Development Of Learning System Research Articles

An Empirical Study on the Effect of Training Data Perturbations on Neural Network Robustness

The vulnerability of modern neural networks to random noise and deliberate attacks has raised concerns about their robustness, particularly as they are increasingly utilized in safety- and security-critical applications. Although recent research efforts were made to enhance robustness through retraining with adversarial examples or employing data augmentation techniques, a comprehensive investigation into the effects of training data perturbations on model robustness remains lacking. This paper presents the first extensive empirical study investigating the influence of data perturbations during model retraining. The experimental analysis focuses on both random and adversarial robustness, following established practices in the field of robustness analysis. Various types of perturbations in different aspects of the dataset are explored, including input, label, and sampling distribution. Single-factor and multi-factor experiments are conducted to assess individual perturbations and their combinations. The findings provide insights into constructing high-quality training datasets for optimizing robustness and recommend the appropriate degree of training set perturbations that balance robustness and correctness, and contribute to understanding model robustness in deep learning and offer practical guidance for enhancing model performance through perturbed retraining, promoting the development of more reliable and trustworthy deep learning systems for safety-critical applications.

Machine Design and Development of CoreXY FDM 3D Printer for Learning

This research aims to design and develop CoreXY FDM 3D Printer that can be optimized for learning purposes. By detailing aspects of design, hardware, and software, this research is expected to make a significant contribution in improving the quality of learning. Research using the Research and Development method, carried out based on the Machine Learning System Development model with stages in the form of Problem Understanding, Data Handling, Model Building, and Model Monitoring. The results showed that the lowest average depreciation value in the 3D printer developed was smaller than the 3D Printer machine from the previous study. The best print quality was produced in experiment number 3 where the print results were almost flat and smooth. So that the best print parameters are produced at a layer thickness of 0.1 mm and a print speed of 60 mm / minute. Blackbox Test results show that all components of the 3D printer machine have been able to function properly. The results of the user trial questionnaire showed that the average value of all aspects received a value of 3.55 from a range of values 1-4, indicating that this machine is very good to be used as a medium in the learning process. Comparison of FDM CoreXY 3D Print printing process time after development shows shorter print time than FDM CoreXY 3D Printer machine before development.

MULTIMODAL SYSTEM FOR FACIAL EMOTION RECOGNITION BASED ON DEEP LEARNING

Emotions are one of the main ways of communication between people and of expressing attitudes towards objects, products, services, etc. They are divided to verbal and non-verbal classes. Human speech and intonation belong to the first class, and to the second (non-verbal) facial and body emotions, known as body language. The subject of this report is the development of multimodal deep learning system intended to recognize facial and body emotions and their relationship with the scene (weather) in which they occur. It is based on three deep learning neural networks (DNN) each one for recognition of facial emotion, body emotion and weather. Combining their results, we improve significantly the final facial emotion recognition (FER) results.

Speech recognition based on mobile sensor networks application in English education intelligent assisted learning system

With the popularity of English learning and the development of mobile technology, the development of intelligent assisted learning system has become an effective learning method. The purpose of this study is to improve the speech recognition performance of English education intelligent learning assistance system by using mobile sensor networks, and improve the learning effect of learners. This paper uses a speech recognition algorithm based on mobile sensor network to acquire learners' speech signals with multiple sensor nodes. By preprocessing and feature extraction of acquired signals, machine learning algorithm is applied to recognize them, thus improving the recognition accuracy. The experimental results show that compared with traditional speech recognition methods, the speech recognition system based on mobile sensor network has better performance in the case of background noise and speaker change. When learners use the system to learn English, they can get more accurate and reliable speech recognition results, improve the learning effect and interactive experience, and provide a more effective learning tool for English learners.

Development and Evaluation of an Image Processing-Based Kinesthetic Learning System

This study aims to develop an interactive language learning game and explore its efficacy for English language learners. A computer-generated playground was projected onto a large classroom floor (4 × 3 m) with a wide-angle projection device. A Kinect depth camera determined the spatial positions of the playground and the positions of the students’ heads, feet, and bodies. Then, we evaluated the system’s effect on English education through pre- and post-tests. While there was no significant difference between the groups in terms of achievement in the pre-tests, the experimental group exhibited significantly greater improvement in the post-tests (F: 14.815, p < 0.001, η2p: 0.086). Also, both groups demonstrated significant learning gains in post-tests compared to pre-tests (F: 98.214, p < 0.001, η2p: 0.383), and the group x time interaction of the experimental group increased more in percentage (32.32% vs. 17.54%) compared to the control group (F: 9.166, p < 0.003, η2p: 0.055). Qualitative data from student views indicated enhanced learning pace, vocabulary acquisition, enjoyment of the learning process, and increased focus. These findings suggest that a kinesthetic learning environment can significantly benefit English language learning in children.

