Modern School Based on the System of Developmental Education of D.B.Elkonin<span style="font-size: 11.0pt; font-family: 'Times New Roman','serif'; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: RU; mso-fareast-language: RU; mso-bidi-language: AR-SA; mso-no-proof: yes;">—–</span>V.V.Davydov”. It was presented to the Presidium of the Russian Academy of Education in 2013. Based on more than half a century experience in the development and implementation of a developmental learning system in the practice of experimental Moscow school 91, the authors prove the relevance of the project in solving the basic problems of modern education. The orientation of the developmental learning system on the principles of activity theory provides an opportunity to achieve high meta-subject results. A retrospective analysis of the stages of the formation of the developmental learning system allows not only to confirm the importance of its fundamental theoretical provisions, but also to identify points of growth,to determine the prospects for further development. The article presents a list of problems, which must be solved in order to achieve an effective implementation of developmental learning in modern educational practice. There is a detailed list of practical content (textbooks, manuals, methodological recommendations, workbooks) created by the authors of educational programs for students of different ages and for teachers.</p>

Foundation models are platform models: Prompting and the political economy of AI

A recent innovation in the field of machine learning has been the creation of very large pre-trained models, also referred to as ‘foundation models’, that draw on much larger and broader sets of data than typical deep learning systems and can be applied to a wide variety of tasks. Underpinning text-based systems such as OpenAI's ChatGPT and image generators such as Midjourney, these models have received extraordinary amounts of public attention, in part due to their reliance on prompting as the main technique to direct and apply them. This paper thus uses prompting as an entry point into the critical study of foundation models and their implications. The paper proceeds as follows: In the first section, we introduce foundation models in more detail, outline some of the main critiques, and present our general approach. We then discuss prompting as an algorithmic technique, show how it makes foundation models programmable, and explain how it enables different audiences to use these models as (computational) platforms. In the third section, we link the material properties of the technologies under scrutiny to questions of political economy, discussing, in turn, deep user interactions, reordered cost structures, and centralization and lock-in. We conclude by arguing that foundation models and prompting further strengthen Big Tech's dominance over the field of computing and, through their broad applicability, many other economic sectors, challenging our capacities for critical appraisal and regulatory response.

Application of wireless sensor network based on improved genetic algorithm in English blended learning

Adaptive genetic algorithm is one of the classic algorithms to solve multi-objective optimization problems. In order to make the convergence set to the Pareto boundary more uniform, this article improves how to compare multiple distances in the algorithm, introduces the concept of comparing the difference between single distances, and uses the adaptive and mutation of crossover operators and the minimum distance filtering mechanism. An improved NSGA-II adaptive algorithm is proposed. Among other things, in the machine learning system model, the environment refers to an external source of information, which provides research-related information. This study proved that learning requires systematic organization, similar to short-term memory in information processing models. Find the external environment, then use the methods of thinking analysis, integration, comparison and induction to obtain knowledge, and store the knowledge in a scientific way. With the rapid development of knowledge in modern society, on the basis of the development of English text analysis technology and network technology, the reform of China's education system has reached a new level. Reforming modern teaching methods is an important part of education reform. More and more educators are considering analyzing English texts as one of the important tools to reform teaching methods and improve teaching effects. Among other things, based on the B/S model development technology, the teaching system is researched using production theories and methods, and the teaching system is actually being tested, developed and applied. By comparing the effects of computer network teaching methods and traditional teaching methods, the feasibility and effectiveness of the network teaching system are determined.

Enhancing Procedural Writing Through Personalized Example Retrieval: A Case Study on Cooking Recipes

AbstractWriting high-quality procedural texts is a challenging task for many learners. While example-based learning has shown promise as a feedback approach, a limitation arises when all learners receive the same content without considering their individual input or prior knowledge. Consequently, some learners struggle to grasp or relate to the feedback, finding it redundant and unhelpful. To address this issue, we present , an adaptive learning system designed to enhance procedural writing through personalized example-based learning. The core of our system is a multi-step example retrieval pipeline that selects a higher quality and contextually relevant example for each learner based on their unique input. We instantiate our system in the domain of cooking recipes. Specifically, we leverage a fine-tuned Large Language Model to predict the quality score of the learner’s cooking recipe. Using this score, we retrieve recipes with higher quality from a vast database of over 180,000 recipes. Next, we apply to select the semantically most similar recipe in real-time. Finally, we use domain knowledge and regular expressions to enrich the selected example recipe with personalized instructional explanations. We evaluate in a 2x2 controlled study (personalized vs. non-personalized examples, reflective prompts vs. none) with 200 participants. Our results show that providing tailored examples contributes to better writing performance and user experience.

