Learning System Research Articles

Pedagogical Implementation of Directive Feedback Manikins on Cardiopulmonary Resuscitation (CPR) Competencies: Expert Versus Peer Coaching.

Directive feedback manikins in resuscitation training evolved faster than the pedagogical evidence. Educators and learning systems must seek clarification on the efficacy of this technology to have evidence-based practices. This project explores directive feedback device use in cardiopulmonary resuscitation (CPR) education for laypersons. A prospective nonrandomized-controlled design assessed two pedagogical approaches of directive feedback manikins in adult CPR lessons.The 230 participants were distributed between three groups: a control group without directive feedback manikins (no lights, NL), an expert coaching (EC) group with directive feedback and educator interpretation, and a peer coaching (PC) group with directive feedback, peer interpretation, and expert quality assurance. From the 25 courses observed, average compression depth (mm) did not differ between groups (p = .498),average compression rate (compressions: minute) significantly differed between groups (p = .004), and correct hand placement did not differ between groups (p = .249). A chi-square test showed no significant association between groups and CPR skill feedback, or between groups and "recommending the course to a friend or family member." The PC group was more likely to agree that they could "coach someone to do CPR skills" than the NL or EC. This study expands the knowledge base of directive feedback manikins in a pedagogical setting to improve CPR competencies. Training organizations may consider any of these practices effective,choosing those that align with desired outcomes. CPR educators need orientation to feedback devices as well as professional development on educational options for their use. Considerations for further research include technology costs, access, and cultural aspects of implementing these tools.

Deep Learning for Predicting the Difficulty Level of Removing the Impacted Mandibular Third Molar

BackgroundPreoperative assessment of the impacted mandibular third molar (LM3) in a panoramic radiograph is important in surgical planning. The aim of this study was to develop and evaluate a computer-aided visualisation–based deep learning (DL) system using a panoramic radiograph to predict the difficulty level of surgical removal of an impacted LM3. MethodsThe study included 1367 LM3 images from 784 patients who presented from 2021–2023 to the University Dental Hospital; images were collected retrospectively. The difficulty level of surgically removing impacted LM3s was assessed via our newly developed DL system, which seamlessly integrated 3 distinct DL models. ResNet101V2 handled binary classification for identifying impacted LM3s in panoramic radiographs, RetinaNet detected the precise location of the impacted LM3, and Vision Transformer performed multiclass image classification to evaluate the difficulty levels of removing the detected impacted LM3. ResultsThe ResNet101V2 model achieved a classification accuracy of 0.8671. The RetinaNet model demonstrated exceptional detection performance, with a mean average precision of 0.9928. Additionally, the Vision Transformer model delivered an average accuracy of 0.7899 in predicting removal difficulty levels. ConclusionsThe development of a 3-phase computer-aided visualisation–based DL system has yielded a very good performance in using panoramic radiographs to predict the difficulty level of surgically removing an impacted LM3.

Exploring ICT-based learning adoption in higher education: An extended perspective of the technology acceptance model

This article analyses the self-perception of digital technology learning competence among engineering and science students. The use of information and communication technologies (ICT) is now considered highly essential in higher education. Today's higher education institutions (HEIs) seldom employ traditional methods of instruction; instead, sophisticated ICT is starting to emerge as a feasible paradigm for fundamental change. To this end, Pakistan's Higher Education Commission (HEC) has heavily invested in technology in the education sector and initiated several programs. The ramifications of ICT utilization on the environment, student academic performance, and capabilities remain ambiguous. In delving into how students embrace and utilize ICT systems for learning in HEIs, researchers felt the need to grasp the factors that influence students' acceptance and utilization of ICT skills through the lens of the Extended Technology Acceptance Model (TAM). This study develops an exclusive conceptual model by incorporating TAM with a set of latent variables identified in the available literature: interest, ICT self-efficacy, economic cost, and performance expectancy. The adapted TAM model developed in this study considers the influence of these variables on students’ ICT acceptance and its impact on academic performance in emerging countries. As part of this study, reports on the development of instruments and validation in the research field were managed using a cross-sectional survey method and SPSS-22 and Smart PLS-4 software. In addition, association rule mining applied in demographic data. A sample of 69 students was randomly selected from three universities representing engineering, medical, and general HEIs in the Sindh province. Though the sample size was small, it showcases that the reliability of the scales is within an acceptable range and can be used to test the main study hypotheses. A conceptual framework model is introduced to offer a comprehensive framework derived from the amalgamation of various acceptance and usage models of technology. The results demonstrate that the survey items are appropriate and suitable for further research.

