Personalized Learning System Research Articles

Human Computer Interaction Strategies for Effective Digital Learning Experiences: From Classroom to Screen

The rapid advancement of technology has transformed traditional classroom settings into complex digital learning environments, emphasizing the need for effective Human-Computer Interaction (HCI) strategies to enhance online learning experiences. This research explores the intersection of HCI and digital learning, focusing on how HCI strategies can improve user engagement, comprehension, and learning outcomes. Through a review of existing literature, this study identifies common challenges faced by learners and educators, such as usability issues, technical difficulties, and inadequate localization. It also highlights effective HCI strategies, including user- centered design, interactive multimedia features, and feedback mechanisms, which are crucial for creating engaging and personalized learning experiences. Findings indicate that personalized learning systems and interactive content significantly enhance performance and achievement by addressing individual learning needs and improving content retention. Best practices derived from user feedback suggest adopting a user-centered design approach, integrating interactive elements, focusing on usability, and continuously updating learning tools. This research underscores the importance of aligning HCI principles with educational technology to foster a more effective and inclusive digital learning environment. Keywords - Human-Computer Interaction (HCI), Digital Learning Environments, User Experience (UX),Interactive Design, Personalized Learning, Educational Technology.

AI-Enabled E-Learning Systems: A Systematic Literature

Today e-learning system plays an essential role in the education system. Technology integration in teaching helps to teach content-based curriculum effectively and efficiently to build confidence among students. Personalized learning systems focus on learning behavior, interest, and design curriculum according to learners‟ ability and basic knowledge. It is a flexible teaching methodology to meet the individual needs of students. The personalized learning approach optimizes the needs of each learner. For an effective education system, it is necessary to understand learners and develop a plan that copes up with the individual learning needs and the interest of students‟. An intelligent Tutor system is an expert system to monitor the learners‟ performance to provide personalized coaching. E-learning applications include computer-based learning, web-based learning, digital collaboration and virtual classrooms. Artificial Intelligence can be used for automating learning activities like designing teaching tools, curriculum, training, evaluating students‟ performance, and using modern teaching methodology. Artificial intelligence is the most modern e-learning trend in higher education and the corporate world. AI helps to provide individual decisions using data analytics that leads to better education for personalized instruction to streamline the education process.

Construction and Innovative Exploration of Personalized Learning Systems in the Context of Educational Data Mining

This study examines the current research on educational data mining, educational learning support services, personalized learning services, and personalized learning paths in education. The authors aim to integrate personalized learning concepts into traditional support services by drawing on the latest theoretical and practical research. Using multimodal data fusion techniques, the study conduct exploratory analyses on various data types, including learner academic performance, psychological assessments, learning behavior, and physiological information. This leads to the construction of a personalized education learning support service model. The model focuses on objectives such as monitoring learning behavior, identifying preferences, recognizing abilities, optimizing paths, and recommending resources. The goal is to provide learners with sustained support services throughout the personalized learning process, addressing individual needs, fostering enthusiasm, and maintaining long-term motivation.

Learn with M.E.—Let Us Boost Personalized Learning in K-12 Math Education!

The traditional educational system, in certain aspects, limits personalized learning. This is mainly evident in the fact that average students, who do not have any learning difficulties, are required to solve the same tasks from the same textbook in the same order. Artificial intelligence and other smart learning tools present great opportunities for implementing a personalized learning system. Our previous surveys and literature reviews also show that educators see the greatest potential in personalized education for the assimilation of artificial intelligence into education. In this context, we have developed educational software called “Learn with M.E. as Math Educator”, which facilitates more personalized teaching of basic mathematical operations. This study presents the structure and operation of this application. We tested the usability of the software in several institutions. Our research target group consists of elementary school students, specifically those aged 11–15. This article provides a detailed overview of the accuracy and educational outcomes of the completed application. We evaluated the application and its effectiveness using both qualitative and quantitative methods. Our research design combined elements of educational technology development and effectiveness assessment. To evaluate student performance, we employed a control group methodology. Data were analyzed by comparing test results between students using the software and those receiving traditional instruction. We examined user satisfaction through survey questionnaires. Teachers’ opinions were gathered through structured interviews, and their responses were categorized using a SWOT analysis. The findings indicated that the use of the software significantly improved students’ mathematics performance compared to the control group. Students provided positive feedback on the software’s user interface, describing it as user-friendly and motivating. Teachers regarded the software as an effective educational tool, facilitating differentiated instruction and enhancing student engagement. The results suggest that digital educational tools, such as the developed software, can provide substantial added value in education.