A Review On Early Detection Of Chronic Kidney Disease

Early detection of Chronic Kidney Disease (CKD) is critical for timely intervention and effective treatment. Deep learning algorithms have demonstrated promise in medical applications, including disease detection. In this study, we propose a deep learning-based system for early CKD detection using the Chronic Kidney Disease dataset from Kaggle. Additionally, we incorporate the Grasshopper Optimization Algorithm (GOA) for feature selection to enhance the system's performance and interpretability. Our system employs a convolutional neural network (CNN) architecture to analyze clinical and laboratory attributes from the CKD dataset, obtained from Kaggle, consisting of 4,000 instances with 25 attributes. These attributes encompass patient demographics, blood tests, and medical history, providing a comprehensive representation of CKD-related factors. To improve the system's performance, we integrate the GOA for feature selection. The GOA is a nature-inspired metaheuristic optimization algorithm that mimics the foraging behavior of grasshoppers. It aims to identify the most relevant attributes associated with CKD from the dataset. By selecting a subset of informative features, we enhance the model's predictive accuracy and reduce overfitting. During the training phase, the CNN learns to automatically extract relevant features and patterns associated with CKD from the selected attributes. Additionally, data preprocessing techniques such as normalization and feature scaling are applied to further improve the model's performance and generalizability. To evaluate the system's performance, we conduct experiments using a separate test dataset comprising 1,000 instances from the CKD dataset. The incorporation of the GOA for feature selection in our deep learning system not only improves its performance but also enhances interpretability. By identifying the most relevant attributes associated with CKD, we focus on key biomarkers and risk factors, enhancing the system's accuracy and providing valuable insights into the disease. Our research showcases the potential of deep learning algorithms, coupled with GOA-based feature selection, for early CKD detection. By leveraging the Kaggle CKD dataset and incorporating the GOA, we contribute to improving the accuracy and applicability of the system in real-world clinical settings. To handle Big data we are proposing to implement this problem on Pyspark one of the Big data computational environments for effective learning. In this platform, we can dynamically scale the infrastructure as per the demand of the data. Ultimately, our work aims to advance the early detection and management of CKD, leading to improved patient outcomes and more effective healthcare interventions.

Fossil energy market price prediction by using machine learning with optimal hyper-parameters: A comparative study

Fossil energy markets are important commodities, and their price fluctuations impact worldwide economy and financial markets. Hence, it is essential to forecast the prices of fossil energy commodities. In this study, various machine learning systems are optimized by using Bayesian optimization method and implemented to forecast prices in 12 different fossil energy markets including crude oil, natural gas, propane, kerosene, gasoline, heating oil, and coal based on price information from all fossil energy markets. The optimized machine learning systems considered in modeling and simulations are Gaussian regression process, support vector regression, regression trees, k-nearest neighbor algorithm, and deep feedforward neural networks. The simulation results show that Gaussian regression process is the best system to forecast fossil energy markets. In addition, the deep learning feedforward neural networks system yields to stable forecasts. Furthermore, the prediction of the prices in natural gas, coal, and propane markets is a difficult task compared to the prediction of the prices in crude oil markets. The understanding of the effectiveness of machine learning systems in pricing different fossil energy markets can help international economic agents establish appropriate investment strategies.

Machine learning system to assess rice crop change detection from satellite-derived RGVI due to tropical cyclones using remote sensing dataset

Tropical cyclones strike massive devastation of agricultural crops in South Asian countries practically every year. For assessing impacts on primary land use and land cover (LULC) classes of Patuakhali district in Bangladesh, this study utilized the Landsat-8 image dataset to compute change detection indices of a consistent cyclone landfall period in the late rainy season (October and November). Different stage of cyclones based on their wind speed (km/h) were also analyzed to explore agricultural crop change detection due to the tropical cyclone (TC) Bulbul (made landfall on November 9th, 2019). Three years later, cyclone Sitrang made landfall on October 24th, 2022, and was employed for the test results that the Bulbul's rice growth vegetation index (RGVI)-CD trained in the study's machine learning (ML) system. Therefore, the objective of this study was to determine rice crop changes from RGVI due to a cyclone (Bulbul) to evaluate growth change status by another cyclone (Sitrang) using machine learning system. In Patuakhali district, the rice crops were damaged mainly and affected in classification as moderately changed detected (MCD) 26.07%, very changed detected (VCD) 48.83%, and extremely changed detected (ECD) 17.73% in total agricultural lands area (1548.93 km2). It was also significantly monitored for the rice crops to change as rice growth vegetation index (RGVI)-CD related to cyclone wind speed (km/h), DEM, distance from the rivers, and shorelines. From the result, the symmetric regression of coefficient (R2) of (i) linear regression, (ii) fine tree, (iii) support vector machine, (iv) ensemble boosted trees, and (v) trilayered neural network were obtained 0.735, 0.734, 0.737, 0.756 and 0.736 as good-fitted test outcomes respectively. Applying the five machine learning models that were created from cyclone Sitrang, it was discovered that the test set in this system was accurate to the observed forecast with acceptable RMSE and MAE values. Therefore, study can be helpful for researchers creating new damaged and yield loss area assessment policies to help TC-affected farming communities, especially rice growers, prepare an effective emergency response plan for their socioeconomic needs fulfillment in the changing climate perspectives for attaining sustainable development.