IEEE Transactions on Neural Networks and Learning Systems Information for Authors

IEEE Transactions on Neural Networks and Learning Systems Information for Authors

Innovación educativa con sistemas de aprendizaje adaptativo impulsados por Inteligencia Artificial

The emergence of artificial intelligence (AI) is transforming education through adaptive learning systems. These systems, based on AI algorithms, personalize the educational experience by adjusting to the needs and learning styles of each student. Using techniques such as machine learning and deep learning, they analyze large volumes of data to generate personalized learning itineraries, breaking with the homogeneous teaching model. Their implementation requires a suitable technological platform, a solid data infrastructure and the training of teachers in the use of these tools. The benefits are multiple: students receive real-time feedback and progress at their own pace, improving their motivation and learning effectiveness, while teachers can focus their efforts on higher value-added tasks and obtain valuable information on their students' progress, facilitating adaptive and personalized teaching.

A Dynamic-Inner Manifold Broad Learning System With Coupled Time–Frequency Domain for Wind Speed Prediction

A Dynamic-Inner Manifold Broad Learning System With Coupled Time–Frequency Domain for Wind Speed Prediction

Proposal for an e-learning system model based on the invocation and semantic discovery of web services

Service-oriented computing (SOC) provides a new framework for designing distributed web applications and software in a flexible, scalable, and cost-effective manner. Its use is widespread to efficiently integrate existing Web services and create high value-added applications. This model, proven in various fields such as e-commerce, also shows significant advantages in the field of e-learning. This approach highlights the discovery and use of Web services listed in specialized directories. In fact, this paper proposes a framework for exploring web services associated with education. This approach is based on the application of a matching algorithm to select the services best suited to the needs of users of the online learning system, as well as the ontology of the e-learning domain and the semantic descriptions of the web services via web ontology language for web services (OWL-S).

IEEE Transactions on Neural Networks and Learning Systems Publication Information

IEEE Transactions on Neural Networks and Learning Systems Publication Information

Tree Broad Learning System for Small Data Modeling.

Broad learning system based on neural network (BLS-NN) has poor efficiency for small data modeling with various dimensions. Tree-based BLS (TBLS) is designed for small data modeling by introducing nondifferentiable modules and an ensemble strategy to the traditional broad learning system (BLS). TBLS replaces the neurons of BLS with the tree modules to map the input data. Moreover, we present three new TBLS variant methods and their incremental learning implementations, which are motivated by deep, broad, and ensemble learning. Their major distinction is reflected in the incremental learning strategies based on: 1) mean square error (mse); 2) pseudo-inverse; and 3) pseudo-inverse theory and stack representation. Therefore, this study further explores the domain of BLS based on the nondifferentiable modules. The simulations are compared with some state-of-the-art (SOTA) BLS-NN and tree methods under high-, medium-, and low-dimensional benchmark datasets. Results show that the proposed method outperforms the BLS-NN, and the modeling accuracy is remarkably improved with the small training data of the proposed TBLS.

A Multi-Level Interpretable Sleep Stage Scoring System by Infusing Experts' Knowledge Into a Deep Network Architecture.

In recent years, deep learning has shown potential and efficiency in a wide area including computer vision, image and signal processing. Yet, translational challenges remain for user applications due to a lack of interpretability of algorithmic decisions and results. This black box problem is particularly problematic for high-risk applications such as medical-related decision-making. The current study goal was to design an interpretable deep learning system for time series classification of electroencephalogram (EEG) for sleep stage scoring as a step toward designing a transparent system. We have developed an interpretable deep neural network that includes a kernel-based layer guided by a set of principles used for sleep scoring by human experts in the visual analysis of polysomnographic records. A kernel-based convolutional layer was defined and used as the first layer of the system and made available for user interpretation. The trained system and its results were interpreted in four levels from microstructure of EEG signals, such as trained kernels and effect of each kernel on the detected stages, to macrostructures, such as transitions between stages. The proposed system demonstrated greater performance than prior studies and the system learned information consistent with expert knowledge.

An Energy-Efficient Deep Mutual Learning System Based on D2D-U Communications

An Energy-Efficient Deep Mutual Learning System Based on D2D-U Communications

Advances and Challenges in Meta-Learning: A Technical Review.

Meta-learning empowers learning systems with the ability to acquire knowledge from multiple tasks, enabling faster adaptation and generalization to new tasks. This review provides a comprehensive technical overview of meta-learning, emphasizing its importance in real-world applications where data may be scarce or expensive to obtain. The article covers the state-of-the-art meta-learning approaches and explores the relationship between meta-learning and multi-task learning, transfer learning, domain adaptation and generalization, self-supervised learning, personalized federated learning, and continual learning. By highlighting the synergies between these topics and the field of meta-learning, the article demonstrates how advancements in one area can benefit the field as a whole, while avoiding unnecessary duplication of efforts. Additionally, the article delves into advanced meta-learning topics such as learning from complex multi-modal task distributions, unsupervised meta-learning, learning to efficiently adapt to data distribution shifts, and continual meta-learning. Lastly, the article highlights open problems and challenges for future research in the field. By synthesizing the latest research developments, this article provides a thorough understanding of meta-learning and its potential impact on various machine learning applications. We believe that this technical overview will contribute to the advancement of meta-learning and its practical implications in addressing real-world problems.