Artificial Intelligence in Anatomy Teaching and Learning: A Literature Review

Abstract Medical anatomy is an essential preclinical course for medical undergraduates and provides a fundamental basis for various medical and surgical specializations. Frequently, students encounter difficulties when it comes to studying and comprehending the subject matter. Several pedagogical strategies have been devised and utilized throughout the years to enhance the process of teaching and learning in the field of anatomy. Artificial intelligence (AI) is now transforming anatomy education by utilizing modern technologies such as virtual reality, augmented reality, machine learning, and AI-powered evaluation tools. Recent research explored the AI in anatomy teaching, emphasizing its advantages and constraints. This review provides a thorough overview of the latest developments in the use of AI in anatomical education. It explores how AI-powered technologies can improve the educational experience for anatomy students, including personalized learning, automated grading, and intelligent tutoring systems, and examines the effects of these technologies on student engagement, learning outcomes, and teaching methods.

Implementation of an Artificial Intelligence Based Learning Management System for Adaptive Learning

In the world of modern education, the need for adaptive and personalized learning systems is becoming increasingly important to accommodate the unique learning needs of each individual. Implementing artificial intelligence (AI) in learning management systems offers great potential for optimizing adaptive learning experiences, where the system automatically adjusts learning content and approaches based on student profiles and responses. This research aims to develop and implement an artificial intelligence-based learning management system to personalize learning. The primary objective is to assess the effectiveness of the system in improving student engagement and learning outcomes and identify factors that influence its success. The method includes developing a system using machine learning algorithms for real-time analysis of student learning data. This research uses a mixed design that combines quantitative and qualitative data to analyze learning outcomes and understand user perceptions of the system. The research results show that this AI-based learning management system successfully increases student engagement and provides effective personalization of learning. Data shows improvements in grades and understanding of the material, especially in concepts students previously found difficult. Implementing an artificial intelligence-based learning management system has proven effective in supporting adaptive learning. This system improves learning outcomes and provides new insights into how AI technology can effectively integrate into educational contexts to support diverse and dynamic learning needs. The research also identified several challenges, including the need for more significant and varied training data and smoother integration with school curricula.

The Role of AI in Improving Student Learning Outcomes: Evidence in Vietnam

This study delves into the transformative role of AI in enhancing student learning outcomes in Vietnam, examining both the effectiveness of AI-driven educational technologies and their inclusivity across various learning environments. The research reveals that personalized learning systems and automated assessment tools substantially boost student engagement and learning efficiency. However, significant challenges remain, notably the digital divide and the substantial infrastructure and professional development investments to support widespread AI adoption. The implications of these findings underscore the necessity of strategic management in AI integration to ensure equitable access and effective utilization across all educational sectors. Contributing a nuanced perspective to the discourse on AI in education, this study underscores the potential of AI to revolutionize educational practices while highlighting the critical barriers that must be overcome. It offers concrete recommendations for policymakers, educators, and technology providers to optimize AI's benefits while addressing its challenges. This research provides a foundational analysis for future explorations into strategic AI integration, emphasizing the need to balance technological advancements with equitable educational practices in developing contexts like Vietnam.

Personalization of E-Learning: Future Trends, Opportunities, and Challenges

Electronic learning (e-learning) has become one of the most influential trends in providing enhanced and easily accessible learning to users. However, in contrast to traditional e-learning systems that offer uniform content to all learners, personalized learning systems tailor educational materials and assessments to individual learners, ensuring a customized learning experience. Personalizing e-learning through artificial intelligence (AI) is beneficial in today’s diverse environment, where beginners can tailor the learning experience to suit their preferences and requirements. This paper focuses on the personalization of e-learning, exploring future trends, opportunities, and challenges through an in-depth survey of recent literature on the personalization of e-learning technologies such as extended reality (XR), virtual reality (VR), augmented reality (AR), and mixed reality (MR). Findings identify and propose that such tools should be included in curricula that support e-learning through personalized, adaptive, and delivery models.

