Personal Learning Research Articles

IMPROVING PERSONAL HEALTH LEARNING OUTCOMES THROUGH A JIGSAW TYPE COOPERATIVE LEARNING MODEL FOR FIFTH GRADE STUDENTS AT TELAGA PANGE PUBLIC ELEMENTARY SCHOOL

Health is the most valuable asset, the expression is said when the person has fallen ill. Healthy at least a person is very dependent on the behavior of the person's daily life. Therefore, everyone needs to be given information about health so that they can lead a life in accordance with health principles. Health aims to improve people's standard of living optimally. In order to achieve this goal, the community is encouraged to know the problems and find ways to overcome the various problems that arise. So far, health development has focused more on efforts to cure diseases, so that the focus of government attention has been directed more towards the development of health facilities such as health centers and hospitals. Soekamto, et al in Titu, (2015) stated the purpose of the learning model is "a conceptual framework that describes a systematic procedure for organizing learning experiences to achieve certain learning goals, and serves as a guide for learning designers and teachers in planning learning activities." The jigsaw type cooperative learning model can improve personal health learning outcomes in fifth grade students of Talaga Pange Public Elementary School. Through the application of the jigsaw type cooperative method, it can improve personal health learning outcomes. -the average achievement of the results obtained by the subject or student.

Applying information mining technology in online entrepreneurship training course

The purpose of this study is to deeply explore the application of information mining technology in online entrepreneurship training courses, and to improve students’ learning effects and entrepreneurship success rate through systematic analysis and optimization of key data in the teaching process. With the development of online education, how to effectively use big data technology to meet personalized learning needs has become an important issue. This study takes several online entrepreneurship training courses as the research object, and uses information mining technology to extract and analyze students’ behavioral data during course participation, including data on study time, interaction frequency, assessment results, etc. Through machine learning algorithms and association rule mining, the research revealed the main factors that affect students’ learning effects and entrepreneurial success, and designed targeted teaching strategies, such as dynamically adjusting learning content, providing personalized feedback, optimizing learning paths, etc. Experimental results show that online courses using information mining technology significantly improve students’ knowledge mastery and entrepreneurial success rate, especially in terms of personalized learning experience and teaching efficiency. In addition, this study also explores the application prospects of information mining technology in future online education. It is believed that through the combination with artificial intelligence (AI) technology, the intelligence and adaptability of online courses can be further enhanced to meet more diversified learning needs.

Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education

This paper presents a novel framework, artificial intelligence-enabled intelligent assistant (AIIA), for personalized and adaptive learning in higher education. The AIIA system leverages advanced AI and natural language processing (NLP) techniques to create an interactive and engaging learning platform. This platform is engineered to reduce cognitive load on learners by providing easy access to information, facilitating knowledge assessment, and delivering personalized learning support tailored to individual needs and learning styles. The AIIA’s capabilities include understanding and responding to student inquiries, generating quizzes and flashcards, and offering personalized learning pathways. The research findings have the potential to significantly impact the design, implementation, and evaluation of AI-enabled virtual teaching assistants (VTAs) in higher education, informing the development of innovative educational tools that can enhance student learning outcomes, engagement, and satisfaction. The paper presents the methodology, system architecture, intelligent services, and integration with learning management systems (LMSs) while discussing the challenges, limitations, and future directions for the development of AI-enabled intelligent assistants in education.

Harnessing the Power of Generative Artificial Intelligence in Pathology Education.

Generative artificial intelligence (AI) technologies are rapidly transforming numerous fields, including pathology, and hold significant potential to revolutionize educational approaches. To explore the application of generative AI, particularly large language models and multimodal tools, for enhancing pathology education. We describe their potential to create personalized learning experiences, streamline content development, expand access to educational resources, and support both learners and educators throughout the training and practice continuum. We draw on insights from existing literature on AI in education and the collective expertise of the coauthors within this rapidly evolving field. Case studies highlight practical applications of large language models, demonstrating both the potential benefits and unique challenges associated with implementing these technologies in pathology education. Generative AI presents a powerful tool kit for enriching pathology education, offering opportunities for greater engagement, accessibility, and personalization. Careful consideration of ethical implications, potential risks, and appropriate mitigation strategies is essential for the responsible and effective integration of these technologies. Future success lies in fostering collaborative development between AI experts and medical educators, prioritizing ongoing human oversight and transparency to ensure that generative AI augments, rather than supplants, the vital role of educators in pathology training and practice.