AO-SVM: a machine learning model for predicting water quality in the cauvery river

Water pollution is a significant cause of death globally, resulting in 1.8 million deaths annually due to waterborne diseases. Assessing water quality is a complex process that involves identifying contaminants in water sources and determining whether it is safe for human consumption. In this study, we utilized the Cauvery River dataset to develop a model for evaluating water quality. The aim of our research was to proficiently perform feature selection and classification tasks. We introduced a novel technique called the Aquila Optimization Support Vector Machine (AO-SVM), an advanced and effective machine learning system for predicting water quality. Here SVM is used for the classification, and the Aquila algorithm is used for optimizing SVM. The results show that the proposed method achieved a maximum accuracy rate of 96.3%, an execution time of 0.75 s, a precision of 93.9%, a recall rate of 95.1%, and an F1-Score value of 94.7%. The suggested AO-SVM model outperformed all other existing classification models regarding classification accuracy and other parameters.

Human Versus Machine Intelligence: Assessing Natural Language Generation Models Through Complex Systems Theory.

The introduction of Transformer architectures - with the self-attention mechanism - in automatic Natural Language Generation (NLG) is a breakthrough in solving general task-oriented problems, such as the simple production of long text excerpts that resemble ones written by humans. While the performance of GPT-X architectures is there for all to see, many efforts are underway to penetrate the secrets of these black-boxes in terms of intelligent information processing whose output statistical distributions resemble that of natural language. In this work, through the complexity science framework, a comparative study of the stochastic processes underlying the texts produced by the English version of GPT-2 with respect to texts produced by human beings, notably novels in English and programming codes, is offered. The investigation, of a methodological nature, consists first of all of an analysis phase in which the Multifractal Detrended Fluctuation Analysis and the Recurrence Quantification Analysis - together with Zipf's law and approximate entropy - are adopted to characterize long-term correlations, regularities and recurrences in human and machine-produced texts. Results show several peculiarities and trends in terms of long-range correlations and recurrences in the last case. The synthesis phase, on the other hand, uses the complexity measures to build synthetic text descriptors - hence a suitable text embedding - which serve to constitute the features for feeding a machine learning system designed to operate feature selection through an evolutionary technique. Using multivariate analysis, it is then shown the grouping tendency of the three analyzed text types, allowing to place GTP-2 texts in between natural language texts and computer codes. Similarly, the classification task demonstrates that, given the high accuracy obtained in the automatic discrimination of text classes, the proposed set of complexity measures is highly informative. These interesting results allow us to add another piece to the theoretical understanding of the surprising results obtained by NLG systems based on deep learning and let us to improve the design of new informetrics or text mining systems for text classification, fake news detection, or even plagiarism detection.

Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility

Advances in artificial intelligence (AI) and machine learning systems promise faster, more efficient, and more personalized care. While many of these models are built on the premise of improving access to the timely screening, diagnosis, and treatment of cardiovascular disease, their validity and accessibility across diverse and international cohorts remain unknown. In this mini-review article, we summarize key obstacles in the effort to design AI systems that will be scalable, accessible, and accurate across distinct geographical and temporal settings. We discuss representativeness, interoperability, quality assurance, and the importance of vendor-agnostic data types that will be immediately available to end-users across the globe. These topics illustrate how the timely integration of these principles into AI development is crucial to maximizing the global benefits of AI in cardiology.

Foreign Language Teacher's Attitudes Towards a Pre-designed Language Learning System

Once the pandemic concluded, the Foreign Languages Department of a Chilean state university hired a Canadian company to implement a pre-designed language learning system (PLLS). This platform was to be used by all teachers and students, as it contained various activities to develop all four language skills, including pronunciation practice through AI-based voice recognition. This study explores the attitudes of 17 university teachers towards using these pre-elaborated resources, activities, and assessments in their communicative English and German courses. A mixed-method approach was used, involving a survey based on the Technology Adoption Model (TAM) and individual interviews. Descriptive statistics were obtained from the survey responses, and qualitative data were analysed using content analysis techniques. The results indicate that teachers' attitudes towards the PLLS were generally neutral to negative. Instructors expressed their concerns about the system's pre-designed content and perceived functionality. Perceived ease of use and usefulness were rated low, reporting difficulties in navigation and alignment with their teaching styles. Perceived enjoyment received the lowest rating, mentioning issues such as disconnected content and lack of progressive structure. Qualitative data revealed technical problems, increased workload, and concerns about the system's impact on student motivation and learning outcomes. While some positive aspects were noted, the overall attitude towards the PLLS was predominantly negative, highlighting the need for better alignment with pedagogical goals and improved implementation strategies.