New model of college physical education teaching based on the algorithm and data structure of flipped classroom and OBE

Although college physical education (PE) is a compulsory course in college teaching, due to the openness of assessment standards and students studying to pass the final exam, college PE has been undervalued, so this paper aims to explore the new model of college PE teaching. In response, this paper took the air volleyball course as an example and redesigned the teaching of college PE based on the theory of flipped classroom and outcomes-based education (OBE). This paper also proposed a personalised learning system for college sports based on genetic algorithm (GA) and data structure, greatly improving college PE's autonomous learning ability and willingness. In the design of the teaching model, this paper compared and analysed the teaching model combining flipped classroom and OBE with flipped classroom teaching model, OBE teaching model and traditional teaching model. A one-semester investigation was conducted by selecting 40 students who took the air volleyball PE course at Hebei Normal University for Nationalities. The 40 students were divided into four groups, and their learning after one semester was compared. The experimental results showed that, compared with the traditional teaching model group, the outcome-based education and flipped classroom education group's performance of hitting the ball, passing the ball, spiking the ball, and serving the ball increased by 3.8 %, 14.3 %, 20.8 %, and 10.3 %, respectively. This suggested that the new college physical education teaching model based on flipped classrooms and OBE has a good teaching effect and can be used as a reference and help for others.

Ai-Driven Personalized Learning Systems: Enhancing Educational Effectiveness

Ai-Driven Personalized Learning Systems: Enhancing Educational Effectiveness

ERUDITE: Human-in-the-Loop IoT for an Adaptive Personalized Learning System

ERUDITE: Human-in-the-Loop IoT for an Adaptive Personalized Learning System

ChatGPT - A new milestone in the field of education

With the rapid development of artificial intelligence, natural language processing models like ChatGPT hold creative potential for applications in education. This study focuses on ChatGPT and explores its applications in the field of education. This research employs literature review and case analysis methods to systematically investigate the applications of ChatGPT in instructional assistance, personalized education, and writing support. By analyzing three cases involving ChatGPT's professional competence in medical exams, the construction of personalized learning systems, and writing support, the paper emphasizes ChatGPT's advantages in education, future development directions, as well as ethical and privacy concerns. The study finds that ChatGPT possesses strong semantic understanding and analytical capabilities. Within the context of general applicability, it can provide accurate answers to questions posed by users with certain specialized backgrounds. The paper not only addresses ChatGPT's role as a student's auxiliary tool but also explores collaborative models between ChatGPT and teachers. However, it's important to note that ChatGPT currently has limitations in terms of limited knowledge and inadequate emotional understanding. Additionally, in terms of ethics and privacy, considerations regarding information protection and moral aspects within the educational application of ChatGPT are crucial. In the future, it's worth further exploring specialized models for ChatGPT's tasks in both teacher and student roles, along with enhancing ChatGPT's emotional support capabilities.

The Establishment and Practice of a Blended Teaching Model for Piano Music Education

Abstract This study explores a blended learning model for piano music education, merging traditional classroom instruction with an online personalized learning system to boost resource efficiency, student engagement, and learning outcomes. Utilizing the item collaborative filtering (CF) and learning style filtering recommendation algorithms, we tailored teaching materials to individual student needs, significantly improving match accuracy between resources and learners. Results from implementing this optimized hybrid teaching approach showed a 15% increase in course ratings, a 20% rise in student participation, and a marked enhancement in learners’ interest in piano studies. Additionally, learner satisfaction soared by 25% due to the learning style-based algorithm’s ability to personalize resource allocation. This research underscores the effectiveness of combining blended teaching models with personalized learning systems in elevating piano education quality and efficacy.