Towards a QBLM-Based Qualification-Management Methodology Supporting Human-Resource Management and Development

This position paper presents a novel perspective on addressing the challenges of digital transformation in higher education through the development of a qualification-based learning model (QBLM) qualification management methodology. It argues that the rapid pace of technological advancement and the resulting need for continuous upskilling and reskilling necessitate a more dynamic and adaptive approach to human-resource management and development. The paper posits that by extending QBLM through the integration of artificial intelligence (AI) and machine learning (ML), a more effective system for analyzing competence requirements and designing personalized learning pathways can be created. The paper proposes a three-fold approach: (1) developing the FPHR ontology to support semantic annotation of HR qualifications in higher-education institutions (HEIs), (2) integrating this ontology into QBLM to ensure the machine-readability of qualifications, and (3) modeling a knowledge-based production process for HRs in skills-based learning. This paper outlines the current state of the art, presents conceptual models, and describes planned proof-of-concept implementations and evaluations. It contends that this approach will significantly enhance the effectiveness of human-resource development in the rapidly evolving digital knowledge society. By presenting this position, the paper aims to stimulate discussion and collaboration within the academic community on innovative approaches to qualification management in higher education. The work addresses critical issues arising from technological development and offers a forward-thinking solution to bridge the gap between current and future skill requirements in industry and academia.

Effects of Classroom Management Strategies on Students’ Behavior Regulation in Boarding Secondary Schools in Rwanda: A Case of Muhanga District

This study aims to investigate the effect of classroom management strategies on students’ behavior regulation in boarding secondary schools in Rwanda, a case of Muhanga district. The study was guided by three specific objectives which are to determine the influence of establishing clear classroom rules on students’ engagement and academic performance in boarding secondary schools in Muhanga district, to determine the effect of proactive planning in teaching on behavior regulation in boarding secondary schools in Muhanga district, Rwanda, to assess the effect of gamified personal learning plans in teaching on students’ behavior regulation in boarding secondary schools in Muhanga district. The study was guided and supported by social learning theory. Survey with mixed- approaches was employed in this study. The target population in this research was 5750 respondents including 5290 boarding secondary school students, 414 teachers, 23 directors of discipline and 23 boarding school head teachers. Directors of discipline and head teachers were selected through purposive sampling while simple random sampling was used to select teachers and students. The study used questionnaires for teachers and students and interview guide for directors of discipline and head teachers in data collection due to their availability and daily responsibilities. Data analysis employed SPSS, version 22.0 and data were summarized into means, percentage, standard deviation, correlation coefficients and coefficients of regression. The findings highlighted the significant impact of clear classroom rules on students’ behavior, with 73.8% of students affirming their positive influence and a high mean score of 4.232. While 54.4% reported consistent enforcement, some inconsistencies remain. Teachers overwhelmingly agreed on the importance of these rules in reducing disruptions, achieving a perfect mean score of 5.000, though only 26.9% feel they communicate rules consistently. Proactive planning shows mixed results, with low scores for technology use in lessons (2.293) but more support for structured environments (3.404). Additionally, gamified personal learning plans (GPLPs) are positively perceived by students and teachers, with 53.8% believing they enhance focus and strong support for rewards in maintaining discipline.

Analyzing demand for virtual reality based adaptive learning design in engineering faculty of universitas negeri Makassar Indonesia

This research aims to analyze the needs of students regarding the application of Virtual Reality (VR) technology in learning at the Faculty of Engineering. The main focus is to understand how VR can support adaptive learning and improve students' practical ability according to individual ability. This research uses a needs analysis method involving a survey of engineering students. Data collection was conducted through questionnaires and observations to identify students' constraints, needs, and interests in VR-based learning. The collected data were then analyzed quantitatively and qualitatively. The results show that 85% of students struggle to understand complex engineering concepts, and 90% show high interest in using VR for skills practice in a safe environment. In addition, students expected a more interactive and personalized learning approach. Important features they expected included automatic adjustment of difficulty levels and immediate feedback during practice. The findings of this study confirm the importance of integrating VR into engineering education, especially in creating an adaptive learning environment that meets individual needs. VR has the potential to improve students' understanding of concepts and practical skills, as well as to support the development of curriculum that are more relevant to the needs of modern industry.