Action-guidance and AI ethics: the case of fair machine learning

AbstractA prominent approach to implementing AI ethics involves translating ethical principles, such as fairness and transparency, into practical frameworks and tools that responsible agents, such as ML developers, can use to ensure that machine learning systems act according to the relevant principles. Fair machine learning research exemplifies this approach by producing frameworks and software toolkits that responsible agents could apply to align machine learning systems with principles such as fairness, equality, and justice. However, the application of available frameworks and tools has proven challenging both due to ambiguous operationalization of the relevant principles and many real-life obstacles that agents face in the context of machine learning system design and development, such as lack of access to proper evaluation data. This article conceptualizes these problems as instances of a more general “action-guidance gap” in AI ethics. The article addresses the action-guidance gap by outlining a philosophical account of action-guidance that can be used to identify and address problems related to the specification and practical implementation of AI ethics principles. Centering on fair machine learning practice as a case example, the article presents a set of detailed requirements for action-guidance in fair machine learning practice which explain problems that previous studies have identified with regard to the real-life application of fair machine learning frameworks and tools. Paving a way forward, the article presents theoretical and practical lessons for ensuring action-guidance in fairness-sensitive design, with implications for AI ethics more generally.

Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML

The field of automated machine learning (AutoML) introduces techniques that automate parts of the development of machine learning (ML) systems, accelerating the process and reducing barriers for novices. However, decisions derived from ML models can reproduce, amplify, or even introduce unfairness in our societies, causing harm to (groups of) individuals. In response, researchers have started to propose AutoML systems that jointly optimize fairness and predictive performance to mitigate fairness-related harm. However, fairness is a complex and inherently interdisciplinary subject, and solely posing it as an optimization problem can have adverse side effects. With this work, we aim to raise awareness among developers of AutoML systems about such limitations of fairness-aware AutoML, while also calling attention to the potential of AutoML as a tool for fairness research. We present a comprehensive overview of different ways in which fairness-related harm can arise and the ensuing implications for the design of fairness-aware AutoML. We conclude that while fairness cannot be automated, fairness-aware AutoML can play an important role in the toolbox of ML practitioners. We highlight several open technical challenges for future work in this direction. Additionally, we advocate for the creation of more user-centered assistive systems designed to tackle challenges encountered in fairness work. This article appears in the AI & Society track.

MILSDeM: Guiding immersive learning system development and taxonomy evaluation

AbstractDeveloping immersive learning systems is challenging due to their multidisciplinary nature, involving game design, pedagogical modelling, computer science, and the application domain. The diversity of technologies, practices, and interventions makes it hard to explore solutions systematically. A new methodology called Multimodal Immersive Learning Systems Design Methodology (MILSDeM) is introduced to address these challenges. It includes a unified taxonomy, key performance indicators, and an iterative development process to foster innovation and creativity while enabling reusability and organisational learning. This article further reports on applying design-based research to design and develop MILSDeM. It also discusses the application of MILSDeM through its implementation in a real-life project conducted by the research team, which included four initiatives and eight prototypes. Moreover, the article introduces a unified taxonomy and reports on the qualitative analysis conducted to assess its components by experts from different domains.

Design and Development of Intelligent Learning System for University Innovation and Entrepreneurship Based on Knowledge Visualisation

To build a comprehensive learning resource system covering learning process, learning users, learning resources and other aspects, an intelligent learning system for innovation and entrepreneurship in universities was constructed and designed combined with knowledge visualisation theory. The system includes student information record module, system recommendation calculation module, learning material module, and knowledge visualisation module. The research focuses on the system recommendation calculation module, including the User-based Collaborative Filtering Algorithm (UCF) and the Joint Recommendation (JR) algorithm of the application content recommendation algorithm. The best neighbour number of JR is 20, and the best transmission path length is 2. The recommendation effect is optimal under this setting. The convergence iterations of the training and test set of the joint recommendation algorithm are 80 times and 100 times, respectively. For the training set and test set, the accuracy, precision, sensitivity, recall, running time, error, and other performance evaluation indicators of the joint recommendation algorithm are better than the corresponding values of other recommendation algorithms. The system has a good design effect and experience effect in the application of the online learning platform.