PHILILOGICAL PERSPECTIVES OF DESIGN THINKING AND ARTIFICIAL INTELLIGENCE IN THE EDUCATION 5.0-7.0 ERA

In the era of modern education, the integration of design thinking and artificial intelligence has emerged as a transformative force, particularly in the context of the Education 5.0-7.0, which has its peculiarities. This paper searches through the intersection of these two concepts through a philological point of view, exploring their implications, synergies, and challenges. Design thinking, with its human-centered approach to problem-solving, emphasizes empathy, creativity, and iterative ideation. Artificial intelligence, on the other hand, offers unprecedented opportunities for personalized learning, data-driven decision-making, and adaptive systems. Together, they hold immense potential to revolutionize pedagogy, curriculum development, and educational technologies. The relevance of this research is determined by investigating the integration of philological perspectives, design thinking, and artificial intelligence (AI) in the evolving educational frameworks of Education 5.0 to 7.0. By combining human-centered design with advanced AI technologies, it aims to create personalized and adaptive learning experiences, enhancing student outcomes and engagement. The study provides valuable insights for policymakers and educators on developing inclusive and effective curricula, preparing students for future challenges and careers. Additionally, it contributes to the academic discourse on educational innovation, supporting global trends towards digital transformation and interdisciplinary approaches in education. This research introduces a novel interdisciplinary framework that integrates philological insights with design thinking and artificial intelligence (AI) within the context of Education 5.0 to 7.0. It pioneers the examination of linguistic and cultural dimensions in design thinking methodologies enhanced by AI, offering new perspectives on creating personalized, adaptive learning experiences. By bridging the gap between human-centered design and intelligent systems, the study proposes innovative approaches to curriculum development and pedagogy, setting a foundation for future educational innovations and contributing to the evolving academic discourse on educational technology and methodology. Drawing on insights from philology, the study of language in historical and literary contexts, this paper this article aims to explore how design thinking and artificial intelligence intersect with language acquisition, textual analysis, and cultural interpretation in education. It investigates the role of language in shaping learning environments, communication strategies, and knowledge dissemination processes in the digital age. Furthermore, it explores the ethical considerations inherent in the use of artificial intelligence in education, particularly regarding data privacy, algorithmic bias, and the preservation of linguistic diversity. It advocates for a nuanced understanding of the cultural and social dimensions of language within educational AI systems, promoting inclusivity, equity, and ethical literacy. The methodology involves a comprehensive literature review on cognitive differences, contextual elements, and recent innovations in education, academic papers, books, and articles on philology, design thinking, AI in education, and the evolution of Education 5.0 to 7.0. The analysis and practical implementation of the case studies from educational institutions implementing AI and design thinking methodologies. The ultimate goal is to contribute valuable insights to guide educational institutions in adapting to the 21st-century landscape and fostering inclusive and impactful effective learning environments. Thus, we use such methods of qualitative analysis as thematic analysis for dentifying common themes and insights from literature and case studies regarding the integration of philology, design thinking, and AI in education, content analysis for analyzing curriculum documents and educational artifacts to assess the presence and impact of design thinking and AI elements. Moreover, we use case studies of students who have successfully integrated AI and design thinking while learning foreign language, detailed examination of their implementation processes, challenges, and outcomes. Through a multidisciplinary approach that bridges education, design, artificial intelligence, and philology, this paper seeks to inform educators, researchers, and policymakers about the transformative potential of integrating design thinking and AI in education. By embracing philological perspectives, it aims to foster critical inquiry, interdisciplinary collaboration, and human-centered innovation in the Education 5.0-7.0 era.

Mathematical Modeling of Embodied Cognition and Personalized Learning in Interactive Virtual Reality: A Nonlinear Systems Approach

Abstract This study develops a mathematical model of embodied cognition integrated with personalized learning within an interactive Virtual Reality (VR) environment, employing nonlinear systems theory to optimize educational outcomes. The study mathematically formulates the interactions between learners’ cognitive states, bodily actions, and environmental inputs by applying state-space models and multi-modal data analysis. The framework incorporates real-time affective feedback and dynamic adjustment mechanisms, improving mental performance and reducing cognitive load. A randomized controlled trial validates the model, demonstrating significant enhancements in learning satisfaction and knowledge retention. This research provides a novel quantitative basis for integrating nonlinear mathematical methods into the design and optimization of personalized learning systems in VR.

Intelligence and Its Impact on Society

he present study aims to investigate the phenomenon under examination in a comprehensive and systematic manner. The This article discusses the utilization of artificial intelligence across many industries and its impacts on society, encompassing both advantageous and disadvantageous aspects. Artificial intelligence (AI) plays a significant role in the healthcare sector by aiding in the management of healthcare services, facilitating early diagnosis, enhancing documentation processes, and enabling robotic surgery. Artificial intelligence (AI) in the realm of education offers sophisticated instructional methodologies to students through the utilization of data mining techniques, intelligent teaching platforms, and personalized learning systems. The positive outcomes of artificial intelligence include enhanced productivity, amplified revenue generation, employment creation, and mitigation of human mistakes. Artificial intelligence (AI) engenders both advantageous and detrimental consequences. The adverse consequences of artificial intelligence encompass workforce downsizing, labor market contractions, concerns regarding privacy protection, and disparities. Hence, it is imperative that the development of artificial intelligence adheres to ethical principles and serves as an asset to society.