Artificial Intelligence, Virtual Computer Systems, and Interactive Systems: a Holistic Approach to Enhancing Creative Talent in Art Design

With the rapid development and wide application of artificial intelligence, virtual computer systems and interactive systems, they play an important role in improving innovative talents in art and design. The purpose of this paper is to explore the overall approach of using these technologies to improve innovation in the field of art and design as well as to cultivate innovative talents. Firstly, we introduce the current development of artificial intelligence in art and design. Secondly, we explore the application of AI, virtual computer systems and interactive systems in art and design innovation. Artificial intelligence technology can help designers discover new inspirations and ideas by analysing a large number of artworks and design cases; virtual computer systems can provide designers with a virtual creative environment so that they can experiment and create in an infinite space; interactive systems can provide learners with a personalised learning experience, and, through intelligent educational platforms and interactive tools, help them develop their creative thinking and problem-solving skills. By making full use of these technologies, we can cultivate more creative and innovative art and design talents, and promote the sustainable development of the art and design field.

SDG 4, Academic Integrity and Artificial Intelligence: Clash or Win-Win Cooperation?

This article investigates the relationship between Sustainable Development Goal 4 (SDG 4), academic integrity as its part, and artificial intelligence (AI) through a bibliometric analysis, assessing whether this intersection represents a clash or win-win cooperation. SDG 4 aims to ensure equitable access to quality education, while AI technologies have the potential to enhance educational practices but demote academic integrity. By analyzing a comprehensive body of the literature, this study identifies key trends and thematic areas where AI is applied in educational settings, particularly concerning maintaining academic integrity. The findings reveal a growing body of research highlighting AI’s role in personalizing learning experiences, improving educational accessibility, and supporting educators’ teaching methodologies. However, challenges such as ethical considerations, data privacy, and the digital divide are also addressed, indicating potential conflicts that need to be navigated. Ultimately, this analysis suggests that while there are significant opportunities for synergy between AI and SDG 4, the management of careful implementation and policy frameworks is essential to ensure that AI serves as a tool for promoting inclusive and sustainable education rather than exacerbating existing inequalities. AI transforms science management by enhancing data analysis, streamlining research processes, and improving decision-making, ultimately leading to more efficient and effective scientific research and innovation. The findings reveal that while AI can facilitate personalized learning and enhance educational accessibility, it also poses challenges related to academic misconduct, such as plagiarism and the misuse of AI-generated content. This duality highlights the need for educational institutions to develop robust frameworks that leverage AI’s capabilities while safeguarding academic integrity. The article concludes that a collaborative approach, integrating AI into educational practices with a strong emphasis on ethical considerations and integrity, can lead to a synergistic relationship that supports the goals of SDG 4. Recommendations for future research and practical implications for managers, educators, scientists, and policymakers are also discussed, emphasizing the importance of fostering an educational environment that embraces innovation while upholding ethical standards.

AI's Role in EFL: Optimizing Opportunities while Mitigating Risks

As artificial intelligence (AI) technologies are increasingly being adopted in educational contexts, it is essential to understand how key stakeholders perceive opportunities and challenges for responsible integration. This mixed-methods study explored EFL teachers' perspectives on utilizing AI tools for instruction, learning activities, and assessment. An online survey collected both quantitative and qualitative data from 150 EFL teachers regarding their experiences, attitudes, and beliefs about AI's pedagogical role and ethical considerations. Descriptive statistics revealed generally positive views of AI's potential for personalized learning and practice opportunities, though ongoing concerns around risks to human interaction, privacy, and bias. Teachers favored collaborative models, with AI playing a supporting role under educator oversight. Significant gaps in teachers' technical literacy and lack of training or support for integration emerged as barriers requiring attention. While potential applications for standardized assessment were acknowledged, ongoing validation of AI models against human judgment was deemed necessary. Qualitative analysis identified uncertainty around AI's impacts on higher-order skill development and communicative competence. Guidelines delineating appropriate roles and oversight frameworks were seen as imperative to ensure aligned, ethical usage maximizing opportunities. This study provides timely empirical insights to inform the development of policies, tools, programs, and practices facilitating the judicious adoption of AI as a partner in strengthening EFL education worldwide through collaborative human-AI partnerships grounded in evidence-based research.

Innovation and Practice of Integrated Public Service System for Digitally Enabled Lifelong Learning for All People

Lifelong learning for all has gradually become a norm in developing education policies in various countries. Innovation and Practice of Integrated Public Service System for Digitally Enabled Lifelong Learning for All People attempts to analyze and address the issues above from the data continuous development and improvement perspective. These issues include Unequal distribution of resources, Rapid technological upgrading, Lack of appropriate platforms, and An urgent need for personalized learning, Data security and privacy. China has made some progress in promoting the construction of a digitally empowered learning society while facing some challenges and problems. The solutions include:●Strengthen the overall planning of the platform.●Integration of crucial application scenarios.●Implementing data governance to build a lifelong learning data warehouse.●Using platforms to strengthen the application of learning outcomes.●Construction of platform network security guarantee system.

AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities

Generative AI has drawn significant attention from stakeholders in higher education. As it introduces new opportunities for personalized learning and tutoring support, it simultaneously poses challenges to academic integrity and leads to ethical issues. Consequently, governing responsible AI usage within higher education institutions (HEIs) becomes increasingly important. Leading universities have already published guidelines on Generative AI, with most attempting to embrace this technology responsibly. This study provides a new perspective by focusing on strategies for responsible AI governance as demonstrated in these guidelines. Through a case study of 14 prestigious universities in the United States, we identified the multi-unit governance of AI, the role-specific governance of AI, and the academic characteristics of AI governance from their AI guidelines. The strengths and potential limitations of these strategies and characteristics are discussed. The findings offer practical implications for guiding responsible AI usage in HEIs and beyond.

Differentiation of Educational Content Through Artificial Intelligence Systems in Inclusive Education

The integration of artificial intelligence (AI) systems into education offers transformative potential for personalized learning, particularly within inclusive education frameworks. This research examines the utilization of artificial intelligence (AI) for the differentiation of educational content to cater to the diverse needs of students. By examining current AI applications and their associated benefits and challenges, this study proposes a framework for the ethical and effective use of AI in inclusive educational settings.

Generative AI as a New Paradigm for Personalized Tutoring in Modern Education

Conventional education often falls short when delivering personalized and immediate support to all students, resulting in disparities in understanding and unequal learning opportunities. Generative Artificial Intelligence (GenAI) provides a scalable and cost-effective solution for on-demand tutoring, offering personalized support 24/7. This paper delves into the potential application of GenAI as an on-demand tutoring system, addressing the critical need for immediate and personalized educational support. Implementing GenAI to establish an on-demand tutoring system that delivers personalized, real-time assistance is vital in meeting the demands of today’s academic landscape. Critical components of this approach encompass advanced natural language processing techniques to comprehend and respond to student inquiries, machine learning algorithms to tailor support to individual learning styles, and a scalable cloud-based infrastructure to ensure round-the-clock availability. This approach’s expected scientific surplus value lies in its capacity to significantly enhance educational outcomes by providing scalable, personalized learning experiences. This paper elucidates a pathway for future research and development in this area, highlighting the potential of GenAI to revolutionize education and improve learning outcomes for all students.

Research on Challenges and Strategies of Students' Adaptive Learning within AI

It's acknowledged that an adaptive e-learning system can be efficient for users to conduct personalized learning in which learners could choose appropriate content and preferred models based on artificial intelligence (AI) recommendations. Nowadays, the rapid development of adaptive platforms, fueled by the evolution of machine learning and AI, incontestably brings innovative potential to the educational field. However, there are many unavoidable challenges followed by the swift process of integrating AI and other technologies into adaptive learning practices. Through the extensive review of previous research on adaptive e-learning and comprehensive analysis of their prominent outcomes, the paper focuses on exploring the challenges of adaptive learning within AI that students are facing and proposing possible solutions. It's found that adaptive learning is mainly experiencing the challenges involving how to avoid data bias and protect personal privacy, making continuous evaluations of users, lack of emotion monitoring and technological support for both students and teachers and the technology integration and inequity. To deal with the increasing problems, some practical strategies are presented, such as properly governing data for learning management, conducting continuous evaluation and prediction for users, and visualizing cognition, which can be supportive of students' professional development in adaptive learning.

Integration of AI Technology and ZPD: Pinpointing the Zone of Student Development to Promote Educational Innovation and Efficiency Improvement

In the era of rapid technological advancement, AI has profoundly penetrated various sectors of society, including education. This paper explores the positive impact of AI on students' Zone of Proximal Development (ZPD), aiming to assist teachers in making more accurate judgments regarding multiple students' ZPDs and thereby enhancing educational teaching. The findings reveal that AI technology, through its application in ZPD, can provide personalized learning resources and assistance to students, as well as offer teachers appropriate teaching content and strategies tailored to students' needs. However, in the actual teaching process, the application of ZPD to teaching still faces difficulties related to students' individualization, sensitive emotions, and trust crises. Based on this, this paper puts forward the following suggestions. Teachers need to be actively trained in AI to overcome potential resistance from older educators. At the same time, teachers should focus on identifying further technological advances in ZPD and related areas to maximize the benefits of AI in education.