Development of virtual learning systems based on artificial intelligence: International experience

The aim of the article is to study and improve the use of artificial intelligence in education by analysing international experience. The main methods used were general scientific methods, documentary analysis, standard statistics, and factor analysis. The study's main results demonstrate the rapid growth in the popularity of artificial intelligence in virtual learning systems in all the countries under consideration. The article reveals a tendency to increase the demand for these technologies. The study concludes that AI has an important role in the educational process and that future research should focus on evaluating its effectiveness in training specific specialists.

Integrating Artificial Intelligence into Scholarly Communications for Enhanced Human Cognitive Abilities: The War for Philosophy?

Este artículo explora la integración de la IA en la comunicación académica para mejorar las capacidades cognitivas humanas. La concepción de la comunicación hombre-máquina (CMM), que considera las tecnologías basadas en la IA no como objetos interactivos, sino como sujetos comunicativos, plantea cuestiones más filosóficas en la comunicación académica. Es un hecho conocido que existe una mayor interacción entre los humanos y las máquinas, especialmente consolidada por la pandemia COVID-19, que intensificó el desarrollo del Sistema de Aprendizaje Adaptativo Individual, por lo que necesariamente se requieren aportaciones de las IN para fortalecer la IA. Esto posicionó a la universidad en la yuxtaposición de asegurar la producción de individuos altamente talentosos que puedan continuar pensando más allá de lo pensable inteligentemente y permitir a los expertos en IA continuar construyendo algoritmos que puedan llevar el desarrollo de la tecnología al siguiente nivel. Para llevar a cabo tal tarea, la producción investigadora es primordial para conectar la universidad y la industria editorial. Este artículo examina de forma crítica componentes importantes e intenta hacer justicia a la emanación de preguntas críticas de los investigadores (NI), alimentando así el desarrollo de la tecnología (IA).

Development and Assessment of Learning System with Analytics

This study focuses on exploring the feasibility and effectiveness of employing data analysis techniques in the development of learning systems. Through a comprehensive evaluation of existing learning systems, this research identifies their principal advantages and disadvantages. In response to these issues, a novel learning system is proposed, leveraging data analysis to optimize the learning process. Experimental verification demonstrates a significant enhancement in learning effectiveness and learner satisfaction with this innovative system. The research findings provide valuable guidance for the design and application of future learning systems, emphasizing the importance of considering factors such as cost-effectiveness, sustainability, and long-term benefits for successful development and implementation. Results from an acceptability score survey indicate positive ratings from end users for the Learning System with Analytics, covering functionality, performance efficiency, compatibility, usability, reliability, maintainability, and security. Users express satisfaction with the system's performance, reliability, ease of maintenance, and security. The developed system is recognized as functional, reliable, usable, efficient, maintainable, and portable according to user feedback. Additionally, user suggestions are deemed valuable for potential enhancements to the system in the future. Overall, this research contributes insights into the application of data analysis in learning systems, emphasizing user satisfaction and offering practical implications for system development and refinement.

The artificial intelligence: Prospects for development and problems of humanization

The research explores the main problems associated with the development and implementation of artificial intelligence technologies in human activities, as well as with the humanization of these technologies. In a broad sense, artificial intelligence is a set of algorithms and software systems that can solve some problems the way a person would do and differ in that they are amenable to learning. An analysis of the problems of introducing artificial intelligence technologies makes it possible to substantiate the main levers of state policy aimed at the development and integrated use of digital intelligent systems. The success of the introduction and dissemination of artificial intelligence technologies largely depends on the effectiveness of state regulation of this sphere, both at the state and supranational levels. The development of machine learning systems must necessarily include an ethical aspect and some restrictions, otherwise the rapid development of intelligent machines can lead to the collapse of human civilization. To avoid such a development of events, it is necessary to create a supranational system for regulating artificial intelligence. Thus, the object of study of this article is the use of artificial intelligence systems in various fields of human activity. The authors use content analysis, systemic, adaptive and synergistic methods. In addition, the authors apply modern statistics, empirical generalization and grouping.