Learning activities of students with hearing disabilities in the digital environment: features, problems and styles

Due to the development of digitalization in education, it is important to study the potential of the digital environment in the training of students with hearing disabilities. The primary task is to study the features of learning activities of students with this pathology in interaction with the digital environment and the impact of active use of digital technologies on students’ cognitive sphere to optimize the conditions of digital learning and prevent its negative impact.The views on the properties of the human cognitive sphere developing in the conditions of deafness, including the peculiarities of working memory, have been analyzed. A brief review of research on the organization of e-learning for hearing impaired and deaf students of different countries during the pandemic period was carried out. Special attention is paid to the problems of transition to distance learning and the risks of cognitive performance in the digital environment for students with hearing loss.The results of the study of cognitive difficulties of students with hearing disabilities in the digital environment and their cognitive styles are presented. Based on the analysis of in-depth interviews data, motivational, cognitive, activity, organizational, and technical problems are formulated. Using the grounded theory method, three learning styles were identified: proactive, practical, and passive. Knowing the distinctive features of each style will allow creating of more personalized learning systems as part of the digitalization of inclusive education.

Horay's Personalized Learning System Of Instruction And Course Review On Students' Concept Understanding Ability And Activeness

This study aims to determine the Influence of The Personalized System Of Instruction Learning Model And Horay Course Review On The Ability To Understand Concepts And Activeness Of Class X Students Of Private High School Harapan Mekar Medan Learning Year 2021-2022. This research methodology uses quantitative research methods. The research population is all students of class X of Sma Swasta Harapan Mekar Medan with a total of 28 students in experimental class 1, namely class X-A and 27 students in experimental class 2, namely class X-B. The sampling method is non-probability sampling with a saturated sampling technique where the researcher will take all members of the population to be sampled. Data analysis was carried out with a t-test, with the following results: 1) There is an influence of PSI on concept understanding ability 2) There is an influence of PSI on student activity 3) There is an influence of CRH on students' concept understanding ability 4) There is an influence of CRH on student activity 5) There is no difference in the ability to understand concepts taught with PSI and CRH 6) There is no difference in activeness taught with PSI and CRH.

Enhancing Online Learning Experiences through Personalization Utilizing Recommendation Algorithms

This research investigates the implementation and impact of personalized learning systems underpinned by advanced recommendation algorithms in the realm of online education. The study encompasses a diverse group of participants from various educational backgrounds and explores their interactions with the personalized learning platform. The key findings of this research are noteworthy. Participants who had access to the personalized learning environment exhibited a substantial increase in engagement, satisfaction, and learning outcomes compared to those in the control group. This signifies the transformative potential of personalized learning in online education. The research emphasizes the critical role of personalization in enhancing learner engagement and satisfaction. It highlights how learners actively engaged with the system, making use of personalized recommendations to tailor their learning experiences. Moreover, the study sheds light on the positive impact of personalization on learning outcomes, indicating that learners achieved higher academic performance when their learning experiences were customized to their needs and preferences. In addition to its benefits for learners, the research underscores the advantages of personalized learning for instructors. The system provided instructors with valuable insights into each learner's progress and challenges, enabling more targeted and effective support. While the study demonstrates the effectiveness of personalized learning, it acknowledges certain limitations, including a relatively limited sample size and short duration. Future research endeavors could involve larger and more diverse samples and extend the study duration to gain a more comprehensive understanding of the long-term effects of personalized learning. In conclusion, this research contributes to the growing body of literature on personalized learning in online education. It provides compelling evidence that personalized learning, facilitated by sophisticated recommendation algorithms, can significantly enhance the online learning experience. The findings offer insights for educators and institutions looking to integrate personalized learning features into their online platforms to improve learner engagement, satisfaction, and learning outcomes.

Customized Education Artificial Intelligence's Role In Tailored E-Learning.

This research is driven by the immense potential of personalized e-learning systems to address the challenges of effective online education delivery. It focuses on proposing an efficient architectural framework for personalized e-learning systems, exploring various techniques and challenges and offering innovative solutions. The paper conducts a thorough review of current state-of-the-art methodologies in implementing personalized e-learning systems, along with discussions on the crucial requirements and challenges for successful deployment. Furthermore, it presents an efficient framework for building effective e-learning systems, while also discussing mechanisms, challenges and future research directions that the research community can consider. The subsequent sections of this paper provide a detailed exploration of the research, followed by a proposal for a personalized learning system, and insights into important issues for the community to address. The paper concludes by summarizing its findings and contributions.