Shaping the Future of Learning, Teaching, and School Leadership Management

This paper is about transformative trends in education, exploring learning, teaching methodologies, and management of school leadership, which will redefine education in the next ten years and beyond. The concept of personalized learning, the integration of technology in study activities, and the reform of leadership from the traditional, routine-oriented style into the more open, adaptive one are the critical issues of the research currently being conducted. These changes referring to personal learning differences, lifelong learning, and collaborative environment are meant to make them more evolved or advanced. Through the analysis, teachers being purely instructors changed to facilitators of learning, cultural responsiveness to teaching was emphasized, and the impossibility of attaining good outcomes without digitalization was brought into view. On the other hand, transformational leadership is especially relevant for change management with respect to community participation and data-based decision-making. This view of all angles on the future shows how the education systems that tend to be more flexible and diverse, as well as the ones that are better suited to meet all the diverse learning needs of all students, are the ones to thrive in this new global paradigm.

How does generative AI promote autonomy and inclusivity in language teaching?

Abstract In recent years, artificial intelligence (AI) has increasingly made advances across various fields, including ELT. In this article we embrace an egalitarian dialogue framework to explore and understand the role of AI in language education. This format provides a useful backdrop for navigating the dual responses—enthusiasm and scepticism—by which AI’s influence in education has often been characterized. We critically assess both the advantages and limitations of AI in ELT, highlighting and praising how AI-powered tools can assist educators in differentiating instruction, promoting learner autonomy, and creating personalized and inclusive learning environments, thus making education more accessible to students with diverse and individual needs. However, the article also addresses the challenges posed by the digital divide, particularly the inequities in access to (AI) technologies in low-socioeconomic-status regions. We also reflect upon how AI can become an obstacle in developing learner autonomy due to the risks associated with overdependence on such technologies and the lack of critical skills. The article concludes that although AI presents exciting opportunities for advancing and facilitating language learning, its integration must be approached with a rational mindset to ensure that it serves to bridge educational gaps rather than intensify them.

A cluster approach to matching the competences of data specialists with skills in demand on the labour market

This paper addresses the challenge of aligning the competences of data specialists with skills in demand on the labour market in the rapidly evolving field of data science. Using an open dataset of 3,744 IT job postings, the study applies K-means clustering to identify key skill groupings for data specialist positions. The optimal number of clusters is determined using the elbow method, resulting in four distinct clusters: Data Analyst & Engineer, Data Platform Engineer, Data Science & Engineering Specialist, and Cloud Data Engineer. The research methodology employs unsupervised learning techniques, specifically K-means clustering, to analyze the distribution of skills across job postings. The clusters are visualized using t-distributed Stochastic Neighbor Embedding (t-SNE), providing insights into the relationships between different skill sets. The study reveals that job titles do not always unambiguously define the required skills, emphasizing the importance of focusing on specific skill sets rather than job titles alone. To bridge the gap between specific subject competences academic programs and industry requirements, the paper proposes a novel approach for comparing the proportion of skills in job clusters with the proportion of professional competencies in academic programs. This method is demonstrated using the Information Systems and Technologies Master's program at Kherson State University as a case study. The chi-square test is applied to confirm the statistical similarity between the skill structure of the Data Science & Engineering Specialist cluster and the competency structure of the academic program. The findings highlight the importance of continuous adaptation of profile of academic program to meet evolving industry needs. The proposed approach provides a data-driven framework for universities to align their programs with labor market demands, potentially improving graduate employability in the data science field. The study also underscores the need for personalized learning paths that can be tailored to individual career goals and skill gaps. Future research directions include the development of an artificial intelligence system to form individualized educational trajectories based on the skills required for specific job clusters. This could further enhance the alignment between education and industry needs, preparing students more effectively for the dynamic data science job market.

Unlocking potential: understanding mobile device use patterns among year 9–11 students for learning

ABSTRACT The study examines the nature of smartphones for learning use patterns among students in grades 9–11 and its contexts in relation to personalised learning extracting risk factors associated with less mobile device use in learning. A latent class analysis (LCA) was employed. Various covariates were used to highlight groups of students at risk for effective smartphone-supported instruction. Participants were recruited for a cross-sectional online survey within the Monitoring of digital transformation series conducted in Russia in 2020/21. We identified four distinct profile groups of mobile device use among school students. Examining profile groups through the lenses of the Personalisation–Pluralisation Quadrant (PPQ) developed by Kucirkova and Littleton and the Substitution, Augmentation, Modification and Redefinition (SAMR) framework of Puentedura, we show that heterogeneous guidance should be provided to help students maximise the potential of smartphones for learning. This study represents a first attempt to examine the heterogeneous structure of mobile use among school students, using a latent class analysis and can be used as a reference to better understand the use of personal mobile devices among school students for educational purposes and personalised learning. The findings also result in targeted approaches to the teachers’ instruction according to the constructivism principles.