The development of an explainable machine learning system for predicting brain atrophies from electronic health records

AbstractBackgroundThe number of elderly living with dementia is projected to dramatically increase, especially in low‐income and middle‐income countries (LMICs). While obtaining brain scans to detect atrophies and significant radiology findings related to dementia might be costly, the use of electronic health record (EHR) for elderly patients could facilitate identifying patients at a high risk based on routinely collected data. This works, evaluates the use of an interpretable machine learning‐based system that predicts brain atrophies and small vessel disease based on EHR data.MethodWe develop a machine learning predictive system to predict atrophies or small vessel disease from retrospective anonymized EHR data recorded for patients above the age of 50 years from a multispecialty outpatient medical center from Ramallah, Palestine. The patient labelling involved using natural language processing to identify cases in unstructured radiology reports for structural brain Magnetic Resonance Imaging (MRI). For input features, we extracted and preprocessed 30 features for the most commonly collected clinical features such as demographics and laboratory results. The dataset was split into training and test sets, where data preprocessing and augmentation, and hyperparameter tuning were applied before training multiple models including Xgboost, Support Vector Machines, Random Forest, and Logistic Regression. To better understand the relative impact each feature had on the model’s prediction, shapely feature importance analysis was conducted for the top performing model.ResultThe top preforming model was Random Forest, with a performance of Area Under the Precision‐Recall Curve (AUPRC) of 0.989, and 0.842 for Area Under Receiving Operating Curve (AUROC). The feature importance analysis revealed that age, sex, Vitamin D, Vitamin B12 and red blood cell distribution width were the features with highest impact on the model’s predictionConclusionPredicting atrophies from EHRs for elderly visiting outpatient clinics could assist in case‐finding for dementia patients and ultimately help identify patients that would benefit from an MRI. In future works, we aim to further investigate building low‐cost diagnostic tools based on a smaller feature set, which could be valuable for facilities from LMICs.

Challenges in AutoML and Declarative Studies Using Systematic Literature Review

Machine Learning (ML) technologies have become essential tools, transforming industries and unlocking incredible potential in various fields. ML is now widely used for data-driven decision-making and predictive analytics across fields like healthcare, finance, transportation, and more. However, building and implementing ML models can be complex and time-consuming, often requiring programming proficiency and data science skills. Despite significant progress in ML, non-experts often struggle with selecting algorithms, optimizing models, and deploying ML solutions. This paper conducts a systematic literature review to explore challenges in the area of machine learning based on multiple categories involving features engineering and data extraction, learning model structure and activities, learning-based analysis and visualization, analysis algorithms in data-based systems, machine learning algorithms and systems development, and declarative ML-based prediction. Addressing these challenges underlines the importance of following AutoML and Declarative ML strategies in simplifying the ML process.

Using Descriptive Logic in Intelligent Educational Systems

The purpose of the article is to study and solve the problems of using the basic principles of the modern formalism of descriptive (terminological) logic in intelligent learning systems that use knowledge bases of educational content. The research methods are the main methodological approaches and technological tools for the development of intelligent learning systems. In particular, systemic and comparative analyses to identify the features of intelligent learning systems, the method of expert evaluation, which involves the analysis of literature and information resources, interviews and surveys of experts in the field of information technology and mathematical logic, as well as the process of developing and testing an intelligent system. The scientific novelty of the study is the development of a system for obtaining expert knowledge for an intelligent system that combines the formation of an expert knowledge base, and the formalism for representing which is descriptive logic. The proposed approach of formalized knowledge representation using descriptive logic can be used in the development of authors’ intelligent learning systems in the field of online education and cognitive skills development. Conclusions. The scope of application of descriptive logic in intelligent systems is constantly expanding, increasing the capabilities of these systems to formally derive new knowledge, including in conditions of incomplete certainty. Descriptive logic, used in intelligent learning systems, provides ample opportunities for building a conclusion (i.e., forming an answer), in particular in situations such as forming and answering various types of queries to the system’s knowledge base (e.g., fully defined and clearly formulated, incompletely defined and vaguely formulated, completely vague, incompletely defined and clearly formulated, completely vague, completely vague and vaguely formulated), helping to specify the query. To do this, it is necessary to formulate a rigorous number system (to build an axiomatic theory). Thus, as a means of solving semantic and logical problems, descriptive logic has become a unique and powerful mechanism for representing knowledge. The successful use of descriptive logic in the author’s intelligent educational system will ensure the improvement of the quality of the educational process; monitoring of the learning process and control of its results; will facilitate the integration of this system into many modern information educational systems; will provide various consultations, advice, interpretations, explanations regarding the educational content or educational process, as well as a wide range of topics of educational content and tests of different levels.