Album of animated drawings as an innovative means of teaching algebra in the digital environment of bilingual students

Introduction. Digitalization of education has opened up new opportunities for the development and application of the didactic potential of digital technologies in teaching algebra in the context of bilingualism. This determined the purpose of this work, which is to create a methodological system for teaching algebra to bilingual students and an album of animated drawings in a digital environment. Scientific and pedagogical research focuses on the problem of mathematical training of schoolchildren in the context of bilingualism. From the point of view of improving the quality of education, it is advisable to rely on visual-figurative and associative thinking developed by bilingual students. Materials and methods. The experimental study involved 50 students of the seventh grade of gymnasium No. 5 in the city of Kyzyl, Republic of Tyva (Russian Federation). The methods of comparative analysis of scientific, pedagogical and methodological sources, empirical and statistical methods (Student's t-test) are applied. Results. A methodological system for teaching algebra to bilingual students using an album of animated drawings in a digital environment has been developed. An album of animated drawings is presented, implemented in the form of an electronic training course in the electronic learning system of Tuvan State University and the results of its application in teaching algebra to bilingual students. As a result of the experiment, it was revealed that in the absence of statistically significant differences in the control and experimental groups at the time of the initial control, since tэмп = 2,00 < tкр = 2,01, during the final control, tэмп = 2,80 > tкр = 2,01 at the significance level p < 0,05. This indicates that the compared groups are significantly different and the experimental hypothesis about the effectiveness of using an album of animated drawings as an innovative means of teaching seventh grade algebra in the digital environment of bilingual students is confirmed. Conclusion. The use of an album of animated drawings as an innovative means of teaching algebra in the digital environment of bilingual students of the seventh grade of the Republic of Tyva increases the effectiveness of learning by increasing the clarity of algebraic concepts and statements, reducing the amount of non-essential calculations, as well as the use of mechanisms for rapid assessment of learning outcomes using a developed system of bilingual prompts. The proposed learning tool forms and develops an experimental research style of learning.

Hardening Interpretable Deep Learning Systems: Investigating Adversarial Threats and Defenses

Hardening Interpretable Deep Learning Systems: Investigating Adversarial Threats and Defenses

Implementation of A Graded Point System As An Alternative in Increasing Learning Interest in Sociology Study

The low of student motivation and interest in learning during the COVID-19 pandemic continued to occur even when PTM (face-to-face learning) was held. This research aims to determine the effectiveness of implementing a graded point system as an alternative to increasing learning interest in sociology. This research uses a descriptive-qualitative method with a naturalistic approach. Data was obtained through observation, interviews, and documentation. The population in this research was 150 class XI students at SMAN 5 Banjarmasin. The subjects in this research were 75 students. Data analysis was carried out using narrative and discourse analysis. Based on the research results, it was found that the application of a graded points system can encourage more active interaction between students and teachers. Students also show higher enthusiasm for learning with the implementation of a graded points system in learning. Thus, it can be concluded that the application of the graded point system is quite effective as an alternative for increasing students' interest in learning.

Bibliometric Exploration of Ethnomathematics: Trends, Characteristics, and Future Directions

Ethnomathematics plays a very important role in life, especially in mathematics learning. Even so, ethnomathematics is still rarely known to the public. As a result, ethnomathematics research is still rarely carried out. So, ethnomathematics is a good topic to discuss in research. But before that, do an analysis first. So, researchers conducted analytical ethnomathematics research using bibliometrics to explore publication characteristics, find trends, and find future research directions in the field of ethnomathematics. The data used is sourced from the Scopus database. The science mapping steps taken are study design, data collection, data analysis, data visualization, and interpretation. From the analysis, 580 documents were obtained that had been published from 1984 to 2023 with the core of the study being learners, mathematical concepts, learning mathematics and physics. Based on this information, it is clear that ethnomathematics still needs to be explored more deeply, considering that most documents are not articles. As for the characteristics of ethnomathematics research, apart from the lack of material in the form of articles, it turns out that ethnomathematics, which is currently developing, is a learning system in mathematics. Even ethnomathematics can improve high-order thinking skills (HOTS). Apart from that, research on teaching, design, and thinking processes in ethnomathematics is still small but very important. Based on this information, the most suitable novelty for future ethnomathematics research is research on the design of ethnomathematics-based mathematics learning models.