Classification of Melanoma Based on Clustering Channels RGB of Skin Scan Using Adaptive Light Weight Deep Learning System

Melanoma is considered to be a type of skin cancer that is characterisedby symptoms of poor prognostic responses. In this paper, we propose a deep learning method using DCNNs, modifying the output layer and enhancing the features of skin scan images collected from Kaggle to be distinguished into two groups: melanoma and non-melanoma cells. The proposed modified three types of DCNN (MobilNet-v2, ResNet-18 and Squeeze Net) models have been tested in two experiments. In the first, the obtained values of training accuracy are (93, 95 and 91) % and the testing accuracy is (90.09, 90.54 and 90.4) %, using original datasets only. In the second experiment, the obtained values of training accuracy are (99.7, 96.3 and 92) % and the testing accuracy is (94.41, 94.14 and 91.43) %, The experimental findings show that the model utilized produces enhanced photos with more accuracy than original images.

Digital Competence in Foreign Language Textbooks: Comparative Analysis of Language Learning Systems in English, German and Spanish

The current article is aimed at examining the application of information and communication technologies in present-day foreign language learning systems in Bulgaria. It focuses on systems used in secondary education for teaching English, German and Spanish. The quantitative and qualitative analyses trace the establishment of a relationship between the development of digital competence and foreign language learning. The conclusions of this article point to a reciprocal and complementary relationship between the development of digital and language skills.

The Mode Exploration of Combining Chinese Traditional Culture Content with Computer Aided Instruction in Moral Education of Colleges and Universities

According to the characteristics of learning style, this paper classifies the traditional cultural classics according to the school. Audio, video, text, pictures and other forms are collected to match students' learning styles. The system uses the three-layer structure of My Batis+ Spring MVC, and uses Ajax+ Free marker+ JS technology, MySQL database, Apache Tomcat8 manager, Eclipse development platform, WebStorm and other network front-end development tools. In addition, auxiliary tools such as Camtasia Studio and PhotoshopCS5 are used to generate prototype systems for traditional culture learning. Its data processing is fast.

Lifelong Learning System in the age of Artificial Intelligence: Change, Opportunity and Reconstruction

Artificial Intelligence advances the offline and online education, digital education and virtual education, eventually all the way to lifelong education with online, digital and virtual Characteristics. In the era of “Artificial Intelligence” (AI), computer technology, Internet technology, big data technology, and cloud computing technology have been widely used in all life and work, making the information dissemination space broader and faster. Information Space provides a platform for new knowledge acquisition and learning for education. The lifelong learning system for the whole population has been updated to break through the modes and limitations of traditional education and meet the needs of all people. This paper first analyzes the connotation and characteristics of lifelong learning for all and points out the current policies and opportunities. Finally, it proposed to build a high-quality, efficient, and sustainable comprehensive lifelong learning system to form an excellent social learning situation in which "everyone can learn, everywhere can learn, and always can learn.” To enhance lifelong learning, efforts focus on expanding the main body responsible for learning, building intelligent learning environments, and providing precise guidance based on educational needs. It is also essential to improve feedback mechanisms, service places, and create an integrated online platform, alongside establishing knowledge-based organizations. This research focus on accountability and quality in lifelong learning since it is evolving with new technologies and opportunities. At the same time, it can expand learning, create an intelligent environment, improve feedback, and establish an online educational platform. This will provide inclusive and high-quality educational opportunities to all learners.

Towards achieving the sustainable development goals: a collaborative action plan leveraging the circular economy potentials

AbstractThis contribution to the journal “Gruppe. Interaktion. Organisation. (GIO)” delves into the critical role of Sustainable Development Goal 17 (SDG 17), “Partnership for the Goals,” in fostering interdisciplinary collaboration to further circular economy objectives. Aiming to address the existing research focus on isolated technical aspects within the circular economy, this study uses an expertise- and interaction-based foresight process to integrate perspectives from business, engineering, social sciences, and work and organizational psychology to unearth broader, interconnected challenges and solutions for the implementation of circular economy practices. Based on the pooled experience of experts within the foresight process, this contribution elucidates major challenges, ongoing issues, and prospective hurdles to be tackled by 2030 in circular economy, presenting an action plan centered on interdisciplinary cooperation and stakeholder engagement to advance labor conditions and continuous learning, sustainable and regenerative systems, minimize waste, and optimize resource efficiency. The collaborative action plan serves as a pivotal resource for the transformation task for researchers and practitioners, offers insights and strategic directions for implementing circular economy practices, and emphasizes collective responsibility and action in achieving global sustainability, all while aligning with the principles of SDG 17. The presented collaborative approach propels the discourse on sustainability forward, highlighting the potential of the circular economy in realizing a more sustainable world by 2030.