Computational Psychometrics: Near Future or Reality

This book continues a series of books on the methodology of educational testing and assessment written by leading psychometricians and researchers in the field of educational assessment. Computational psychometrics is defined as the combination of computer science methods and psychometric measurement principles for analysing data obtained as a result of testing using technologically advanced test formats. The first part of the book discusses the changes that have occurred in teaching and educational assessment under the influence of digital technologies. The second part provides an overview of computational psychometric methods: from traditional psychometric models to machine learning technologies.
 The material in the book can be useful to students and researchers in the field of psychometrics who are involved in the development, design and analysis of learning systems and measurements using complex test formats and data. The strength of the book is an electronic application containing the code of the R or Python programming environment for the methodological chapters.

Learning Resource Center Organizational Programs and Facilities

The Organization of Learning Resource Centers (OLRC) acts as a supporting facility for the effectiveness and efficiency of the learning process, designed to provide convenience to students and teachers in utilizing learning resources. This research uses the literature study method by collecting information from various sources such as books, articles, journals, and relevant scientific papers. The purpose of the research is to find out the LRC program and facilities that support the effectiveness and efficiency of learning. The results showed that the LRC program involves at least three activities, namely Learning System Development, Learning Multimedia Development, and Learning Resource Needs Services. LRC, also known as media center, functions as an educational institution that provides facilities for planning, production, operation, and follow-up actions in the development of learning systems with physical and digital facilities following the times and technology.

Development of a novel robot-assisted vocabulary learning system using pure synthetic data

This study investigates the application of deep learning methods trained on synthetic data for the robust detection of vocabulary flashcards, an essential aspect of robot-assisted language learning (RALL). Despite its importance, flashcard detection has not been extensively researched in RALL systems, and it poses significant challenges due to the extensive data collection and annotation needed, especially given the large number of classes involved. These models need to generalize well to different real-world environments. To address these issues, a novel robotic platform designed for flashcard-based vocabulary learning is proposed, supported by a synthetic data generation pipeline using high dynamic range images (HDRIs) and synthetic human actors. The proposed pipeline offers an efficient data generation method and significantly enhances model generalisability to various environments. The proposed method was evaluated with five object detection models based on several challenging real-world datasets, each containing more than 200 class labels. The object detection models trained based on the proposed synthetic datasets (HDRI and HDRI+Humans) demonstrated outstanding performance, achieving median mean average precision (mAP) scores of 0.797 and 0.778. The proposed method achieves this performance without needing to be trained with real data. A comprehensive analysis comparing the proposed method with other synthetic data generation techniques is presented, and its potential for improving vocabulary flashcard detection in RALL systems is highlighted. The data and models are available at https://github.com/aimlab-mu/aimrobot.

The Effectiveness Of Developing An Adaptive Physics E-Module In Moodle-Based Blended Learning On Students' Representation Ability On Thermodynamics Material

The research aims to develop adaptive electronic modules using Moodle-based blended learning as a solution to improve representation skills in the school environment. This research combines online and face-to-face learning methods (blended learning) with the use of Moodle as a digital learning platform. Three forms of representation in the concept of physics, are (1) verbal representation; (2) visual representation; and (3) mathematical representation. The formulation of the problem in this research is the characteristics, validity, and effectiveness of the E-module. This module aims to provide learning content that is tailored to the needs and abilities of each individual student. Adaptive features allow students to access materials, assignments, and learning resources appropriate to their skills level. Learning is done through a combination of classroom teaching and e-modules through Moodle. E-module development model uses the research and development method. The research design used is a true experimental design in the form of a "Posttest-Only Control Design". Assessment includes data collection through surveys, observation, and evaluation of learning outcomes. The purpose of the evaluation is to analyze the effectiveness and efficiency of using adaptive e-modules with Moodle based on blended learning to improve students' representation skills at school. The research results showed that the E-module assessment by experts obtained a content percentage and eligibility criteria of 91.7% (feasible). To determine the level of effectiveness through the t test and comparison of posttest results. This is evidenced by the test using SPSS version 23 which shows a significance value of 0.00 <0.05. In visual representation, a percentage of 87.5% was obtained in a very good category, for mathematical representation, a percentage also was obtained in a very good category, 88.5%, and in verbal representation, a percentage was obtained in a category of 90%, as the greatest. With an average post-test score of 88.8 for the experimental of the class and 74.2 for the control of the class. Adaptive e-modules through Moodle-based blended learning can be an effective choice for increasing representational abilities and facilitating personalized and flexible learning. The results of this study are expected to encourage the development of adaptive learning systems in schools and support the improvement of student learning outcomes.